CN102700567B - For the method for the fuel efficiency of the optimization of maneuvering system, quantity discharged and mission performance - Google Patents

Abstract

For operating, there is the method (800) that at least one diesel oil is the diesel oil maneuvering system of the power generation unit of fuel, described method comprises the performance characteristic (802) of assessment diesel oil maneuvering system, relatively this performance characteristic and the designated value (804) meeting task object, and adopt the closed loop control system run according to feedback principle to regulate performance characteristic to correspond to designated value to meet task object (806).

Description

Method for optimized fuel efficiency, emissions and mission performance of a motorized system

technical field

The present invention relates to powered systems, such as trains, off-road vehicles, marine vessels, transport vehicles, agricultural vehicles, and/or stationary powered systems, etc., and more particularly to optimized fuel efficiency and emissions for diesel powered systems Quantity (emission output) method.

Background technique

Some mobile systems (such as, but not limited to, off-road vehicles, marine vessels, stationary power generation plants, transport vehicles such as transport buses, agricultural vehicles and trains, and other rail vehicle systems) are composed of one or more Diesel units (eg, engines) and/or electrical units (eg, alternators and/or battery systems) provide power. With regard to trains and other rail vehicle systems, a train typically includes one or more locomotives and a plurality of rail cars, such as freight cars. Each locomotive is powered by one or more diesel internal combustion engines. In some configurations, each engine drives an alternator or generator for generating electricity that is used to drive one or more DC or AC traction motors operatively connected to a locomotive axle for locomotive/traction purposes. In any case, a locomotive is a complex system with many subsystems, where each subsystem is interdependent with other subsystems.

The operator is usually on the locomotive to ensure the correct operation of the locomotive, and when there is a locomotive consist, the operator is usually on the lead locomotive. A locomotive "consist" is a group of locomotives that are controlled together in an operating train. In addition to ensuring the correct operation of the locomotive or locomotive consist, the operator is also responsible for determining the speed at which the train is running and the forces within the train. In order to perform this function, the operator must generally have extensive experience operating locomotives and various trains over the assigned terrain. This knowledge is required to comply with prescriptive operating parameters such as speed, emissions, etc. which may vary with the train's position along the track. In addition, the operator is also responsible for ensuring that the in-trainforce remains within acceptable limits.

In marine applications, the operator is usually on the marine vessel to ensure the correct operation of the vessel, and when there is a formation of vessels, the operator is usually on the lead vessel. As with the locomotive example cited above, a vessel consist is a group of vessels operating together in performing a combined mission. In addition to ensuring the correct operation of the vessel or vessel consist, the operator is also responsible for determining the operating speed of the consist and the forces within the consist, eg, between linked vessels. In order to perform this function, the operator must generally have extensive experience operating the vessel and the various formations in the assigned waterway. This knowledge is required to comply with prescriptive operating speeds and other mission parameters that may vary with the vessel's position along the mission. Additionally, the operator is responsible for ensuring that task force and position remain within acceptable limits.

In the case of multiple diesel powered systems (which may be located, for example, on a single vessel, other vehicles, power generating equipment, or a collection or group of power generating equipment), an operator typically directs the entire system to ensure proper operation of the system or group of systems. As generally defined, a "systems composition" is a group of motorized systems operating together in the performance of a mission. In addition to ensuring the correct operation of an individual system or group of systems, the operator is also responsible for determining the operating parameters of the collection of systems and the forces within the collection. In order to perform this function, the operator must generally have extensive experience operating the system and various assemblies on the assigned space and task. This knowledge is required to comply with prescriptive operating parameters and speeds that may vary with the location of the system assembly along a route, task, etc. In addition, the operator is also responsible for ensuring that the forces within the set remain within acceptable limits.

However, with respect to locomotives, even with the knowledge to ensure safe operation, operators generally cannot operate locomotives such that fuel consumption and emissions are minimized for each trip. For example, other factors that must be considered may include emissions, operator's environmental conditions like noise/vibration, a weighted combination of fuel consumption and emissions, and the like. This is difficult to do because (as an example) trains change in size and load, locomotives and their fuel/emissions characteristics differ, and weather and traffic conditions change.

When building a train, it is common practice to provide a series of locomotives in the train formation to power the train, based in part on available locomotives with varying power and travel history, based on the specific train mission. This typically causes large variations in the locomotive power available to individual trains. Additionally, for critical trains, such as Z-trains, backup power (typically backup locomotives) is typically provided to prepare for the event of equipment failure and to ensure that the train arrives at its destination on time.

Furthermore, when building a train, locomotive emissions are typically determined by establishing a weighted average based on the total emissions of the locomotives in the train when the locomotive is idle. These averages are expected to be below certain emissions when the train is idle. Typically, however, no further determinations are made regarding actual emissions when the train is idle. Thus, although established calculations may indicate acceptable emissions, in reality locomotives may emit more emissions than calculated.

When operating a train, train operators typically require the same notch setting as when operating the train, which in turn can result in fuel consumption and/or emissions (such as, but not limited to, _NOx , _CO2 , etc.) , depending on the number of locomotives powering the train. Thus, operators typically cannot operate locomotives to minimize fuel consumption and emissions (for each trip) because train sizes and loads vary, and locomotives and their power availability may vary according to model type.

A train owner typically owns multiple trains, where the trains run on the railroad track network. Due to the integration of simultaneous travel of multiple trains within a railway track network, where scheduling issues regarding train operations must also be considered, train owners will optimize fuel efficiency and emissions to save overall fuel consumption while minimizing the number of trains benefit from methods that reduce emissions and meet mission travel time constraints.

Likewise, owners and/or operators of off-road vehicles, transport vehicles, agricultural vehicles, marine vessels, and/or stationary motorized systems will recognize that when these diesel powered systems exhibit optimized fuel efficiency, emissions, fleet efficiency ) and mission parameter performance to save on overall fuel consumption while minimizing emissions and meeting operational constraints such as, but not limited to, mission time constraints.

Contents of the invention

Embodiments of the present invention disclose systems, methods, and computer software code for operating a motorized system (eg, rail vehicle, marine vessel, off-road vehicle, etc.) having at least one primary power generating unit. Methods are provided for evaluating the operating characteristics of a primary power generating unit. The operating characteristic is compared to a specified value with respect to the mission objective. The operating characteristics may be adjusted to meet the mission objective.

Computer software code operable in a processor and stored on a computer readable medium having a computer software module for evaluating operating characteristics of a primary power generating unit is disclosed. A computer software module is provided for comparing the operating characteristics of the main power generating unit to specified values with respect to mission objectives. Also disclosed are computer software modules for autonomously adjusting operating characteristics to meet the mission objectives.

In another exemplary embodiment, a method includes providing an optimized mission plan to a mobile system, which plan can be applied manually. The optimized mission plan is re-planned in response to the executing human mission plan. The human mission plan is adjusted when the human mission plan deviates from the optimized plan by more than a predetermined amount.

In another exemplary embodiment, a method includes executing a mission with an optimized mission plan. The mission is executed autonomously with an optimized mission plan. The input device is configured and provided to allow manual fine-tuning (adjustment) of at least one characteristic of the task within a predetermined range while the task is in progress.

In another exemplary embodiment, a method includes performing a task based on a manually performed task plan. The manually executed mission plan is fine-tuned while in progress using the information contained in the optimized mission plan.

In another exemplary embodiment, a method includes providing an optimized mission plan. At least a first characteristic associated with the mission plan is manually controlled. At least a second characteristic associated with the mission plan is autonomously controlled. The optimized mission plan adjusts autonomously according to the characteristics of human control.

Description of drawings

A more particular illustration of the invention briefly described above will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings. With the understanding that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope, exemplary embodiments of the invention will be described and illustrated with additional features and details through the use of the accompanying drawings, in which:

FIG. 1 is a flowchart depicting a method of trip optimization according to an embodiment of the invention;

Figure 2 depicts a simplified mathematical model of a train that may be used in connection with embodiments of the present invention;

Fig. 3 is the schematic diagram of track system;

FIG. 4 depicts an exemplary embodiment of a fuel usage/travel time profile;

FIG. 5 depicts an exemplary embodiment of segmentation decomposition for trip planning;

FIG. 6 depicts another exemplary embodiment of segmentation decomposition for trip planning;

Figure 7 is a schematic diagram and flowchart depicting another exemplary embodiment of a method and system for trip optimization;

Figure 8 depicts an exemplary embodiment of a dynamic display for use by an operator;

Figure 9 depicts another exemplary embodiment of a dynamic display for use by an operator;

Figure 10 depicts another exemplary embodiment of a dynamic display for use by an operator;

Figure 11 depicts an exemplary embodiment of a railway track network with multiple trains;

12 is a flowchart of a method for improving fuel efficiency of a train through an optimized train power composition according to another embodiment of the present invention;

13 depicts a block diagram of exemplary elements included in a system for optimized train power makeup;

14 depicts a block diagram of a transfer function for determining fuel efficiency and emissions of a diesel powered system;

15 is a flowchart depicting an exemplary embodiment of a method for determining a configuration of a diesel powered system having at least one diesel fueled power generating unit;

Figure 16 depicts an exemplary embodiment of a closed loop system for operating a rail vehicle;

Figure 17 depicts the closed loop system of Figure 16 combined with a master control unit;

Figure 18 depicts an exemplary embodiment of a closed loop system for operating a rail vehicle in conjunction with another input operating subsystem of the rail vehicle;

Figure 19 depicts another exemplary embodiment of a closed-loop system with a converter that can command a master controller to operate;

Figure 20 depicts another exemplary embodiment of a closed loop system;

Figure 21 is a flowchart illustrating an exemplary embodiment of a trip optimization method when operator input may be included in a decision loop;

Figure 22 is a flowchart illustrating an exemplary embodiment of a trip optimization method wherein an operator interface is available to the operator to fine-tune the optimized mission plan;

Figure 23 is a flowchart illustrating an exemplary embodiment of a trip optimization method, wherein the optimizer may modify the operator's mission plan;

FIG. 24 is a flowchart illustrating an exemplary embodiment of a method of trip optimization in which a portion of the tasks are divided between at least a trip optimizer and another entity;

Figure 25 is a flowchart illustrating an exemplary embodiment of a method for operating a powered system;

26 is a flowchart illustrating an exemplary embodiment of a method for operating a rail vehicle in a closed loop process;

Figure 27 depicts an example of a speed versus time graph comparing current operation to emissions optimized operation;

Figure 28 depicts a modulation pattern compared to a given notch level;

29 is a flowchart illustrating an exemplary embodiment of a method for determining a configuration of a diesel powered system;

Figure 30 depicts a system for minimizing emissions;

Figure 31 depicts a system for minimizing emissions from a diesel powered system;

32 depicts a method for operating a diesel powered system having at least one diesel fueled power generating unit;

33 depicts a block diagram of an exemplary system for operating a diesel powered system having at least one diesel fueled power generating unit; and

34 is a flowchart illustrating an exemplary embodiment of a method for increasing fuel efficiency of a powered system through an optimized powertrain.

Detailed ways

Reference will now be made in detail to embodiments consistent with the present invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or like parts.

Although the exemplary embodiments of the present invention are described with respect to rail vehicles or rail transportation systems, particularly trains and locomotives with diesel engines, the exemplary embodiments of the present invention are also applicable to other applications such as but not limited to off-road vehicles, marine vessels, Stationary units, as well as agricultural vehicles, transport buses, each of which may use at least one diesel engine or diesel internal combustion engine. For this reason, when discussing designated missions, this includes missions or requirements to be performed by diesel powered systems. Thus, with respect to rail, marine, transport vehicle, agricultural vehicle or off-road vehicle applications, this may refer to the movement of the system from its current location to its destination. In the case of a stationary application, such as but not limited to a stationary power generation station or network of power generation stations, the specified mission may refer to wattage (eg, MW/hr) or other parameters or requirements to be met by the diesel powered system. Likewise, the operating conditions of the diesel fueled power generating unit may include one or more of speed, load, fueling value, timing, and the like. Furthermore, while diesel powered systems are disclosed, those skilled in the art will readily recognize that embodiments of the present invention may also be used with non-diesel powered systems such as, but not limited to, natural gas powered systems, biodiesel powered systems, electric powered systems, and the like. Furthermore, as disclosed herein, such non-diesel powered systems as well as diesel powered systems may include multiple engines, other power sources, and/or additional power sources or energy storage devices such as, but not limited to, battery sources, voltage sources (such as but not limited to capacitors), chemical sources, pressure-based sources (such as but not limited to springs and/or hydraulic expansion), current sources (such as but not limited to inductors), inertial sources (such as but not limited to flywheel devices), Gravity-based power source and/or heat-based power source.

In one illustrative example involving marine vessels, multiple tugboats may operate together, all moving the same larger vessel, with each tugboat linked in time to complete the task of moving the larger vessel. In another illustrative example, a single marine vessel may have multiple engines. Off-road vehicles (OHVs) may involve a fleet of vehicles with the same mission of moving over the ground from location "A" to location "B," with each ORV linked in time to complete the mission. With respect to fixed power generating stations, multiple stations may be grouped together to collectively generate power for a particular location and/or purpose. In another exemplary embodiment, a single station is provided, but with a plurality of power generators making up the single station. In one illustrative example involving a rolling stock, multiple diesel powered systems may operate together, all moving the same larger load, with each system linked in time to accomplish the task of moving the larger load. In another exemplary embodiment, a rolling stock may have multiple diesel powered systems.

Exemplary embodiments of the present invention address problems in the art by providing systems, methods, and computer-implemented methods (eg, computer software code, etc.) for improving overall fuel efficiency and emissions through optimized powertrain. With respect to locomotives, the exemplary embodiment of the present invention is also operable when the locomotive consist operates with distributed power.

Those skilled in the art will recognize that equipment such as data processing systems, including CPUs, memory, I/O, program storage devices, connecting buses, and other appropriate components, can be programmed or otherwise designed to facilitate the practice of the methods of the present invention . Such a system will include appropriate program means for carrying out the method of the invention.

Also, an article of manufacture (e.g., a pre-recorded disk or other similar computer program product, etc.) for use with a data processing system may include instructions recorded thereon for instructing the data processing system to facilitate the practice of the methods of the present invention. Storage medium and program device. Such devices and articles of manufacture also fall within the spirit and scope of the invention.

Broadly speaking, the technical effect is to operate a diesel powered system (having at least one diesel fueled power generating unit) or other powered system by adjusting selected operating characteristics of the powered system in order to meet mission objectives of the diesel powered system. In order to facilitate the understanding of the exemplary embodiments of the present invention, the description below refers to specific implementations thereof. Exemplary embodiments of this invention may be described in the general context of computer-executable instructions, such as program modules, etc., executed by any device, such as but not limited to a computer, designed to receive data, perform prescribed mathematics, and/or OR Logical operations, where the results of such operations may or may not be displayed. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. For example, the software programs underlying the exemplary embodiments of the present invention may be coded in different programming languages for use with different devices or platforms. In the ensuing description, examples of the invention may be described in the context of a web portal employing a web browser. It will be appreciated, however, that the principles underlying the exemplary embodiments of the present invention may also be implemented with other types of computer software technology.

Additionally, those skilled in the art will recognize that the exemplary embodiments of the present invention may be practiced with other computer system configurations, including handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers , large computers, etc. Exemplary embodiments of the invention may be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. These local and remote computing environments may be contained entirely within the locomotive, or within adjacent locomotives in the consist, or off-board in a wayside or central station where wireless communications are used.

The term locomotive "consists" is used throughout this document. A locomotive consist may be described as having one or more locomotives connected together in series to provide starting and/or braking capabilities. The locomotives are joined together with no train cars between the locomotives. The train may have multiple locomotive consists in its composition. Specifically, there may be a lead consist and one or more remote consists, eg in the middle of a train and another remote consist at the end of the train, etc. Each locomotive consist may have a first locomotive and a trailing locomotive. While the first locomotive is generally shown as the lead locomotive, those skilled in the art will readily recognize that the first locomotive in a multiple locomotive consist may be physically located in a physically aft position. And, although the locomotive consist is generally shown as a series of locomotives, those skilled in the art will readily recognize that even when one or more railcars separate the locomotives, such as when the locomotive consist is configured for distributed power operation, the locomotive consist A locomotive may also be a consist where gas and brake commands are relayed from the lead locomotive to the remote trains via a radio link or physical cables. For this reason, the term "locomotive consist" should not be construed as a limiting factor when discussing multiple locomotives within the same train.

The marshalling may also be applicable to other diesel powered systems including marine vessels, off-road vehicles, and/or stationary power generating equipment operating together to provide locomotion, power generation and/or braking capabilities. Therefore, even though "locomotive consist" is used herein, the term can also be applied to other diesel powered systems. Similarly, subgroups can exist. For example, a diesel powered system may have multiple diesel fueled power generating units. For example, a power generation facility may have multiple diesel electric units, where optimization may be at the sub-constellation level. Likewise, a locomotive may have multiple diesel powered units.

Referring now to the drawings, embodiments of the present invention will be described. Exemplary embodiments of the invention can be implemented in numerous ways, including as a system (including a computer processing system), a method (including a computerized method), an apparatus, a computer readable medium, a computer program product, a graphical user interface, including a web portal or a data structure physically fixed in a computer readable memory. Several embodiments of the invention are discussed below.

Figure 1 depicts a flowchart of an exemplary embodiment of a method for trip optimization. 3 and 7 illustrate various elements of a motorized system (eg, a train) including a trip optimizer system configured to perform the method shown in FIG. 1 . As illustrated, an instruction is an input specific to a planned trip at or from a remote location (eg, dispatch center 10, etc.). Such input information includes, but is not limited to, train location, consist description (e.g., locomotive model, etc.), locomotive power description, performance of locomotive traction transmission, engine fuel consumption (as a function of output power), cooling characteristics, planned travel route (as effective track slope and curvature as a function of milestones or "effective slope" components to reflect curvature following standard railroad practice), trains and loads represented by the composition of cars together with effective drag coefficients, parameters of travel expectations, which include but are not limited to departure Time and location, end location, expected travel time, crew (user and/or operator) identity, crew shift due time, and route.

This data can be provided to the locomotive 42 (see FIGS. 3 and 7 ) in a number of ways, such as, but not limited to, an operator manually entering this data into the locomotive 42 via an on-board display, inserting, for example, a hard card and/or a USB flash drive containing the data, etc. The memory device goes into a socket on the locomotive and transmits this information to the locomotive 42 by wireless communication from a central or wayside location 41 such as a track signaling device and/or wayside device. Locomotive 42 and train 31 load characteristics (e.g., drag) may also vary along the route (e.g., with altitude, ambient temperature, and track and track-car conditions), and planning may be by any of the methods discussed above and/or by locomotive The real-time autonomous collection of train conditions is updated as needed to reflect such changes. This includes changes in locomotive or train characteristics detected, for example, by monitoring equipment on or off the locomotive 42 .

The track signaling system determines the allowable speed of the train. There are many types of track signaling systems and operating rules associated with each of the signals. For example, some signals have a single light (on/off), some signals have a single lens with multiple colors, and some signals have multiple lights and colors. These signals may indicate that the track is clear and that the train may travel at the maximum allowable speed. They can also indicate a request to slow down or stop. This deceleration may have to be done immediately, or at a certain location (eg, before the next signal or intersection).

The signal status is communicated to the train and/or operator by various means. Some systems have an electrical circuit in the track and an inductive pickup coil on the locomotive. Other systems have wireless communication systems. The signaling system may also require the operator to visually inspect the signal and take appropriate action.

The signaling system can interface with the on-board signaling system and regulate the speed of the locomotive according to the input and the appropriate operating rules. For signaling systems that require the operator to visually check the status of the signal, the operator screen will present the appropriate signal options for operator input based on the train's location. The type and operating rules of the signaling system as a function of location may be stored in the on-board database 63 .

Based on the specification data input into the trip optimizer system, an optimal plan with desired departure and finish times, minimizing fuel usage and/or resulting emissions along the route subject to speed limits is calculated to generate a trip profile 12 . The profile contains the optimal speed and power (notch) settings that the train will follow, expressed as a function of distance and/or time, and such train operating limits include, but are not limited to, maximum notch power and brake settings, and as Speed limits as a function of location as well as expected fuel use and resulting emissions. In the exemplary embodiment, the value of the notch setting is selected to obtain a throttle change decision approximately every 10 to 30 seconds. Those skilled in the art will readily recognize that throttle change decisions may occur for longer or shorter durations if this is required and/or desired in order to follow the optimal speed profile. In a broader sense, it will be apparent to those skilled in the art that a profile provides a train with power settings at the train level, consist level, and/or individual train level. Power includes braking power, cranking power and air braking power. In another embodiment, instead of operating at traditional discrete notch power settings, a continuous power setting determined to be optimal for the selected profile may be selected. Thus, for example, if instead of operating at a notch setting of 7 (assuming discrete notch settings such as 6, 7, 8, etc.), the optimal profile specifies a notch setting of 6.8, locomotive 42 may operate at 6.8. Allowing such intermediate power settings may result in additional efficiency benefits as described below.

The procedure used to calculate the optimal profile may be any number of methods for calculating the power sequence driving the train 31 to minimize fuel and/or emissions subject to locomotive operation and schedule constraints, as summarized below. In some cases, due to similarities in train configurations, routes and environmental conditions, the required optimal profile may be sufficiently close to a previously determined one. In these cases it may be sufficient to look up the driving trajectory in the database 63 and try to follow it. When no previously calculated plan is suitable, methods for calculating a new plan include, but are not limited to, direct calculation of the best profile using a differential equation model that approximates the physics of train motion. This setup involves the choice of a quantitative objective function, typically a weighted sum (integral) of model variables corresponding to the rates of emissions production and fuel consumption plus a term to penalize excessive throttle changes.

The optimal control formula is set to minimize a quantitative objective function subject to constraints including, but not limited to, speed limits and minimum and maximum power (throttle) settings and maximum cumulative and instantaneous emissions. Depending on the planning goals at any time, the problem can be flexibly implemented to minimize fuel subject to emissions and speed limits, or to minimize emissions subject to fuel usage and arrival time constraints. It is also possible to establish goals such as minimizing total travel time without total emissions or fuel use constraints where such loosening of constraints would be permitted or required for the mission.

Exemplary equations and objective functions are provided throughout this document for minimizing locomotive fuel consumption. These equations and functions are for illustration only while other equations and objective functions may be employed to optimize fuel consumption or to optimize other locomotive/train operating parameters.

Mathematically, the problem to be solved can be stated more precisely. The basic physics are expressed by:

$\frac{\mathrm{<span\; class="notranslate">\; dx</span>}}{\mathrm{<span\; class="notranslate">\; dt</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; ;</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0.0</span>}<span\; class="notranslate">\; ;</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; D.</span>}$

$\frac{\mathrm{<span\; class="notranslate">\; dv</span>}}{\mathrm{<span\; class="notranslate">\; dt</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0.0</span>}<span\; class="notranslate">\; ;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0.0</span>}$

Where x is the position of the train, v is its speed and t is time (in miles, miles per hour and minutes or hours as appropriate) and u is the notch (throttle) command input. In addition, D indicates the distance to be traveled, _Tf is the expected arrival time along the track at distance D, _Te is the tractive force produced by the locomotive consist, and _Ga is the gravitational resistance depending on the train length, train composition and the terrain on which the train is located , and R is the net speed-dependent drag of the locomotive consist and train combination. Initial and final velocities can also be specified, but are taken to be zero here without loss of generality (eg train stops at start and end). Eventually, the model is easily modified to include other important dynamics such as the hysteresis between changes in throttle u and the resulting traction or braking. Using this model, an optimal control formulation is set to minimize a quantitative objective function subject to constraints including, but not limited to, speed limits and minimum and maximum power (throttle) settings. Depending on the planning goals at any time, the problem can be flexibly set to minimize fuel subject to emissions and speed limits, or to minimize emissions subject to fuel usage and arrival time constraints.

It is also possible to achieve goals such as minimizing total travel time without total emissions or fuel use constraints where such loosening of constraints would be permitted or required for the mission. All of these performance measures can be expressed as any linear combination of the following:

$\underset{u\left(i\right)}{\min }\underset{0}{\overset{T_f}{\&Integral;}}f\left(u\left(t\right)\right)\mathrm{dt}$ -Minimize total fuel consumption (1)

$\underset{u\left(t\right)}{\min }{T}_f$ - Minimize travel time

$\underset{u_i}{\min }\underset{i=2}{\overset{{\mathrm{no}}_d}{\mathrm{\&Sigma;}}}{(u_i-u_{i-1})}^2$ - Minimize level manipulation (piecewise constant input)

$\underset{u\left(t\right)}{\min }\underset{0}{\overset{T_f}{\&Integral;}}{(\mathrm{du}/\mathrm{dt})}^2\mathrm{dt}$ - Minimal level manipulation (continuous input)

It is possible to replace the fuel term F in (1) with a term corresponding to emission generation. For example, for emissions -Minimize total emission generation. E in this equation is the emissions in gm/hphr for each of the notches (or power settings). Alternatively, minimization can be done based on a weighted total of fuel and emissions.

A commonly used and representative objective function is thus:

$\underset{\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; min</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\underset{\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; du</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; OP</span>}<span\; class="notranslate">\; )</span>$

The coefficients of this linear combination depend on the importance (weight) given to each of the terms. Note that in equation (OP), u(t) is the optimization variable, which is the continuous notch position. If discrete notches are required (eg for older locomotives), the solution to equation (OP) is discretized, which can lead to lower fuel savings. Finding a minimum time solution (α ₁ set to zero and α ₂ set to zero or a relatively small value) is used to find a lower bound on the achievable travel time (T _f =T _fmin ). In this case, both u(t) and T _f are optimization variables. In one embodiment, equation (OP) is solved for various values of T _f where T _f > T _{f min} , with α ₃ set to zero. In this latter case, T _{f is} taken as a constraint.

For those familiar with the solution of such optimization problems, it may be necessary to connect constraints, such as speed limits along the path:

0≤v≤SL(x)

Or when using the minimum time as the goal, an endpoint constraint must be maintained, e.g. the total fuel consumed must be less than the fuel in the tank, e.g. by:

$\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; <</span>\underset{\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}$

Here, _WF is the fuel remaining in the tank at _Tf . Those skilled in the art will readily recognize that equation (OP) may take other forms as well and that provided above is an exemplary equation for use in an exemplary embodiment of the invention. For example, those skilled in the art will readily recognize that the equation ( OP) variant.

References to emissions in the context of the exemplary embodiments of the present invention are actually directed to emissions using nitrogen oxides (NO _x ), carbon oxides (CO _x ), unburned hydrocarbons (HC), particulate matter (PM), etc. cumulative emissions in the form of However, other emissions may include, but are not limited to, maximums for electromagnetic emissions, such as limitations on radio frequency (RF) power output measured in watts at various frequencies emitted by the locomotive, and the like. Yet another form of emissions is noise produced by locomotives, typically measured in decibels (dB). Emission requirements may be based on variables such as time of day, time of year, and/or atmospheric conditions such as weather or levels of pollutants in the atmosphere. Emissions regulations may vary geographically across rail systems. For example, an operating region such as a city or state may have specified emission targets, and adjacent regions may have different emission targets, such as lower allowable emissions or higher fees for a given level of emissions.

Accordingly, an emissions profile for a certain geographic region may be adjusted to include maximum emission values for each of the specified emissions included in the profile to meet predetermined emission targets required for that region. Typically for a locomotive, these emission parameters are determined by, but not limited to, power (notch) setting, environmental conditions and engine control method. By design, each locomotive must meet EPA emission standards, and thus in an embodiment of the invention that optimizes emissions this may refer to mission total emissions, for which there is no prevailing EPA specification. Operation of the locomotive according to this optimized trip plan has been in compliance with EPA emission standards. Those skilled in the art will readily recognize that as diesel engines are used in other applications, other regulations may also be applicable. For example, _CO2 emissions are considered in certain international treaties.

If the goal during the trip mission is to reduce emissions, the optimal control formula, equation (OP), will be revised to take this trip goal into account. A key flexibility in the optimization setup is that any or all of the trip objectives can vary by geographic region or mission. For example, for high-priority trains, since it is high-priority traffic, minimum time may be the only goal on a route. In another example, emissions may vary from state to state along planned train routes.

To solve the resulting optimization problem, in the exemplary embodiment, the dynamic optimal control problem in the time domain is transcribed into an equivalent static mathematical programming problem with N decision variables, where the number "N" depends on making throttle and control The frequency of manual adjustments and the duration of strokes. For typical problems, this N can be in the thousands. For example, assume a train travels 172 miles (276.8 kilometers) of track in the Southwestern United States. With the trip optimizer system, the fuel in use can be achieved when comparing the trip determined and followed using the trip optimizer system with the actual driver throttle/speed history where the trip is determined by the operator. An exemplary 7.6% savings on . This increased savings is achieved because the trip optimizer system produces a driving strategy with less drag losses and little or no braking losses compared to the operator's trip plan.

To make the optimization described above computationally tractable, a simplified mathematical model of the train can be employed, such as the equations illustrated in Figure 2 and discussed above. As illustrated, certain setup specifications such as, but not limited to, information about consist, route information, train information and/or trip information are considered to determine a profile such as an optimized profile or the like. Such factors included in the profile include, but are not limited to, speed, distance remaining on the mission, and/or fuel used. Other factors that may be included in the profile are notch settings and time, as disclosed herein. A possible refinement of the optimal profile was generated by driving a more detailed model with the optimal power sequence generated to test isolation of other thermal, electrical and mechanical constraints. This results in a modified profile with the closest speed-to-distance relationship that can be achieved without harming the locomotive or train equipment, i.e. meeting additional requirements such as thermal and electrical constraints on the forces between the locomotive and the cars in the train. implicit constraints. Those skilled in the art will readily recognize how the equations discussed herein can be utilized with FIG. 2 .

Referring back to FIG. 1, once the trip begins 12, a power command is generated 14 to put the mission plan into motion. Depending on the operating settings of the trip optimizer system, a command is given to the locomotive to follow the optimized power command 16 in order to achieve the optimum speed. The trip optimizer system obtains actual speed and power information 18 from the train's locomotive consist. Due to unavoidable approximations in the model used for optimization, a corrected closed-loop calculation of the optimized power is obtained in pursuit of the desired optimal speed. Such corrections to train operating limits can be made automatically or by an operator who has ultimate control of the train at all times.

In some cases, the model used in the optimization may differ significantly from the actual train. This can occur for a number of reasons including, but not limited to, additional cargo pick-up or placement, locomotives that may become inoperable in the route, and due to operator error in the initial database 63 or data entry. For these reasons, the monitoring system is configured to use real-time train data to estimate locomotive and/or train parameters 20 in real time. The estimated parameters are then compared 22 to the assumed parameters used when the trip was originally formed. Based on any discrepancy in the assumptions and estimates, the trip can be replanned 24 should a sufficiently large savings be obtained from the new plan.

Other reasons why trips may be replanned include instructions from remote locations, such as dispatch and/or operators requiring target changes to align with more global movement planning targets. Additional global movement planning objectives may include, but are not limited to, other train schedules, allowing exhaust to dissipate from tunnels, maintenance operations, and the like. Another reason can be due to on-board degradation of components. Strategies for replanning can be grouped into incremental and major adjustments, depending on the severity of the disruption, as discussed in more detail below. In general, a "new" plan must be derived from the solution of the optimization problem equation (OP) described above, but often faster approximate solutions can be found, as described herein.

In operation, the locomotive 42 will continuously monitor system efficiency and continuously update the trip plan based on this measured actual efficiency (whenever such an update would improve trip performance). The replan calculations can be performed entirely within the locomotive or moved completely or partially to a remote location, such as a dispatch or wayside processing facility, where wireless technology is used to communicate the plan to the locomotive 42 . In one embodiment, the trip optimizer system may also generate efficiency trends, which may be used to develop locomotive platoon data with respect to efficiency transfer functions. This platoon-wide data can be used when determining the initial trip plan, and can be used for network-wide optimization trade-offs when considering the positions of multiple trains. For example, as discussed in detail below, a travel-time fuel-use trade-off curve updated from an overall mean taken from many similar trains on the same route as illustrated in FIG. . Thus, a central dispatch facility collecting curves like Figure 8 from many locomotives uses this information to better coordinate overall train movement for system-wide advantages in fuel usage or productivity. As disclosed above, those skilled in the art will recognize that various fuel types such as, but not limited to, diesel fuel, heavy marine fuel, palm oil, biodiesel, etc. may be used.

Furthermore, as disclosed above, those skilled in the art will recognize that various capability storage devices may be used. For example, the amount of power drawn from a particular source, such as a diesel engine and battery, may be optimized for maximum fuel efficiency/emissions, which may be an objective function. As further explained, assume that the total power demand is 2000 horsepower (HP), where the battery can supply 1500HP and the engine can supply 4400HP. The sweet spot may be when the battery supplies 1200HP and the engine supplies 200HP.

Similarly, the amount of power can also be based on the amount of energy stored and future energy needs. For example, if there is an imminent long-term high demand for power, the battery can be discharged at a slower rate. For example, if 1000 horsepower hours (HPhr) are stored in the battery and the demand is 4400HP for the next 2 hours, it may be optimal to discharge the battery at 800HP for the next 1.25 hours and take 3600HP from the engine for that duration.

Many events in day-to-day operations can lead to the need to create or modify a currently executing plan where it is desired to maintain the same trip goal, such as when a train does not make a planned meet or pass with another train on time ), it must compensate for time. Using the actual speed, power and position of the locomotive, a comparison is made between the planned arrival time and the current estimated (predicted) arrival time 25 . The schedule 26 is adjusted based on differences in time as well as differences in parameters (detected or changed by the scheduler or operator). This adjustment can be made according to the railroad company's expectations as to how such a deviation from plan should be handled, or alternatives can be presented manually to the on-board operators and dispatchers to jointly decide the best way to get back to plan. Whenever the plan is updated, additional changes may be factored in at the same time, such as new future speed limit changes, which may affect reverting to the original plan at any time, provided that the original goals (such as but not limited to arrival times, etc.) remain the same feasibility. In such instances, if the original trip plan cannot be maintained, or that is, the train cannot meet the original trip plan goals, other trip plans as discussed herein may be provided to the operator and/or remotely set or dispatched.

A replan 24 or an adjustment 26 to the plan as illustrated in FIG. 1 may also be made when it is desired to change the original goal. Such rescheduling may be performed at fixed pre-planned times, manually at the discretion of the operator or dispatcher, or autonomously when pre-defined limits such as train operating limits are exceeded. For example, if a currently planned execution run is delayed by more than a specified threshold, such as thirty minutes, etc., an exemplary embodiment of the invention may replan the trip as described above to accommodate this delay at the expense of increased fuel use, or alert the operator and How much time the dispatcher can make up in total (e.g., what is the minimum time to travel or the maximum fuel savings that can be made within time constraints). Other triggers for rescheduling are also conceivable based on consumed fuel or the health of the power pack, including but not limited to arrival time, loss of horsepower due to equipment degradation (e.g. running too hot or too cold) and/or e.g. Detection of total setup errors in train loads. That is, if changes reflect impairments in locomotive performance for the current trip, these can be factored into the models and/or equations used in the optimization.

Changes in planning goals can also arise from the need to coordinate events where one train's plan impairs another train's ability to meet goals and requires arbitration at a different level (eg, dispatch office). For example, the coordination of merging and passing can be further optimized through train-to-train communication. Thus, as an example, if a train knows it is behind schedule in arriving at a location to merge and/or pass, a communication from another train may inform the late train (and/or schedule). The operator can then enter information about being late into the trip optimizer system where the system will recalculate the train's trip plan. The trip optimizer system can also be used at a high level or network level to allow dispatch to determine which train should slow down or speed up (should it be the case that scheduled merge and/or transit time constraints may not be met). As discussed herein, this is accomplished by the trains sending data to the dispatcher to determine how each train should change the priority of its planning goals. Selection may be based on schedule, fuel saving benefits and/or emissions, depending on the circumstances.

Exemplary embodiments of the present invention may provide the operator with multiple trip/mission plans for any manually or automatically initiated re-plans. In an exemplary embodiment, the trip optimizer system provides different profiles to the operator, allowing the operator to select an arrival time and understand the corresponding fuel and/or emissions impact. Such information may also be provided to scheduling for similar consideration, either as a simple list of alternatives or as multiple tradeoff curves such as illustrated in FIG. 4 .

The trip optimizer system has the ability to learn and adapt to critical changes in train and power consist that may be included in current plans and/or future plans. For example, one of the triggers discussed above is the loss of horsepower. Transition logic is utilized to determine when the desired horsepower is reached as horsepower is increased over time after a loss of horsepower or when starting a trip. This information can be stored in the locomotive database 61 for use in optimizing future trips where horsepower loss is likely to reoccur, or the current trip.

Likewise, in a similar manner that multiple thrusters are available, each of which may require independent control. For example, a marine vessel may have many force generating elements or thrusters, such as but not limited to propellers. Each propeller may require independent control to produce optimum output. Thus, using transition logic, the trip optimizer system can determine which propeller to operate based on what has been learned previously and by adapting to key changes in marine vessel operation.

As mentioned above, FIG. 3 depicts various elements that may be part of a trip optimizer system according to an embodiment of the invention. A locator element 30 is provided to determine the position of the train 31 . The locator element 30 may be a GPS sensor or a sensor system, which determines the position of the train 31 . Examples of such other systems may include, but are not limited to, roadside devices such as radio frequency automatic equipment identification (RF AEI) tags, dispatch and/or video determination. Another system may include a tachometer on the locomotive and a distance calculation from a reference point. As previously discussed, a wireless communication system 47 may also be provided to allow communication between trains and/or with remote locations such as dispatch 60 . Information about travel locations can also be passed on from other trains.

A track characterization element 33 is also provided which provides information about the track, mainly grade and elevation and curvature information. The track characterization element 33 may include an onboard track integration database 36 . Sensors 28 are used to measure tractive effort 40 pulled by locomotives 42, throttle settings of locomotive consist 42, locomotive consist 42 configuration information, speed of locomotive consist 42, individual locomotive configuration, individual locomotive capabilities, and the like. In the exemplary embodiment, locomotive consist 42 configuration information may not be loaded using sensors 38 but otherwise input as discussed above. Additionally, the health of the locomotives in the consist may also be considered. For example, if a locomotive in the consist cannot operate above power notch level 5, this information is used when optimizing the trip plan.

Information from the locator element can also be used to determine the appropriate arrival time of the train 31 . For example, if there is a train 31 moving along track 34 towards a destination and no trains are following it, and the train has no fixed arrival deadlines to follow, including but not limited to RFAEI tags, scheduling, and/or video-determined positioning elements Can be used to measure the exact position of the train 31. Additionally, inputs from these signaling systems can be used to regulate train speed. Using the onboard track database discussed below and locator elements such as GPS, the trip optimizer system can adjust the operator interface to reflect the signaling system status at a given locomotive location. Where the signal status would indicate a restrictive speed ahead, the planner may choose to slow down the train to save fuel consumption.

Information from the locator element 30 can also be used to change the planning target as a function of the distance to the destination. For example, due to the inevitable uncertainty about traffic jams along a route, a "faster" time target may be employed on the early part of the route as a safeguard against delays that occur statistically later. If by chance a particular trip is encountered where no delays occur, the target on the later part of the journey can be modified to take advantage of the inherent rich time saved earlier, and thus regain some fuel efficiency. Similar strategies can be invoked with respect to emission limitation targets (eg when approaching urban areas).

As an example of a security policy, if a trip is planned from New York to Chicago, the system may have the option to run the train slower at the beginning of the trip, or in the middle of the trip, or at the end of the trip. In one embodiment, the trip optimizer system will optimize the trip plan to allow for slower travel at the end of the trip because unknown constraints (such as but not limited to weather conditions and track maintenance, etc.) may occur and be known during the trip. As another consideration, if traditionally congested areas are known, develop a plan with options for greater flexibility around these traditionally congested areas. Thus, the trip optimizer system may also consider weights/penalties as a function of time/distance into the future and/or based on known/past experience. At any time during the trip, planning and re-planning may also take into account weather conditions, track conditions, other trains on the track, etc., with the trip plan adjusted accordingly.

FIG. 3 further discloses other elements that may be part of the trip optimizer system. A processor 44 operable to receive information from the locator element 30 , the track-characterizing element 33 and the sensor 38 is provided. Algorithm 46 operates within processor 44 . The algorithm 46 is used to calculate an optimized trip/mission plan based on parameters involving the locomotive 42, train 31, track 34 and objectives of the mission as described above. In the exemplary embodiment, the trip plan is based on a model of the train's behavior as the train 31 moves along the track 34 , established as the solution of nonlinear differential equations derived from physics (simplifying the assumptions provided in the algorithm). Algorithms 46 have access to information from locator elements 30, track characterization elements 33, and/or sensors 38 to form minimized train consist 42 fuel consumption, minimize train consist 42 emissions, establish desired travel times, and/or guarantee On train consist 42 a trip plan for the appropriate crew operating time. In the exemplary embodiment, a controller element 51 (and/or the driver or operator) is also provided. As discussed herein, the controller element 51 is used to control the train in such a way that it follows the trip plan. In the exemplary embodiment discussed further herein, the controller element 51 makes train operating decisions autonomously. In another exemplary embodiment, an operator may be involved in directing the train to follow the trip plan.

A feature of the exemplary embodiment of the trip optimizer system is the ability to initially formulate and quickly modify any executing plan "without interruption". Due to the complexity of plan optimization algorithms, this includes forming an initial plan when long distances are involved. When the total length of the trip profile exceeds a given distance, the algorithm 46 can be used to segment tasks, where the tasks can be divided by waypoints. While only a single algorithm 46 is discussed, those skilled in the art will readily recognize that multiple algorithms may be used (and/or the same algorithm may be executed multiple times), where the algorithms may be linked together. Waypoints may include natural locations where trains 31 stop, such as, but not limited to, sidings where a merge with opposing traffic (or a train passing with a train behind the current train) is arranged to occur on a single-track railway, or where a junction with opposing traffic (or a train passing with a train behind the current train) is scheduled to occur on a single-track railway, or where a train 31 is to be accessed and Lay out sidings or work points for cars and where to plan work. At such waypoints, the train 31 may be required to be at the location at the scheduled time and stop or move with a speed within a specified range. The duration from arrival to departure at a waypoint is called the "dwell time".

In an exemplary embodiment, the trip optimizer system is able to break down longer trips into smaller segments in a special systematic way. Each segment can be somewhat arbitrary in length, but is typically chosen at natural locations such as stops or significant speed restrictions, or at key milestones defining intersections with other routes. Given a partition or segment selected in this way, a driving profile is formed for each segment of the track as a function of travel time taken as an independent variable, as shown in FIG. 4 . The used fuel/travel time trade-off associated with each segment may be calculated before the train 31 arrives at that track segment. An overall trip plan can be formed from the driving profiles formed for each segment. The exemplary embodiment of the present invention distributes the travel time among all segments of the trip in an optimal manner such that the required total travel time is met and the total fuel consumed on all segments is as little as possible. An exemplary three-segment trip is disclosed in FIG. 6 and discussed below. However, those skilled in the art will recognize that although segments are discussed, a trip plan may include a single segment representing a complete trip.

FIG. 4 depicts an exemplary embodiment of a fuel use/travel time curve 50 . As previously mentioned, such a curve 50 is formed when calculating the optimal trip profile for various travel times for each segment. That is, for a given travel time 49, the fuel used 53 is the result of the detailed driving profile calculated as described above. Once the travel time for each segment is assigned, the power/speed plan for each segment is determined from the previously computed solution. If there are any waypoint constraints on speed between segments, such as but not limited to changes in speed limits, they are matched during the formation of the optimal trip profile. If the speed limit is only changed in a single segment, the fuel use/travel time curve 50 must be recalculated only for that changed segment. This reduces the time that more parts or segments of the trip have to be recalculated. If a locomotive consist or train changes significantly along the route, for example due to locomotive wear or access or placement of cars, then the driving profiles for all subsequent segments must be recalculated, thereby forming a new instance of curve 50 . These new curves 50 will then be used along with the new dispatch objectives to plan the remaining trip.

Once the trip plan is developed as discussed above, the speed and power versus distance trajectories are used to reach the destination at the required trip time with minimal fuel use and/or emissions. There are several ways of performing trip planning. As provided in more detail below, in the exemplary embodiment, when the operator "coach" mode is employed, information is displayed to the operator for the operator to follow to achieve the desired power and speed determined from the optimal trip plan. In this mode, the health information includes suggested health conditions that the operator should use. In another exemplary embodiment, accelerating and maintaining a constant speed are performed autonomously. However, the operator is responsible for applying the braking system 52 when the train 31 must slow down. In another exemplary embodiment, commands to provide power and braking are provided as required to follow a desired speed-distance path.

The feedback control strategy is used to provide corrections to the power control sequence in the profile to make corrections for events such as, but not limited to, train load changes caused by fluctuating headwinds and/or tailwinds. Another such error may be caused by errors in train parameters such as, but not limited to, train mass and/or drag when compared to assumptions in the optimized trip plan. A third type of error may occur with the information contained in orbital database 36 . Another possible error may involve unsimulated performance differences due to locomotive engine, traction motor thermal derating, and/or other factors. Feedback control strategies compare the actual speed as a function of position to the speed in the desired optimum profile. Based on this difference, a correction to the optimal power profile is added to drive the actual rate closer to the optimal profile. To ensure a stable regulation, a compensation algorithm can be provided which filters the feedback velocity into the power correction such that closed performance stability is guaranteed. Compensation may include standard dynamic compensation as used by those skilled in the art of control system design to meet performance goals.

The trip optimizer system provides the easiest and therefore fastest method to accommodate changes in trip goals, which is the rule rather than the exception in railroad operations. In an exemplary embodiment, in order to determine the fuel-optimized trip from point "A" to point "B" (where there are stops along the way), and to update the trip for the remainder of the trip once the trip has started, the most important For the best itinerary profile, a suboptimal decomposition method is available. Using a simulation approach, a computational method finds a trip plan with specified travel times and initial and final velocities such that all speed limits and locomotive capacity constraints are satisfied when stops are present. While the following discussion is directed to optimizing fuel usage, it can also be applied to optimizing other factors such as, but not limited to, emissions, schedule, crew comfort, and load impact. This method can be used at the outset in developing a trip plan, and more importantly to adapt to changes in goals after a trip is initiated.

As discussed herein, exemplary embodiments of the present invention may employ a setup as illustrated in the exemplary flowchart depicted in FIG. 5 and as the exemplary three-stage example depicted in detail in FIG. 6 . As illustrated, a trip may be divided into two or more segments, T1, T2 and T3. (As mentioned above, it is possible to consider a trip as a single segment.) As discussed herein, segment boundaries may not result in equal segments. Instead, segmentation can use natural or task-specific boundaries. The optimal trip plan is precomputed for each segment. If fuel use vs. trip time is the trip goal to be met, a fuel vs. trip time curve is built for each segment. As discussed herein, the curve may be based on other factors, where the factors are the goals the trip plan is to meet. When travel time is the parameter being determined, the travel time for each segment is computed while satisfying the overall travel time constraint. FIG. 6 illustrates speed limits 97 for an exemplary three-segment, 200-mile (321.9-kilometer) trip. Further illustrated is the grade change 98 over the 200 mile (321.9 km) trip. Also shown is a combined graph 99 of curves illustrating fuel used over travel time for each trip segment.

Using the previously described optimal control settings and the computational methods described herein, the trip optimizer system can generate trip plans with specified travel times and initial and final velocities such that all speed limits and locomotive capacity constraints are met when stops are present. While the following detailed discussion is directed to optimizing fuel usage, it can also be applied to optimizing other factors as discussed herein, such as but not limited to emissions. The key flexibility is to adapt to the desired dwell time at the station and to take into account the earliest arrival at a location (where the time to be in it or the time obtained by the sideline is critical) as might be required, for example, in a single track operation and leaving constraints.

An exemplary embodiment of the invention finds a fuel-optimized trip traveling from distance _D0 to DM in time T with _M -1 intermediate stops at D1,..., _{DM-1 ,} and with arrival and departure times at these stations constrained by:

t _min (i)≤t _arr (D _i )≤t _max (i)-Δt _i

t _arr (D _i )+Δt _i ≤t _dep (D _i )≤t _max (i)i=1,...,M-1

Among them, t _arr (D _i ), t _dep (D _i ) and Δt _i are the arrival, departure and minimum stop times of the ^ith station respectively. Assuming fuel optimality implies minimizing stop time, thus t _dep (D _i )=t _arr (D _i )+Δt _i , which eliminates the second inequality above. Assume that for each i=1,...,M, the fuel-optimized trip from D _i-1 to D _i is known for travel time t (T _min (i) ≤ t ≤ T _max (i)) . Let F _i (t) be the fuel usage corresponding to the trip. If the travel time from D _j-1 to D _j is indicated as T _j , then the arrival time at D _i is given by:

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; arr</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>$

where Δt ₀ is defined as zero. The fuel-optimized trip from D ₀ to D _M for travel time T is then obtained by finding T _i , i=1, . . . , M, which minimizes:

$\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span>$

The conditions are:

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}$

$\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; T</span>}$

Once the trip is in progress, the problem is to re-determine the fuel-optimal solution for the remainder of the trip (initially from D ₀ to D _M at time T) while the trip is being run, but where disturbances prevent following this fuel-optimal solution. Let the current distance and velocity be x and v respectively, where D _i-1 <x≤D _i . And, let the current time since the start of the trip be t _act . Then the fuel-optimal solution for the remainder of the trip from x to D _M (which remains at D _M 's original arrival time) is obtained by finding T _j , obtained by j=i+1,..., M, which minimizes:

${\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>$

The conditions are:

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}$

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; T</span>}$

here is the fuel used for the optimal trip from x to D _i traveled at time t with an initial velocity v at x.

As discussed above, an exemplary way to achieve more efficient re-planning is to construct an optimal solution for station-to-site trips from the divided segments. For a trip from D _i-1 to D _i with travel time T _i , a set of intermediate points D _ij , j=1, . . . , N _i −1 is selected. Let D _i0 =D _i-1 and Then express the fuel usage for the optimal trip from D _i-1 to D _i as:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; )</span>$

where f _ij (t, v _{i, j-1} , v _ij ) is the fuel usage for the optimal trip from D _{i, j-1} to D _ij traveled at time t, with v _{i, j-1} and The initial and final velocities of v _ij . Also, t _ij is the time corresponding to the distance D _ij in the optimal trip. By limiting, Because trains in D _i0 and stop, $v_{i0}=v_{i_i}=0.$

The above expression enables the function F _i (t) to be obtained by first determining the function f _ij ( ), 1≤j≤N _i , and then finding τ _ij , 1≤j≤N _i and v _ij , 1≤j<N _i , alternatively determined, which minimizes

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; )</span>$

The conditions are:

$\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

v _min (i, j) ≤ v _ij ≤ v _max (i, j) j = 1, ..., N _i -1

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}$

By choosing D _ij (eg, at a speed limit or a merging point), v _max (i, j) - v _min (i, j) can be minimized, thereby minimizing the domain over which f _ij ( ) needs to be known.

Based on the division above, a simpler suboptimal replanning method than the one described above will limit the time to replan to trains at distance points D _ij , 1≤i≤M, 1≤j≤N _i . At point D _ij , the new optimal itinerary from D _ij to D _M can be found by finding τ _ik , j<k≤N _i , v _ik , j<k<N _i , and τ _mn , i<m≤M, 1≤n≤N _m , v _mn , i<m≤M, 1≤n<N _m , to determine the minimum

$\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; mn</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; mn</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; mn</span>}}<span\; class="notranslate">\; )</span>$

The condition is

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}$

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; T</span>}$

in

${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; mn</span>}}$

A further simplification is obtained by waiting for the recalculation of T _m , i<m≦M until the distance point D _i is reached. Thus, at a point D _ij between D _i-1 and D _i , the above minimization only needs to be performed on τ _ik , j<k≦N _i , vi _ik , j<k<N _i . T _i is increased as needed to accommodate any longer actual travel time from D _i-1 to D _ij than planned. This point increase is later compensated (if possible) by a recalculation at the distance point D _i , T _m , i<m≦M.

With respect to the closed-loop configuration disclosed above, the total input energy required to move the train 31 from point A to point B consists of the sum of four components, specifically: the difference in kinetic energy between points A and B; The difference in potential energy between B; energy losses due to friction and other drag losses; and energy dissipated by applied braking. Assuming the start and end velocities are equal (eg, fixed), the first component is zero. Furthermore, the second component is independent of driving strategy. Thus minimizing the sum of the last two components is sufficient.

Following a constant speed profile minimizes drag losses. Following a constant speed profile also minimizes total energy input when braking is not required to maintain a constant speed. However, if braking is required to maintain a constant speed, applying the brakes just to maintain a constant speed will likely increase the total energy required since the energy dissipated by braking must be replenished. There is some possibility that braking may actually reduce overall energy use by reducing speed variation (if the additional braking losses outweigh the offset from the resulting reduction in drag losses caused by braking).

After the replanning of acquisitions from the events described above is complete, the closure control described herein can be used to follow a new optimal notch/speed plan. In some cases, however, there may not be sufficient time to execute the segment breakdown plan described above, and particularly when there are critical speed restrictions that must be observed, alternatives are required. The exemplary embodiment of the present invention accomplishes this with an algorithm known as "smart cruise control". This intelligent cruise control algorithm is an efficient way of generating, without interruption, an energy-efficient (and thus fuel-efficient) sub-optimal solution for driving the train 31 over known terrain. The algorithm assumes that the position of the train 31 along the track 34 is known at all times, and that the slope and curvature of the track relate to position. The method relies on a point-mass model of the motion of the train 31 , the parameters of which can be adaptively estimated from online measurements of the train's motion as described earlier.

The smart cruise control algorithm has three main components, specifically: a modified speed limit profile that acts as an energy efficient (and/or emissions efficient or any other objective function) guide around speed limit reduction; A profile of ideal throttle or dynamic brake settings balanced between brake and brake; and a mechanism for combining the latter two components to produce a notch command employing a speed feedback loop to compensate for mismatches in simulated parameters when compared to real-world parameters . Smart cruise control may accommodate a strategy of no active braking (eg, signaling the driver and assuming it provides the necessary braking) or a variant of active braking in an exemplary embodiment of the invention.

Regarding cruise control algorithms that do not control dynamic braking, four exemplary components are a modified speed limit profile that acts as an energy-efficient guide around speed limit reductions, a notification signal for informing the operator when the brakes should be applied, an attempt at An ideal throttle profile that minimizes the balance between speed changes and informs the operator to apply the brakes, a mechanism that employs a feedback loop to compensate for mismatches between model and real-world parameters.

Also included in the exemplary embodiment of the trip optimizer system is a method of identifying key parameter values for the train 31 . For example, with regard to estimating train mass, Kalman filters and recursive least squares methods can be used to detect errors that may develop over time.

Figure 7 is a schematic diagram showing information flow between elements of an embodiment of a trip optimizer system. As previously discussed, a remote facility such as dispatch 60 may provide information. As illustrated, such information is provided to executive control element 62 . Also supplied to the executive control element 62 is information from a locomotive simulation database 63 ("Loco Models"), information from the track and/or segment database 36 (including, for example, track grade information and speed limit information and, for example, but not limited to Estimated train parameters such as train weight and drag coefficient) and the fuel ratio table from the fuel ratio estimator 64. The executive control element 62 supplies information to the trip profile planner 12 , which is disclosed in more detail in FIG. 1 . Once the trip plan has been calculated, this plan is supplied to the driving advisor, driver/operator or controller element 51 . This trip plan is also supplied to the executive control element 62 so that it can compare trips when other new data are provided.

As discussed above, the controller element 51 may automatically set the notch power, a pre-established notch setting or an optimum continuous notch power. In addition to supplying speed commands to the train 31, a display 68 is provided so that the operator can see what the planner recommends. The operator also has access to the control panel/desk 69 . Through this control panel 69, the operator can decide whether to apply the recommended notch power. To this end, the operator may limit the target or recommended power. That is, the operator always has final authority as to what power setting the locomotive consist will run at at any time. This includes deciding whether to apply braking if the trip plan recommends decelerating the train 31 . For example, if operating in dark terrain, or where the wayside equipment cannot electronically transmit information to the train and instead the operator views visual signals from the wayside equipment, the operator will visual signal to enter commands. Information about fuel measurements is supplied to a fuel ratio estimator 64 based on how the train 31 is functioning. Since direct measurement of fuel flow is typically not available in locomotive consist, all information about fuel consumed so far within the trip and planning to follow the best plan into the future uses such as those used in developing the best plan Calibrated physics model implementation. For example, such predictions may include, but are not limited to, using measured gross horsepower and known fuel and emissions characteristics to derive cumulative fuel used and emissions produced.

The train 31 also has a locator element 30 such as a GPS sensor. The information is supplied to the train parameter estimator 65 . Such information may include, but is not limited to, GPS sensor data, traction/braking force data, braking status data, speed and any changes in speed data. The train weight and drag coefficient information is supplied to the executive control element 62 with the information on the gradient and the speed limit information.

Exemplary embodiments of the present invention may also allow the use of continuously variable power throughout optimization planning and closed-loop control implementation. In conventional locomotives, power is typically quantified into eight discrete levels. Modern locomotives can achieve continuous variation in horsepower, which can be incorporated into the previously described optimization methods. With continuous power, locomotive 42 can further optimize operating conditions, such as by minimizing auxiliary loads and power transmission losses, and fine-tuning the engine horsepower region for best efficiency, or to the point of increased emissions margin. Examples include, but are not limited to, minimizing cooling system losses, adjusting alternator voltage, adjusting engine speed, and reducing the number of motor vehicle axles. Additionally, the locomotive 42 may use the on-board track database 36 and predicted performance requirements to minimize auxiliary loads and power transmission losses to provide optimum efficiency for target fuel consumption/emissions. Examples include, but are not limited to, reducing the number of motor vehicle axles on flat terrain and pre-cooling locomotive engines before entering tunnels.

An exemplary embodiment of the trip optimizer system may also use the on-board track database 36 and predicted performance to adjust locomotive performance to ensure that the train has sufficient speed as it approaches hills and/or tunnels. For example, this can be expressed as a velocity constraint at a particular location, which becomes part of the optimal plan generation formed by solving the equation (OP). Additionally, the trip optimizer system may include train handling rules such as, but not limited to, traction effort ramp rate and maximum braking effort ramp rate. These can be incorporated directly into the formula for the optimum travel profile or alternatively into a closed loop regulator for controlling power application to achieve the target speed.

In one embodiment, the trip optimizer system is only installed on the lead locomotive of the train consist. Even though the exemplary embodiment of the present invention does not rely on data or interaction with other locomotives, it can be used with consist managers as disclosed in US Patent No. 6,691,957 and US Patent No. 7,021,588 (owned by the assignee and incorporated by reference) and/or marshalling optimizer functions to improve efficiency. Interaction with multiple trains is not excluded, as illustrated by the example of arbitrating the scheduling of two "correlationally optimized" trains described herein.

Trains with distributed power systems can operate in different modes. One mode is where all locomotives in the train operate with the same notch command. Thus if the lead locomotive commands ON-N8, all units in the train will be commanded to generate ON-N8 power. Another mode of operation is "independent" control. In this mode, a locomotive or collection of locomotives distributed throughout the train can be operated at different starting or braking powers. For example, when a train reaches the top of a hill, the lead locomotive (on the downhill slope) may be braking, while the locomotive in the middle or end of the train (on the uphill slope) may be in motion. This is done to minimize the strain on the mechanical coupler connecting the rail car and the locomotive. Traditionally, operating a distributed power system in an "independent" mode required the operator to manually command each remote locomotive or set of locomotives through a display period in the lead locomotive. Using physics-based planning models, train setup information, on-board track databases, on-board operating rules, position determination systems, real-time closed-loop power/brake control, and sensor feedback, the system is able to automatically operate the distributed power system in a "stand-alone" mode.

When operating with distributed power, the operator in the lead locomotive can control the operating functions of the remote locomotives in the remote consist through a control system such as a distributed power control unit. Thus, when operating with distributed power, the operator can command each locomotive consist to operate at a different notch power level (or one consist can be on and the other can be on brake), where each locomotive consist Individual locomotives run at the same notch power. In an exemplary embodiment, employing a trip optimizer system mounted on a train and in communication with a distributed power control element, when the notch power level of the remote locomotive consist is expected to be recommended by the optimized trip plan, the trip optimizer The system will transmit this power setting to the remote locomotive consist for implementation. The same is true with respect to braking, as discussed below.

Exemplary embodiments of the present invention may be used with consists where the locomotives are not contiguous, eg, consist with one or more locomotives in front and others in the middle and/or rear of the train. Such a configuration is called "distributed power", where the standard connection between the locomotives is replaced by a radio link or auxiliary cables to link the locomotives externally. When operating with distributed power, the operator in the lead locomotive can control the operating functions of the remote locomotives in the consist through a control system such as a distributed power control unit. In particular, when operating with distributed power, the operator can command each locomotive consist to operate at a different notch power level (or one consist can be on and the other can be on brakes), where each locomotive consist Individuals run at the same level of power.

In an exemplary embodiment, employing a trip optimizer system mounted on a train and in communication with a distributed power control element, when the notch power level of the remote locomotive consist is expected to be recommended by the optimized trip plan, the trip optimizer The system will transmit this power setting to the remote locomotive consist for implementation. The same is true with respect to braking, as discussed below. When operating with distributed power, the optimization problem described previously can be enhanced to allow additional degrees of freedom, where each of the remote units can be independently controlled from the lead unit. The value of this is that additional objectives or constraints related to the train hook force can be included in the performance function (assuming a model reflecting the train hook force is also included). Thus, exemplary embodiments of the present invention may include the use of multiple throttle controls to better manage train hook forces as well as fuel consumption and emissions.

In a train utilizing the consist manager, the lead locomotive in a locomotive consist may operate at a different notch power setting than the other locomotives in the consist. The other locomotives in the consist run at the same notch power setting. The trip optimizer system may be used in conjunction with the consist manager to command the notch power settings of the locomotives in the consist. Thus, based on the trip optimizer system, since the consist manager divides the locomotive consist into two groups, the lead locomotive and the tail unit, the lead locomotive will be commanded to run at a certain notch power and the tail locomotive will be commanded to run at another certain notch power run. In an exemplary embodiment, a distributed power control element may be a system and/or device that accommodates this operation.

Likewise, when a consist optimizer is used with a locomotive consist, the trip optimizer system can be used in conjunction with the consist optimizer to determine notch power for each locomotive in the locomotive consist. For example, assume the trip planner recommends a notch power setting of 4 to the locomotive consist. Based on the position of the train, the consist optimizer will take this information and then determine the notch power setting for each locomotive in the consist. In this implementation, the efficiency of setting notch power settings on communication channels within a train is improved. Furthermore, as discussed above, implementation of this configuration can be performed using a distributed control system.

In addition, as previously discussed, exemplary embodiments of the present invention may be used to provide information about Replanning and continuous correction of when a train consist uses braking, where each locomotive in the consist may require a different braking option. For example, if a train is passing over a hill, the lead locomotive may have to enter a braking condition, while the remote locomotive that has not yet reached the top of the hill may have to remain in motion.

8, 9 and 10 are illustrations of dynamic displays 68 for use by an operator according to various embodiments of the invention. As shown in FIG. 8 , a trip profile 72 may be provided as part of the dynamic display 68 . Within this overview the location 73 of the locomotive is provided. Such information as train length 105 and number of cars in the train 106 is provided. Display elements are also provided regarding track grade 107 , curve and curb elements 108 , including bridge locations 109 and train speeds 110 . Display 68 allows the operator to view such information and also see where the train is along the route. Information regarding distance and/or estimated time of arrival to locations such as intersection 112 , signal 114 , speed change 116 , waypoint 118 , and destination 120 is provided. An arrival time management tool 125 is also provided to allow the user to determine the fuel savings achieved during the trip. The operator has the ability to change the arrival time 127 and see how this affects fuel savings. As discussed herein, those skilled in the art will recognize that fuel savings is the only example of a goal that may be reviewed with a management tool. To this end, other parameters discussed herein may be viewed and evaluated with management tools visible to the operator, depending on the parameter being viewed. Information is also provided to the operator as to how long the crew has been operating the train. In an exemplary embodiment, the time and distance information may be graphed as time and/or distance up to a particular event and/or location, or it may provide a total elapsed time.

As illustrated in FIG. 9 , an exemplary display provides information about group data 130 , event and condition chart 132 , arrival time management tool 134 , and action keys 136 . Similar information as discussed above is also provided in this display. The display 68 also provides action keys 138 to allow the operator to re-plan, and disarm 140 the trip optimizer system.

Figure 10 depicts another exemplary embodiment of a display. Data typical of a modern locomotive is visible including air brake status 71 , analog speedometer with digital insert 74 and information on tractive effort in pounds force (or tractive amp for DC locomotives). An indicator 74 is provided to show the current best speed in the plan being executed, and an accelerometer graph is provided to supplement the reading in mph/minute. Important new data for optimal plan execution is in the middle of the screen, which includes a scrolling strip chart 76 with optimal speed and notch settings versus distance compared to the current history of these variables. In the exemplary embodiment, the position of the train is obtained using a locator element. As illustrated, location is provided by identifying how far the train is from its final destination, absolute location, original destination, intermediate points, and/or operator input.

The strip chart provides a look-ahead estimate of the velocity change required to follow the optimal plan, which is useful in manual control, and to monitor the plan-actual relationship during automatic control. As discussed herein, for example when employing the guided mode, the operator may follow the notch or speed suggested by the exemplary embodiments of the present invention. The vertical bars give a graph of desired and actual notches, which are also shown numerically below the strip chart. When utilizing continuous notch power, as discussed above, the display will simply be rounded to the nearest discrete equivalent. The display may be an analog display such that an analog equivalent or percentage or actual horsepower/pull is displayed.

Key information about the trip status is displayed on the screen, and shows the current grade 88 encountered by the train (by the lead locomotive, at a location elsewhere along the train, or on average over the length of the train). Also shown are the planned distance traveled so far 90, the cumulative fuel used 92, where the next stop is planned 94 (and/or the distance to the next planned stop) and the current and extrapolated arrival times for the next stop 96. Display 68 also shows the maximum possible time to destination possible with the available calculated plans. If a later arrival is requested, a reschedule will be performed. Delta plan data shows the status of the fuel and the schedule ahead or behind the current best plan. Negative numbers mean less fuel than planned or earlier than it is, positive numbers mean more fuel than planned or later than it is, and typically trade off in the opposite direction (decelerating trains late to save fuel and vice versa) .

All the while, these displays 68 provide the operator with a snapshot of where the train is with respect to the currently enacted driving plan. This display is for illustrative purposes only, as there are many other ways of displaying/communicating this information to the operator and/or dispatch. To this end, the information disclosed above may be intermixed to provide a different display than that disclosed.

Other features that may be included in a trip optimizer system include, but are not limited to, allowing data logging and report generation. This information can be stored on the train and downloaded in time to an off-board system at some point. Downloading can occur manually and/or wirelessly. This information is also viewable by the operator via the locomotive display. Data may include information such as, but not limited to, operator input, time the system has run, fuel saved, fuel imbalance across locomotives in the train, train yaw travel, and system diagnostic issues such as if a GPS sensor fails.

Because trip planning must also take into account allowable crew operating time, exemplary embodiments of the present invention may take such information into account when planning a trip. For example, if the maximum time a crew can operate is eight hours, the itinerary may be changed to include stops for a new crew to replace the current crew. Such designated stopping locations may include, but are not limited to, rail yards, merge/pass locations, and the like. If the trip time may be exceeded as the trip progresses, the trip optimizer system may be overridden by the operator to meet the criteria as determined by the operator. Ultimately, the operator maintains control to command the speed and/or behavior of the train regardless of the train's operating conditions (eg, high load, low speed, and train run conditions).

Using the trip optimizer system, trains can run in multiple modes/configurations. In one operating concept, the trip optimizer system may provide commands for commanding propulsion and dynamic braking. The operator then handles all other train functions. In another operating concept, the trip optimizer system may provide commands for command-only propulsion. The operator then handles dynamic braking and all other train functions. In yet another operating concept, the trip optimizer system may provide commands for commanding the application of propulsion, dynamic braking and air braking. The operator then handles all other train functions.

The trip optimizer system can also be used to notify the operator of upcoming items of interest and/or actions to be taken. Specifically, using the predictive logic of exemplary embodiments of the present invention, continuous correction and replanning of optimized trip plans and/or track databases, operators can be notified of upcoming intersections, signals, grade changes, braking Actions, sidings, rail yards, fuel stations, and more. This notification can occur audibly and/or through an operator interface.

Specifically, using physics-based planning models, train setup information, onboard track databases, onboard operating rules, position determination systems, real-time closed-loop power/braking controls, and sensor feedback, the system presents and/or notifies the operator of required actions. The notification can be visual and/or audible. Examples include intersections where the announcement requires the operator to activate the locomotive horn and/or bell, and "silent" intersections where the announcement does not require the operator to activate the locomotive horn or bell.

In another exemplary embodiment, using the physics-based planning model, train setup information, on-board track database, on-board operating rules, position determination system, real-time closed-loop power/brake control, and sensor feedback discussed above, the operator Information (eg, displayed gauges) that allows the operator to see when the train will arrive at various locations may be presented, as illustrated in FIG. 9 . The system allows the operator to adjust the trip plan (eg, target arrival time). This information (actual estimated time of arrival or information needed to be obtained off-board) can also be communicated to the dispatch center to allow the dispatcher or dispatch system to adjust the target time of arrival. This allows the system to be tuned quickly and optimized for the appropriate objective function (eg tradeoff speed and fuel use).

Figure 11 depicts an exemplary embodiment of a railway track network with multiple trains. In the railway network 200, it is desirable to obtain optimized fuel efficiency and arrival times for the overall network of multiple interacting tracks 210, 220, 230 and trains 235, 236, 237. As illustrated, multiple tracks 210, 220, 230 are shown with a train 235, 236, 237 on each respective track. While the locomotive consist 42 is shown as part of the trains 235, 236, 237, those skilled in the art will readily recognize that any train may have only a single locomotive consist with a single locomotive. As disclosed herein, remote facility 240 may also be involved in improving fuel efficiency and reducing emissions from trains through optimized train power makeup. This can be accomplished with a processor 245 , such as a computer, located at the remote facility 240 . In another exemplary embodiment, the handheld device 250 may be used to facilitate improving the fuel efficiency of the trains 235, 236, 237 through optimized train power makeup. Typically employing either of these methods, the configuration of the trains 235, 236, 237 typically takes place at the hump, rail yard, etc. when the train is being compiled.

Alternatively, as discussed below, the processor 245 may be located on the train 235, 236, 237 or on another train where train setup may be done using input from the other train. For example, if a train has recently completed a mission on the same track, input from that train's mission may be supplied to the current train (as it executes and/or will begin its mission). Thus, configuring the train can take place during train running time and even during running time. For example, real-time configuration data can be used to configure train locomotives. One such example is provided above with respect to using data from another train. Another example entails using other data associated with train's trip optimization as discussed above. Additionally, train setup may be performed using input from multiple sources such as, but not limited to, dispatch systems, wayside systems 270, operators, off-line real-time systems, external settings, distributed networks, local networks, and/or centralized networks.

12 is a flowchart depicting an exemplary embodiment of a method for improving fuel efficiency and reducing emissions through optimized train power composition. As disclosed above, acceleration and matching braking may be minimized in order to minimize fuel use and emissions while maintaining arrival times. Undesirable emissions can also be minimized by powering a minimal set of locomotives. For example, in a train with several locomotives or locomotive consist, a minimum set of locomotives is powered at a higher power setting while the remaining locomotives are in idle, unpowered standby, or automatic engine start and stop (“AESS”) as discussed below mode that will reduce emissions. This is at least in part because exhaust aftertreatment devices (eg, catalytic converters) on locomotives are at temperatures below their optimal operating temperatures when the locomotive is operating at lower power settings (eg, notches 1-3). Therefore, using the minimum number of locomotives or locomotive consists to keep missions on schedule, operating at high power settings will allow the exhaust emission treatment devices to operate at optimum temperatures, thereby further reducing emissions.

The method illustrated in flowchart 500 in FIG. 12 provides for determining train load at 510 . When the engine is used in other applications, the load is determined based on the engine configuration. Train load may be determined with a load or train load estimator 560 as illustrated in FIG. 13 . In an exemplary embodiment, the train load is estimated based on information obtained as disclosed in the train composition checklist 480 as illustrated in FIG. 11 . For example, the train composition checklist 480 may be contained within the processor 245 (illustrated in FIGS. 11 and 13 ) where the processor 245 makes the estimate, or may be on paper on which the operator makes the estimate. Train composition list 480 may include information such as number of cars, car weight, car contents, car age, and the like. In another exemplary embodiment, the train load is estimated using historical data such as, but not limited to, previous train missions that made the same trip, similar train car configurations, and the like. As discussed above, using historical data can be done by a processor or manually. In yet another exemplary embodiment, train loads are estimated using rules of thumb or tabular data. For example, an operator configuring a train 235, 236, 237 may determine the required train load based on established guidelines such as, but not limited to, the number of cars in the train, the type of cars in the train, the weight of the cars in the train, and positive The volume of products transported by train, etc. This same rule-of-thumb determination can also be done using processor 245 .

Referring back to FIG. 12 , the disclosure identifies at 520 mission times and/or durations for diesel power systems. With respect to engines used in other applications, identifying a mission time and/or duration for a diesel power system may be equivalent to defining a mission time for which the engine configuration is expected to complete the mission. A determination is made at 530 as to the minimum amount of total power required based on the train load. A locomotive is selected at 540 to meet the minimum required power while producing improved fuel efficiency and/or minimized emissions.

Although FIG. 12 discloses determining the minimum total amount of power required, those skilled in the art will recognize that other mission characteristics may be substituted for determining the minimum total power required. As an example, and not disclosed as a limitation, a determination may be made regarding a minimum allowable discharge amount. Likewise, identifying task time and duration should not be considered a limiting factor. Those skilled in the art will readily recognize that other characteristics or factors can be identified. Such other characteristics and/or factors may include, but are not limited to, maintenance schedules, degraded operation of powered systems (e.g. restricted use due to failure or partial failure of systems and/or subsystems), signal and/or routing ( For example from a network point of view), track wear data, etc.

FIG. 34 depicts a flowchart illustrating an exemplary embodiment of a method for increasing fuel efficiency of a powered system through optimized powertrain. Flowchart 400 illustrates determining a demanded load or load requirement at 402 . At 404 a first characteristic associated with the powered system is identified. A second characteristic is identified at 406 , wherein a particular range of the second characteristic is desired based on load requirements. A power generating unit is selected at 408 to meet the specified range of the second characteristic while producing at least one of improved fuel efficiency and minimized emissions.

A locomotive may be selected based on the type of locomotive needed (based on its engine) and/or the number of locomotives needed (based on the number of engines). Similarly, with respect to diesel engines used in other power applications such as but not limited to marine, OHV, and stationary power stations, etc., multiple units of each are used to accomplish assigned tasks unique to the particular application.

To this end, a trip mission time determinator 570 as illustrated in FIG. 13 may be used to determine mission times based on information such as, but not limited to, weather conditions, track conditions, and the like. The locomotive composition may be based on the type of locomotive needed (as a function of power output or otherwise) and/or the minimum number of locomotives needed. For example, based on the locomotives available, those locomotives that just meet the total power required are selected. To this end, as an example, if ten locomotives are available, a determination is made of the power output from each locomotive. Based on this information, select the minimum number and type of locomotives needed to meet the total power requirement. For example, locomotives may have different horsepower (HP) ratings or starting tractive effort (TE) ratings. In addition to the required total power, the power distribution and power type in the train can be determined. For example, to limit the maximum coupler force on a heavy train, the locomotives may be distributed among the train. Another consideration is the capacity of the locomotive. It may be possible to place four DC locomotives at the head end of the train; however, four AC units with the same HP may not be used at the head end because the total hitch force may exceed specified limits.

In another exemplary embodiment, locomotive selection may not be based solely on reducing the number of locomotives used in the train. For example, if the total power requirement is minimally met by five of the available locomotives (when compared to also meeting the power requirement by using three of the available locomotives), five locomotives are used instead of three. Given these options, those skilled in the art will readily recognize that the minimum number of locomotives can be selected from a sequential (and random) set of available locomotives. Such an approach may be used when a train 235, 236, 237 has been programmed and decisions are being made during run time and/or during a mission, where the remaining locomotives are not used to power the train 235, 236, 237, as discussed in further detail below of.

When preparing trains 235, 236, 237, if trains 235, 236, 237 require standby power, incremental locomotives 255 or a plurality of locomotives can be added (see Figure 11). However, this additional locomotive 255 is segregated to minimize fuel usage, emissions, and power variation, but is available to provide backup power in the event an active locomotive becomes inoperable, and/or to provide additional power to complete within established mission times journey. Isolated locomotives 255 may be in AESS mode to minimize fuel usage while keeping the locomotives available when needed. In the exemplary embodiment, if a spare or isolated locomotive 255 is provided, its dimensions (eg, weight) may be considered when determining train load.

Thus, as discussed in greater detail above, determining the minimum power required to power a train 235, 236, 237 may occur at train run time and/or during a run (or mission). In this example, once a determination is made regarding optimized train power and the locomotives or locomotive consist 42 in the train 235, 236, 237 are identified to provide the necessary power needed, additional locomotives 255 not identified for use are idle or AESS mode.

In an exemplary embodiment, the overall task run may be divided into multiple parts or segments, such as but not limited to at least 2 segments, such as segment A and segment B as illustrated in FIG. 11 . Based on the amount of time it takes to complete any segment, reserve power provided by the isolated locomotive 255 is made available in the event incremental power is required to meet trip mission goals. To this end, the isolation locomotive 255 may be utilized for a particular trip segment to bring the train 235, 236, 237 back on time and then closed for the subsequent segment (if the train 235, 236, 237 remains on time).

Thus, in operation, the lead locomotive can keep the locomotive 255 provided for incremental power in isolation mode until that power is required. This can be done using a wired or wireless modem or communication from the operator, usually on the lead locomotive, to the isolated locomotive 255. In another exemplary embodiment, the locomotives operate in a distributed power configuration and isolated locomotives 255 are already incorporated in the distributed power configuration, but they are idle and activated when additional power is required. In yet another embodiment, the operator puts the isolated locomotive 255 in the appropriate mode.

In the exemplary embodiment, initial settings of locomotives based on train loads and mission times are updated by the trip optimizer, as disclosed above, and adjustments are made to the number and type of locomotives. As an exemplary illustration, consider a locomotive consist 42 of three locomotives having relative maximum power available of 1, 1.5, and 0.75, respectively. (Relative available power is relative to a "baseline" locomotive, which is used to determine total consist power. For example, in the case of a "3000HP" baseline locomotive, the first locomotive has 3000HP, the second 4500HP, and the third 2250HP.) Hypothetical Mission Divided into seven segments. Considering the above scenario, the following combinations are available and can be matched to track part loads: 0.75, 1, 1.5, 1.75, 2.25, 2.5, 3.25, which are the combinations of maximum relative HP settings for the composition. Thus, for each of the respective relative HP settings mentioned above, for the 0.75 setting, the third locomotive is on and the first and second are off, for 1 the first locomotive is on and the second and third are off, and so on. In one embodiment, the trip optimizer selects the maximum required load and adjusts through notch calls while minimizing overlap in power settings. So if a segment requires between 2 and 2.5 (by 3000HP) then use locomotive 1 and locomotive 2 while locomotive 3 is idle or in standby mode depending on how long it was in the segment and when the locomotive restarted.

In another exemplary embodiment, an analysis may be performed to determine a trade-off between emissions and locomotive power settings to maximize higher notch operation where emissions from exhaust aftertreatment devices are more optimal. The analysis may also take into account one of the other parameters discussed above with respect to train operation optimization. The analysis can be performed on the entire task run, segments of the task run, and/or a combination of both.

13 depicts a block diagram of elements included in a system for optimized train power makeup according to one aspect of the present invention. As illustrated and discussed above, a train load estimator 560 is provided. A travel task time determinator 570 is also provided. A processor 245 is also provided. As disclosed above, although for trains, similar elements may be used for other engines not used in rail vehicles, such as but not limited to off-road vehicles, marine vessels, and stationary units. Processor 245 calculates the total amount of power required to power trains 235 , 236 , 237 based on the train load determined by train load estimator 560 and the trip mission time determined by trip mission time determinator 570 . The required locomotive type and/or the required number of locomotives are determined further based on each locomotive power output to at least achieve a minimum total amount of power required based on train load and trip duty time.

Travel task time determinator 570 may segment the task into multiple task segments, such as segment A and segment B, as discussed above. The total power amount can then be determined separately for each segment of the task. As discussed further above, the additional locomotive 255 is part of the trains 235, 236, 237 and is provided for backup power. Power from this spare locomotive 255 may be used incrementally when a need is identified, such as but not limited to providing power to bring the trains 235, 236, 237 back on time for a particular trip segment. In this case, the trains 235, 236, 237 are operated to meet and/or meet the trip mission time.

Train load estimator 560 may estimate train load based on information contained in train composition inventory 480, historical data, rule-of-thumb estimates, and/or tabular data. Additionally, the processor 245 may determine a trade-off between emissions and locomotive power settings to maximize higher notch operation where emissions from exhaust aftertreatment devices are optimized.

14 depicts a block diagram of a transfer function for determining fuel efficiency and emissions of a diesel powered system. Such diesel powered systems include, but are not limited to, locomotives, marine vessels, OHVs, and/or stationary power generating stations. As illustrated, information about input energy 580 (eg, power, waste heat, etc.) and information about post-processing 583 are provided to transfer function 585 ("f(x,y)"). The transfer function 585 utilizes this information to determine optimal fuel efficiency 587 and emissions 590 .

15 depicts an exemplary embodiment of a method for determining a configuration of a diesel powered system having at least one diesel fueled power generating unit. As shown in flowchart 600 , the method includes determining at 605 a minimum power required from the diesel powered system in order to accomplish the specified task. As at 610 , an operating condition of the diesel-fueled power generating unit is determined such that a minimum power requirement is met while yielding at least one of lower fuel consumption and/or lower emissions of the diesel powered system. As disclosed above, the method illustrated in flowchart 600 is applicable to multiple diesel-fueled power generating units such as, but not limited to, locomotives, marine vessels, OHVs, and/or stationary power generating stations. Additionally, the flowchart 600 can be implemented using a computer software program, which can reside on a computer readable medium.

Figure 16 depicts an exemplary embodiment of a closed loop system for operating a rail vehicle. As illustrated, the system includes an optimizer 650, a converter 652, a rail vehicle 653, and at least one output 654 from which specific information is collected, such as, but not limited to, speed, emissions, traction, horsepower, and friction modification techniques (e.g., applied sand). Output 654 may be determined by sensor 656 that is part of rail vehicle 653 , or independent of rail vehicle 653 in another exemplary embodiment. Information initially derived from information generated by trip optimizer 650 and/or a regulator is provided to rail vehicle 653 via converter 652 . Locomotive data collected by sensors 656 from rail vehicles is then communicated back to optimizer 650 over closed loop communication path 657 .

The optimizer 650 determines the operating characteristics of at least one factor to be adjusted, such as speed, fuel, emissions, and the like. The optimizer 650 determines at least one of power and/or torque settings based on the determined optimization values. Converter 652 provides for converting information about power, torque, speed, emissions, friction modification techniques (such as but not limited to applying sand), settings, configuration, etc., into control inputs suitable for application to a rail vehicle 653 (typically a locomotive) form. Specifically, this information or data may be converted into electrical signals.

As described in further detail below, the converter 652 may interface with any of a number of devices, such as a master controller, a remote control locomotive controller, a distributed power drive controller, a train line modem, an analog input wait. FIG. 17 depicts a closed loop system in combination with a main control unit or controller 651 . The converter may, for example, selectively disconnect or disable the output of the master controller (or actuator) 651 . (Master controller 651 is generally used by the operator to command the locomotive, as pertaining to power, horsepower, traction, implementation of friction modification techniques (such as but not limited to applying sand), braking (including dynamic braking, air braking, manual braking) etc.), propulsion, etc. Those skilled in the art will readily recognize that the master controller can be used to control both hard switches and software-based switches used in controlling the locomotive.) Once the master controller 651 is disconnected On, the converter 652 then replaces the main controller 651 to generate the control signal. Disengagement of the actuator 651 may be through a wire, a software switch, a configurable input selection process, or the like. The illustrated switching device 655 performs this function. More specifically, the operator control input to the master controller 651 is disconnected.

While Figure 17 discloses a master controller 651, this is locomotive specific. Those skilled in the art will recognize that in other applications such as those disclosed above, other devices may provide equivalent functionality to a master controller as used in a locomotive. For example, the accelerator pedal is used in OHVs or transit buses, and the excitation control is used on the generator. With regard to marine vessels, there may be multiple force generating devices (eg propellers), at different angles/orientations, which are controlled in a closed loop manner.

As discussed above, the same technique can be used for other devices such as controlling locomotive controllers, distributed power drive controllers, train line modems, analog inputs, and the like. Although not illustrated, those skilled in the art will readily recognize that converters could equally use these devices and their associated connections to the locomotive for applying input control signals to the locomotive. The communication system 657 of these other devices may be wireless or wired. More specifically, the converter may interface with devices other than the main controller 651 (eg, drive controller, modem, etc.).

Figure 18 depicts an exemplary embodiment of a closed loop system for operating a rail vehicle in conjunction with another input operating subsystem of the rail vehicle. For example, distributed power drive controller 659 may receive input from various sources 661 (such as, but not limited to, operators, train line and locomotive controllers, etc.) and transmit information to locomotives at remote locations. Converter 652 may provide information directly to the input of DP controller 659 (as an additional input) or disconnect one of the inputs and send the information to DP controller 659 . A switch 655 is provided to instruct the converter 652 how to provide information to the DP controller 659 as discussed above. Switch 655 may be a software-based switch and/or a wired switch. Additionally, switch 655 need not be a two-way switch. A switch can have multiple switch directions based on the number of signals it controls.

In another exemplary embodiment, the converter may command the operation of the main controller, as illustrated in FIG. 19 . The converter 652 has mechanical means for automatically moving the actuator 651 based on electrical signals received from the optimizer 650 .

Sensors 656 are provided on the locomotive to collect operating condition data 654 such as speed, emissions, traction, horsepower, and the like. Locomotive output information from sensors 656 is then provided to optimizer 650, typically via rail vehicles 653, completing the closed loop system.

Figure 20 depicts another closed loop system, but with the operator in the loop. Optimizer 650 produces the power/operating characteristics required for optimum performance. This information is communicated to operator 647 via a human machine interface (HMI) and/or display 649 or the like. Information may be conveyed in a variety of formats including audio, text or graphic or visual displays. The operator 647 in this case can operate the master controller or pedal or any other actuator 651 to follow the optimum power level.

If the operator follows the plan, the optimizer continuously displays the required next steps. If the operator does not follow the plan, the optimizer can recalculate/reoptimize the plan, depending on the deviation and duration of the deviation from the plan in power, speed, position, emissions, etc. In an exemplary embodiment, the optimizer may control the vehicle to ensure optimized operation if the operator fails to meet the optimized plan to the extent that re-optimizing the plan is not possible or where safety criteria have been or may be exceeded, Notifications need to allow for optimized mission planning or simply log the event for future analysis and/or use. In such an embodiment, the operator can regain control by manually disabling the optimizer.

FIG. 21 is a flowchart 300 illustrating an exemplary embodiment of a method for trip optimization when operator input may be included in a decision loop. At 301 , an optimized mission plan is provided, which can be applied manually. More specifically, an input device is available through which an operator can control the vehicle (or other powered system) based on the information contained in the optimized mission plan. (For example, aspects of the optimized mission plan may be displayed to the operator as guidance for controlling the vehicle via the input device.) At 302, the optimized mission plan is re-planned in response to the manual mission plan being implemented. The manual mission plan may be a manual application/implementation of the optimized mission plan, or it may be unrelated to the optimized mission plan, i.e., the optimized mission plan is presented to the operator for use in controlling the vehicle, but the operator manually applies a different plan. (In one embodiment, the manual mission plan may be an actual plan, i.e., a predetermined set of control operations; in another embodiment, the manual mission plan includes the operator manually applying an optimized mission plan, which includes the effective possible variation due to the nature of the defect; in another embodiment, the manual mission plan includes any control actions performed by the operator for controlling the vehicle, where the control actions are not predetermined.) Responsive to the manual task plan being implemented The purpose of planning the optimized mission plan is to account for the deviation between the optimized mission plan and the human mission plan, i.e., the optimized mission plan is re-planned to reflect the actual way the vehicle is controlled by the human mission plan. (Thus, in one embodiment, if the optimized mission plan is manually applied and does not deviate from the optimized mission plan, then the optimized mission plan is not re-planned.) At 303, when the manual mission plan deviates from the optimized mission plan by more than predetermined amount, the manual plan can be adjusted autonomously, for example based on the information contained in the optimized plan. For example, if an optimized mission plan provides a certain velocity for a given segment of a mission, if a manually applied mission plan results in exceeding that velocity, the optimized mission plan can be implemented autonomously to make corrections to ensure that velocity remains at an acceptable rate. For example when a hard limit is about to be broken (a hard limit means a limit that cannot be broken under any circumstances) or when a soft limit is exceeded for a predetermined amount of time (a soft limit means a limit that can be broken but only for a limited Limits under specified conditions) can use such a method.

In another exemplary embodiment, when the vehicle is being controlled based on the optimized mission plan, the operator is allowed to modify, adjust or trim the values determined by the optimized mission plan—by a selected amount or for a given period of time part. By way of example, if the trip optimizer system commands a particular speed for a particular segment of the track, but by way of example only, this is a mission segment that the operator has previously traveled through and prefers a different speed, the trip optimizer system is configured to allow the operator to adjust the speed, The adjusted speed is assumed to be within a preset adjustment range as established within the trip optimizer system. If the adjustment is outside the adjustment range, the operator has the option of disabling the stroke optimizer system and then setting the preferred speed. Similarly, the optimizer system can be configured to modify an operator command-selected amount.

22 depicts an exemplary embodiment of a trip optimization method in which an operator interface is available to an operator to adjust, modify, and/or fine-tune an optimized mission plan or commands related thereto. In flowchart 305 of FIG. 22 , at 306 , the mission executes autonomously according to the optimized mission plan. "Autonomous execution" may include performing optimized tasks automatically using closed-loop techniques. The task plan is manually adjusted. More specifically, an input device is provided at 307 configured to allow an operator to manually fine-tune at least one characteristic of the task within a predetermined range while the task is in progress. The mission plan may be re-optimized (at 308 ) after the optimized mission plan is fine-tuned. More specifically, re-optimization of mission plans occurs at other times, not just before mission plan implementation. At 308, the mission plan may be adjusted to correspond to the optimized mission plan after a certain period of time and/or when at least one characteristic of the mission is fine-tuned to meet certain criteria. For example, where the operator desires to operate the locomotive at a given speed for some portion of the mission, when the operator adjusts the mission plan, the operator can also implement (when the operator wishes) an optimized mission to be followed planning (eg after exiting a tunnel). Re-planning of the optimized mission plan may be performed prior to reusing the optimized mission plan. The terms "adjustment" and "fine-tuning" are both used here. Trimming also refers to adjustment or regulation, however trimming may be considered as making finer adjustments.

Thus, one embodiment relates to a method 305 for operating at least one powered system having at least one power generating unit. The method includes autonomously executing 306 the mission according to the optimized mission plan. The method also includes fine-tuning 307 at least one characteristic of the mission plan within a predetermined range by input means configured for manual input and fine-tuning of at least one characteristic of the mission plan while the mission is in progress (herein, as indicated above, “fine-tuning " roughly refers to regulation).

The inversion of the embodiment of Fig. 22 is also possible. More specifically, FIG. 23 shows a flow diagram of a trip optimization method illustrating a situation where the trip optimization system may modify the operator's mission plan or order. The flowchart 310 of FIG. 22 illustrates at 311 tasks performed with a manually implemented task plan. At 312, while in progress, the manually implemented missions are fine-tuned, adjusted and/or modified with information contained in the optimized mission plan. The mission plan is re-optimized after the optimized mission plan has been fine-tuned, adjusted and/or modified. As further disclosed, the mission is adjusted to correspond to the manually implemented mission plan after a certain period of time and/or when at least one characteristic of the mission is fine-tuned to meet certain criteria.

In another embodiment, an operator and a trip optimizer system may work together to operate a diesel powered system. For example, an operator may control a characteristic such as but not limited to pitch, and the stroke optimizer system is configured to control at least one other characteristic such as but not limited to thrust. In another exemplary embodiment, where multiple thrusters and/or engines are available, the operator can control at least one thruster and/or engine and the trip optimizer system can control at least one other thruster and/or engine or engine.

24 depicts an exemplary embodiment of a trip optimization method in which portions of a task are divided between a trip optimizer system and another entity (or multiple other entities), such as but not limited to an operator. In this embodiment, an optimized mission plan is provided at 316 as shown in flowchart 315 . At 317, at least one characteristic of the mission plan is manually controlled. At 318, at least another characteristic in the mission plan is autonomously controlled. At 319, the optimized mission plan is adjusted autonomously through a closed-loop process based on at least one manually controlled characteristic.

Embodiments disclosed herein may also be used where the powered system is part of a fleet and/or network of powered systems. FIG. 25 shows a flowchart 320 depicting an exemplary embodiment of a method for operating a powered system having at least one power generating unit, where the powered system may be part of a fleet and/or network of powered systems. At 322, the method includes evaluating operating characteristics of at least one power generating unit. At 324, the operating characteristic is compared to a specified value related to the mission objective. At 326, operating characteristics are adjusted autonomously to meet mission objectives. As disclosed herein, autonomous adjustments can be performed using closed-loop techniques.

Figure 26 depicts an exemplary method for operating a rail vehicle in a closed loop process. As shown in flowchart 660 in FIG. 26 , at 662 the method includes determining optimal settings for the locomotive consist. The optimized settings may include settings for any set variable (eg, power level, optimized torque emissions, other locomotive configurations, etc.). At 664, the method also includes translating the optimized power level and/or torque setting into an input signal recognizable to the locomotive consist. At 667, at least one operating condition of the locomotive consist is determined when at least one of the optimized power level and the optimized torque setting is applied. At 668, the method further includes communicating the at least one operating condition to the optimizer system (within the closed control loop), wherein the optimizer system uses the at least one operating condition to further optimize the power level and/or torque setting.

As disclosed above, the method of flowchart 660 may be performed using computer software code. Thus, for rail vehicles that may not initially have the capability to utilize the method of flowchart 660 disclosed herein, electronic media containing computer software modules may be accessed by a computer on board the rail vehicle such that the software modules may be loaded onto the rail vehicle for implementation. Electronic media shall not be limiting, as any computer software module may also be loaded via electronic media transmission systems including wireless and/or wired transmission systems, such as but not limited to using the Internet to effectuate the installation.

Locomotives generate emission rates based on class level. In fact, lower notch levels do not necessarily result in lower emissions per unit of output, eg, gm/hp-hr, and vice versa. Such emissions may include, but are not limited to, particulates, exhaust, heat, and the like. Similarly, noise levels from locomotives may also vary based on notch levels, particularly noise frequency levels. Thus, while emissions are referred to herein, those skilled in the art will readily recognize that the exemplary embodiments of the present invention are also applicable to reducing noise levels produced by diesel powered systems. Therefore, even though emissions and noise are disclosed herein at different times, the term emissions should be understood to also include noise.

When the operator requests a specific horsepower level or notch level or other throttle level, the operator is expecting the locomotive to run at a certain pulling power or effort. In the exemplary embodiment, in order to minimize emissions, the locomotive is able to switch between notch/power/engine speed levels while maintaining the operator's desired average pulling power. For example, assume the operator requests notch 4 or 2000HP. The locomotive can then run at notch 3 for a given period of time, say one minute, then move to notch 5 for a period of time, and then back to notch 3 for a period of time such that the average power produced corresponds to notch 4 . The locomotive is moved to notch 5 because it is known that emissions from locomotives placed in notch are lower than when in notch 4. During the total time the locomotive moves between notch settings, the average is still notch 4 so that the operator's desired pulling power is still achieved.

The time at each notch is determined by various factors such as emissions at each notch, power level at each notch, and operator sensitivity. Those skilled in the art will readily recognize that embodiments of the present invention are operable when the locomotive is being manually operated and/or when operation is performed autonomously (such as but not limited to when controlled by a trip optimizer system) and during low speed control of.

In another embodiment, multiple set points are used. These set points may be determined by considering factors such as, but not limited to, notch setting, engine speed, power, engine control settings, and the like. In another embodiment, when using multiple locomotives that can operate at different notch/power settings, the notch/power setting is determined as a function of performance and/or time. When emissions are reduced, other factors can be weighed against reducing emissions. Such factors include, but are not limited to, fuel efficiency and noise. Likewise, if the desire is to reduce noise, emissions and fuel efficiency may be considered. If fuel efficiency is to be improved, a similar analysis can be applied.

FIG. 27 depicts an example of a speed versus time graph comparing current operation to emissions optimized operation. The speed variation compared to the desired speed can be minimized arbitrarily. For example, if the operator desires to move from one speed (S1) to another (S2) within a desired time, it can be achieved with a slight deviation.

FIG. 28 depicts a modulation pattern resulting in maintaining a constant desired notch and/or horsepower. The amount of time at each notch depends on the number of locomotives and the weight and characteristics of the train. Essentially, the train's inertia is used to combine tractive power/force to achieve the desired speed. For example, if the train is heavy, the time between transitions of notches 3 to 5 in the example may be large, and vice versa. In another example, if the number of locomotives for a given train is huge, the time between transitions must be smaller. More specifically, the time modulation and/or cycle will depend on train and/or locomotive characteristics.

As previously discussed, emissions may be based on an assumed notch distribution, but the operator/railway is not required to have this overall distribution. Thus, it is possible to apply notch distribution over a period of time, over many locomotives over a period of time, and/or to a train of locomotives over a period of time. Provided with emissions data, the trip optimizer system can compare desired notch/power to emissions based on notch/power settings and determine notch/power cycling to meet the required speed while minimizing emissions. Optimization can be used explicitly to generate plans, or plans can be modified to implement, reduce and/or meet required emissions.

29 depicts an exemplary method for determining a configuration of a diesel powered system having at least one diesel fueled power generating unit. The method is illustrated in flowchart 700 in FIG. 29 and includes determining at 702 a minimum power (or power level) required from the diesel powered system in order to accomplish a specified task. At 704, the method additionally includes determining the emissions based on the required minimum power (power level). At 706, the method further includes using at least one other power level that results in lower emissions, wherein the total resulting power is close to the requested power. Thus, in operation, a desired power level may be used together with at least one other power level, and/or two power levels may be used that do not include the desired power level. In a second example, if the desired power level is notch 4, the two power levels used may include notch 3 and notch 5, as disclosed.

Emissions data (based on notch speed) is provided to the trip optimizer system. If a certain notch speed produces high emissions, the trip optimizer system can act by cycling between notch settings that produce lower emissions, so that the locomotive will avoid operating in that particular notch while still meeting the avoidance requirements. The speed at which the level is set. For example, applying the same example provided above, if notch 4 is identified as being less than optimal for operation due to emissions, but notches 3 and 5 produce lower emissions, the trip optimizer system can and 5, where the average speed is equal to the speed achieved at level 4. Thus, the total emissions are less than would be expected at notch 4 when the speed associated with notch 4 is provided.

Thus, while operating with this configuration, overall emissions over missions completed may be improved, although speed constraints imposed based on defined notch limits may not actually be obeyed. More specifically, while it may be imposed in certain areas that rail vehicles will not exceed notch 5, the trip optimizer system may determine that cycling between notches 6 and 4 may be preferable to reach the notch 5 speed limit while also improving Emissions because the combined emissions of notch 6 and 4 are better than when operating at notch 5 because notch 4 or notch 6 or both are better than notch 5.

Figure 30 illustrates a system for minimizing emissions, noise levels, etc. from a diesel powered system having at least one diesel fueled power generating unit while maintaining a specific speed. System 722 includes processor 725 for determining the minimum power required from diesel powered system 18 in order to accomplish a given task. The processor 725 can also determine when to alternate between two power levels. Determining means 727 are used to determine the emissions based on the required minimum power. Also included is a power level controller 729 for alternating between power levels to achieve the required minimum power. The power level controller 729 functions to produce lower emissions while the total average resulting power approaches the required minimum power.

Figure 31 illustrates a system for minimizing emissions, noise output, etc. from a diesel powered system having at least one diesel fueled power generating unit while maintaining a specific speed. The system includes a processor 727 for determining the power level required from the diesel powered system in order to accomplish a given task. The system also includes an emissions meter device 727 for determining emissions based on the required power level. Emission comparison means 731 are also disclosed. The emission comparing means 731 compares the emission amount of other power levels with the emission amount based on the requested power level. Emissions from the diesel fueled power generating unit 18 are reduced based on the requested power level by alternating between at least two other power levels that produce less emissions than the requested power level, wherein between the at least two other power levels Alternating between producing (i) an average power level close to the required power level while (ii) producing lower emissions than the required power level. As disclosed herein, alternating may simply result in using at least one other power level. Thus, while power levels (eg, throttle settings) are characterized herein as alternating, the term is not used as limiting. To this end, means 753 are provided for alternating between at least two power levels and/or using at least one other power level.

While the examples above illustrate cycling between two notch levels to satisfy a third notch level, those skilled in the art will readily recognize that more than two notch levels may be used when attempting to meet a particular desired notch level. . Accordingly, three or more notch levels may be included in the cycle to achieve a particular desired notch level to improve emissions while still meeting speed requirements. Additionally, one of the alternate notch levels may be the desired notch level. Thus, at a minimum, a desired notch level and another notch level may be two power levels alternating between them.

32 shows a flowchart illustrating an exemplary embodiment of a method for operating a diesel powered system having at least one diesel fueled power generating unit. (The method can also be applied to control other powered systems with other power generating units). Operate a diesel powered system to perform a mission with one or more mission objectives. The mission objective may include consideration of at least one of total emissions, maximum emissions, fuel consumption, speed, reliability, wear, force, power, mission time, arrival time, midpoint time, and braking distance of the powered system. Those skilled in the art will readily recognize that mission objectives may further include other objectives based on the specific mission of the diesel powered system. For example, as disclosed above, the mission objectives of a locomotive differ from that of a stationary power generation system. Thus, the mission objective is based on the type of diesel powered system and the nature of the diesel powered system's mission.

The method, as shown in flowchart 800 in FIG. 32 , includes evaluating at 802 operating characteristics of a diesel powered system. "Operating characteristic" means a characteristic, characteristic or quality relating to the operation of a powered system, which includes a characteristic, characteristic or quality of the powered system itself or the environment in which the powered system operates. Thus, the operating characteristics may include at least one of emissions, speed, horsepower, friction modifiers, tractive effort, total power output, mission time, fuel consumption, energy storage, and/or conditions of surfaces on which the diesel powered system operates. Energy storage is important when a diesel powered system is a hybrid system having, for example, a diesel fueled power generation unit as its primary power generation system and an electric, hydraulic or other non-diesel power generation system as its secondary power generation system of. With respect to speed, the operating characteristics can be further subdivided with respect to the speed of change in time and the speed of change in position.

When used in conjunction with at least one other diesel powered system, the operating characteristic may be further based on the location of the diesel powered system. For example, a train may include multiple diesel powered systems, such as locomotives organized in a consist. Therefore, there will be a lead locomotive and a remote locomotive. For those locomotives in the aft position, aft mode considerations are also involved. The operating characteristics may further be based on environmental conditions (referring to conditions external to the powered system), such as, but not limited to, temperature and/or pressure.

The method illustrated in flowchart 800 in FIG. 32 further includes comparing, at 804 , the operating characteristic to a desired/specified value that satisfies the mission objective. The specified value may be determined from at least one of operating characteristics, capabilities of the diesel powered system, and/or at least one design characteristic of the diesel powered system. With regard to the design characteristics of diesel powered systems, there are various modules of the locomotive with varying design characteristics. The specified value may be determined at a remote location, such as, but not limited to, a remote monitoring station and/or a location that is part of the diesel powered system.

The assigned value may be based on the location and/or operating hours of the diesel powered system. If operating characteristics are used, the specified value is further based on emissions, speed, horsepower, friction modifiers, traction effort, environmental conditions including at least one of temperature and pressure, mission time, fuel consumption, energy storage, and/or the diesel powered system on which At least one of the conditions of the surface on which it operates. The specified value may be further determined based on the portion of the diesel powered system and/or the portion of the consist or power generating unit fueled by a number of diesels at a sub-consist level as disclosed above.

The method of flowchart 800 further includes, at 806 , adjusting the operating characteristic to correspond to the specified value with the closed loop control system operating in the feedback process to meet the mission objective. (That is, in one embodiment, to meet mission objectives, the powered system is controlled through a feedback process such that a measured operating characteristic of the powered system corresponds to a specified value.) The feedback process may include feedback principles readily known to those skilled in the art . Generally, but not considered limiting, the feedback process receives information and makes determinations based on the received information. The closed loop approach allows the method of flowchart 800 to be implemented without external interference. However, manual overrides are also provided if required due to security concerns. Adjustments to operating characteristics may be made based on environmental conditions. As disclosed above, the method of flowchart 800 may also be implemented using computer software code, where the computer software code may reside on a computer readable medium.

In one example, a closed-loop feedback process involves incrementally controlling a powered system such that an operating characteristic moves toward or near a specified value. That is, there is a difference between the operating characteristic and the specified value, and the powered system is controlled such that the difference is reduced. As should be appreciated, when it is referred to herein as an operating characteristic "corresponding to" a specified value, this may be a direct correspondence, e.g. both the operating characteristic and the specified value relate to the same type of system measurement (time, speed, fuel consumption, emissions, etc.), Or it may be an indirect correspondence, eg, the operating characteristics and specified values refer to different system measurements, but the different system measurements may be related to each other. For example, if the operating characteristic is speed and the specified value is emissions, emissions can be related to speed by a lookup table, algorithm, or the like. Once the motorized system is incrementally controlled, the operating characteristics are re-measured or otherwise re-evaluated (which provides feedback). based on remeasured operating characteristics, if the operating characteristic still does not correspond to the specified value, the motorized system is further incrementally controlled in a loop involving subsequent remeasurement (feedback) and incremental control until the operating characteristic corresponds to the specified value .

Another embodiment similar to the method of FIG. 32 relates to a method (800) for operating a diesel powered system having at least one diesel fueled power generating unit. The method includes determining an operating characteristic of a diesel powered system. The method also includes comparing the operating characteristic to a specified value that satisfies a mission objective of the mission of the maneuvering system. The method further includes controlling the powered system to adjust the operating characteristic toward a specified value. The method further includes repeating determining the operating characteristic and controlling the powered system in a closed loop feedback process until the operating characteristic corresponds to or approaches within a predetermined range of a prescribed value. In particular, it may be the case that the operating characteristic does not have to exactly match or correspond to a specified value, but only approximately within a predetermined range/threshold.

33 shows a block diagram of an exemplary system 810 for operating a diesel powered system having at least one diesel fueled power generating unit. The system 810 includes a sensor 812 configured to determine at least one operating characteristic of a diesel powered system. In the exemplary embodiment, multiple sensors 812 are provided to gather information about operating characteristics from multiple locations on the diesel powered system and/or multiple subsystems within the diesel powered system. Those skilled in the art will also recognize that sensor 812 may be an operational input device. Accordingly, the sensors 812 gather information about the operating characteristics of the diesel powered system, including, for example, related to emissions, speed, horsepower, friction modifiers, traction, environmental conditions including at least one of temperature and pressure, mission time, fuel consumption, energy Information on the condition of storage and/or surfaces on which diesel powered systems operate. Processor 814 is in communication with sensor 812 . A benchmark generation device 816 is provided and configured to identify preferred or benchmark operating characteristics (ie, specific operating characteristics of interest). The benchmark generating means 816 communicates with the processor 814 via wired and/or wireless communication systems and/or means. The reference generating device 816 may be remote from or part of a diesel powered system.

Processor 814 runs an algorithm 818 that implements a feedback process for comparing determined operating characteristics (regarding information provided by sensors 812 ) to baseline operating characteristics to determine desired operating characteristics. A converter 820 is further provided in closed loop communication with the processor 814 to achieve desired operating characteristics. Thus, the current/actual operating characteristics of the powered system are compared to the baseline operating characteristics, as determined by the sensors 812 . Based on this comparison, the powered system is controlled to achieve the desired operating characteristics through a closed-loop process, ie, the powered system is controlled such that the powered system exhibits the desired operating characteristics (or at least moves toward exhibiting the desired operating characteristics). Converter 820 may be at least one of a master controller, a remote control controller, a distributed power controller, and a train line modem. More specifically, when the diesel powered system is a locomotive system, the converter may be a remote control locomotive controller, a distributed power locomotive controller, and/or a train line modem.

As further illustrated, a second sensor 821 may be included. The second sensor is configured to measure at least one environmental condition, information that is provided to the algorithm 818 and/or the processor 814 to determine the desired operating characteristic. As disclosed above, exemplary examples of environmental conditions include, but are not limited to, temperature and pressure.

Another embodiment relates to a method for controlling the operation of a train. The method can also be applied to control other vehicles or other powered systems. According to the method, the train is controlled based on an optimized mission plan, typically to reduce fuel usage and/or reduce emissions. In order to calculate the mission plan, the following steps can be performed. First, route data and train data are received eg from a database or elsewhere. The route data includes data relating to one or more properties of the track on which the train travels along the route and data relating to at least one speed limit along the route. Train data relates to one or more characteristics of the train. The mission plan is formed on the train at any time during the train's travel along the route. A mission plan is formed at a first point along the route based on the received data and covers at least a segment of the route extending to a second point along the route further than the first point. A mission plan is formed to cover the entirety of the segment (based on or regardless of any different geographic features or other characteristics along the route of the segment for which data is available). In this case, it means that: (i) the mission plan takes into account all the different geographic features or other characteristics along the route segment for which data is available, and (ii) the mission plan is formed regardless of the particular geography of the route along the segment What are the characteristics or other characteristics. Thus, regardless of known geographic features or other route characteristics along a route segment, a mission plan is formed for that segment.

Another embodiment relates to a method for operating a vehicle. The method includes receiving route data and vehicle data at a vehicle. The route data includes data relating to one or more properties of the route along which the vehicle is traveling, and the vehicle data relates to one or more properties of the vehicle. The method further includes developing a mission plan on the vehicle at any time during travel of the vehicle along the route. A mission plan is formed at a first point along the route based on the received data and covers at least a segment of the route extending to a second point along the route further than the first point. A mission plan is formed for the entirety of the covering segment, based on or regardless of all the different geographic features or other characteristics along the route of the segment for which data is available. The method further includes controlling the vehicle according to the mission plan as the vehicle travels along the route segment. The mission plan is configured to reduce the vehicle's fuel usage and/or reduce emissions produced by the vehicle along the route segment.

After the mission plan is formed, it is determined whether the mission plan is correct for meeting at least one mission objective of the vehicle. If it is determined that the mission plan is incorrect for meeting at least one mission objective of the vehicle, the method further includes updating the received data used to form the mission plan. The mission plan is then revised to meet at least one mission objective based on the updated receipt data. After the revised mission plan, the method further includes operating the maneuvering system based on the revised mission plan.

As should be appreciated, any description herein that refers to a "trip plan" may also apply to a "mission plan", as a trip plan is a type of mission plan, ie a trip plan is a mission plan for a vehicle. This is generally true for both "trips" and "tasks", that is, a trip is a specific kind of task.

While the invention has been described with reference to various exemplary embodiments, those skilled in the art will understand that changes, deletions and/or additions may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Furthermore, unless specifically stated, any use of the terms first, second, etc. does not imply any order or importance, but rather these terms are used to distinguish one element from another.

Claims (5)

1. one kind for operating the method (300) of at least one maneuvering system with at least one power generation unit, and described method comprises:

The task scheduling that (301) are to be applied is provided, with the movement of controlling machine motor vehicle along path, described task scheduling represents that in the discharge making consumption of fuel or produced by described vehicle, at least one is relative to the movement of the mobile vehicle reduced of the vehicle planned according to other;

Receive the manual change to described task scheduling; And

The described task scheduling on described vehicle is replanned when described manual change departs from when described task scheduling exceedes scheduled volume.

2. the method for claim 1 (300), the described task scheduling wherein replanned on described vehicle comprises further implements Closed loop Control to replan described task scheduling.

3. one kind for operating the method (305) of at least one maneuvering system with at least one power generation unit, and described method comprises:

For the plan of power actuated vehicle calculation task, described task scheduling to represent when described vehicle is advanced along path as time or the movement of the described vehicle of the function of at least one in the distance in path, makes the consumption of fuel of described vehicle or at least one reduction of advancing relative to the vehicle planned according to other in the discharge that produced by described vehicle;

When described vehicle is advanced according to described task scheduling, by being arranged on the operator reception artificial fine setting to described task scheduling of the input media on described vehicle from described vehicle, described artificial fine setting comprises the order making described vehicle depart from the described movement that described task scheduling represents; And

When described artificial fine setting changes at least one characteristic (307) of the described movement of described vehicle and exceeds the preset range of described task scheduling, replan the task scheduling on described vehicle.

4. method (305) as claimed in claim 3, comprise further after a specific amount of time or when the described artificial fine setting of at least one characteristic of described task scheduling received and reach certain criteria time after at least one of them time, regulate (309) described task scheduling to correspond to the task scheduling optimized.

5. method (305) as claimed in claim 3, comprises further and runs automatically to make described vehicle the described movement automatically controlling described vehicle according to Closed loop Control according to described task scheduling.