CN119879979A - New energy heavy truck energy-saving path planning method and electronic equipment - Google Patents
New energy heavy truck energy-saving path planning method and electronic equipment Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags or using precalculated routes
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Abstract
The invention relates to the technical field of new energy, and particularly discloses a new energy heavy-duty energy-saving path planning method and electronic equipment, wherein the new energy heavy-duty energy-saving path planning method comprises the steps of constructing a layered road network based on an energy consumption model, dividing a road into a plurality of layers through a comprehensive weight formula, wherein a high-level road network comprises a low-energy-consumption main road, and the low-level road network comprises all road types; based on real-time state parameters and destination information of the vehicle, a bidirectional A algorithm is adopted to search paths in the layered road network, the bidirectional A algorithm is synchronously expanded from a starting point and a terminal point, and the searching direction is optimized based on a dynamic heuristic function. The new energy re-truck energy-saving path planning method provided by the embodiment of the invention optimizes and solves three pain points with high energy consumption, difficult energy supplement and poor real-time performance in new energy re-truck path planning through modules such as layered road network construction, dynamic weight adjustment, bidirectional A-algorithm optimization and the like, and solves the energy supplement bottleneck problem of the new energy re-truck.
Description
Technical Field
The invention relates to the technical field of new energy, in particular to a new energy heavy truck energy-saving path planning method and electronic equipment.
Background
Along with the continuous development of intelligent traffic, especially electronic maps and navigation technologies, the method provides possibility for providing real-time road network and working condition information for the new energy automobile, and also provides a new thought for dynamic planning of the energy-saving path and the economic speed of the new energy automobile and making an optimal management strategy suitable for the real-time working condition.
When a traditional path planning method is used for processing a large-scale and multi-level traffic network, the calculation complexity is high, the real-time requirement is difficult to meet, and particularly, for vehicles needing frequent energy supplement such as new energy heavy trucks, the traditional method cannot efficiently integrate road network level and energy consumption constraint. And the current new energy heavy truck is limited by battery capacity and energy supplementing facility distribution, and the traditional path planning does not fully consider the energy consumption minimization and energy supplementing requirements, so that energy deficiency or path infeasibility is easily caused.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, the invention aims to provide a new energy heavy truck energy-saving path planning method and electronic equipment so as to reduce vehicle energy consumption.
In order to achieve the above objective, an embodiment of a first aspect of the present invention provides a new energy heavy truck energy-saving path planning method, which includes:
constructing a layered road network based on an energy consumption model, wherein the layered road network divides roads into a plurality of layers through a comprehensive weight formula, a high-layer road network comprises low-energy-consumption main roads, and a low-layer road network comprises all road types;
Based on real-time state parameters and destination information of a vehicle, adopting a bidirectional A-algorithm to search paths in the layered road network, wherein the bidirectional A-algorithm is synchronously expanded from a starting point and a terminal point, and optimizing a searching direction based on a dynamic heuristic function;
In the path searching process, dynamically adjusting a searching level and a searching range according to the residual energy of the vehicle, and triggering an energy supplementing decision and re-planning a path if the residual energy is lower than a safety threshold value;
And updating the energy consumption model in real time based on the change of the load of the vehicle or the change of the external environment, dynamically correcting the predicted energy consumption of the non-driving road section, and starting the path re-planning to generate an optimized energy-saving path.
In some embodiments of the invention, the comprehensive weight formula comprises:
Wherein, AndIs a nonlinear index used for enhancing or weakening the influence of node attributes and edge attributes;
The method comprises the steps of selecting a road network with a higher priority in a path planning, and selecting a road network with a lower priority in a path planning;
the node attribute weight is represented, and specifically, the node traffic capacity and speed limit weight is represented; The weight of the road section length and the energy consumption coefficient is represented; representing distance-dependent weights; the weight of the traffic condition is represented, Representing energy consumption weights; the weight coefficients of the factors are respectively ;、、The maximum value of each factor is respectively shown.
In some embodiments of the present invention, the dynamic heuristic function of the bi-directional a-algorithm is:
()=()
Wherein, Is the product of Euclidean distance and energy consumption coefficient;、、 The dynamic weight coefficients are road network level coefficients, energy consumption coefficients and real-time traffic coefficients respectively;
for the current road network level, For the total number of layers,For the average energy consumption of the road segment,For the real-time traffic time period,Based on historical average speed or time under ideal traffic conditions.
In some embodiments of the present invention, the dynamically adjusting the search level and scope specifically includes the steps of:
let the remaining energy of the vehicle be Estimated energy consumption of a vehicle reaching a destination isThe safety threshold of the vehicle is;
If the energy remainsSearching in a high-level road network preferentially;
if the energy remains And forcedly switching to low-level road network searching.
In some embodiments of the invention, the dynamic weight coefficients、、The adjustment rules of (2) are:
When the vehicle is in the course of traveling, =(1-/) Updating;
Wherein, For the total energy of the vehicle,Is thatIs a reference value of (2);
When the load of the vehicle is increased, According to=(1+ΔW/) Updating;
Wherein, In order to change the amount of the load,Is a reference load;
When a traffic jam is detected, Pressing the button=(1+/) Updating;
Wherein, Is that,Is a real-time traffic congestion time or congestion index.
In some embodiments of the invention, the energy replenishment decision comprises the steps of:
(a) Generating a candidate energy supplementing point set { according to the current position of the vehicle };
(B) Calculating the comprehensive cost of each energy supplementing point, wherein,For the current energy consumption rate,In order to achieve the energy consumption rate after energy supplement,In order to supplement the cost of the energy-saving time,As the time-weighting factor is used,Is thatDistance to endpoint;
(c) Selecting the energy supplementing point with the minimum total cost And plan from the current location toAndHierarchical path to endpoint.
In some embodiments of the present invention, the generation manner of the candidate energy compensating point set is:
Based on residual energy And a safety thresholdScreening meetsIs provided.
In some embodiments of the present invention, in the path re-planning process, if a load of the vehicle is detected to be reduced and the original planned energy-compensating point is not necessary, the path is re-calculated and the redundant energy-compensating point is deleted.
In some embodiments of the invention, during the path execution phase, every preset time intervalRe-evaluating the remaining energy and the energy consumption of the non-driving road section, if the energy consumption is satisfied-∑Immediately triggering local path re-planning, wherein ΣThe total estimated energy consumption of the non-driving road section.
In some embodiments of the present invention, the connection rule between each level in the layered road network is that the low-level road network is connected to the middle-level road network through a key node, and the middle-level road network is connected to the high-level road network through a main road junction.
In order to achieve the above objective, an embodiment of a second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory, where the computer program, when executed by the processor, implements the above-mentioned new energy re-card energy saving path planning method.
According to the new energy heavy truck energy-saving path planning method and the electronic device, the problems of energy consumption constraint, calculation complexity and dynamic adaptability in new energy heavy truck path planning are systematically solved through mechanisms such as layered road network construction, dynamic weight adjustment and bidirectional A-algorithm optimization, a driver is helped to select the most energy-saving driving route, the whole energy level of a new energy vehicle is reduced, the energy supplementing bottleneck problem of the new energy heavy truck is solved, and the development of the new energy automobile industry is promoted.
Drawings
FIG. 1 is a flow chart of a new energy heavy truck energy-saving path planning method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a path search in a hierarchical network using a bi-directional A algorithm according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to another embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The following describes a new energy heavy truck energy-saving path planning method and electronic equipment according to an embodiment of the invention with reference to the accompanying drawings.
Fig. 1 is a flow chart of a new energy heavy truck energy-saving path planning method according to an embodiment of the invention.
As shown in fig. 1 and 2, the new energy heavy truck energy-saving path planning method comprises the following steps:
S1, constructing a layered road network based on an energy consumption model, wherein the layered road network divides roads into a plurality of layers through a comprehensive weight formula, a high-level road network comprises low-energy-consumption main roads, and a low-level road network comprises all road types, and because of the connectivity of topology, the high-level road network also comprises a small amount of other types of road sections. By dividing the road network into different levels according to energy consumption and priority, the search space can be remarkably reduced, and the calculation efficiency is improved. For example, in a global road network, only long-distance low-energy-consumption paths need to be quickly screened in a high-level road network, so that all possible complex road segments are avoided from being traversed.
As an example, road networks are divided into 3-5 levels according to road grades, a high-level road network comprises low-energy main roads such as expressways and national roads, a middle-level road network further comprises urban main roads, a low-level road network comprises all roads such as secondary main roads and branches, traffic capacity and speed limit are marked for each road node, and length, gradient and historical average vehicle flow are marked for each side.
The high-level road network ensures the high efficiency of long-distance transportation by screening low-energy-consumption main roads, and the roads have the characteristics of strong traffic capacity, low energy consumption and continuous paths, thereby being suitable for the rapid search of long-distance and global optimal paths. For example, the expressway has low gradient and high speed limit, and the energy consumption per unit distance is obviously lower than that of urban branches, while the low-level road network comprises all roads including rural roads, urban roads and the like, covers the road sections with short distance, high energy consumption and complex traffic environment, is used for fine local path planning, and ensures that a feasible path can be found under special conditions (such as energy supplementing requirement and traffic control).
S2, based on real-time state parameters and destination information of the vehicle, carrying out path search in the layered road network by adopting a bidirectional A-algorithm, synchronously expanding the bidirectional A-algorithm from a starting point and a terminal point, and optimizing the search direction based on a dynamic heuristic function. The method has the advantages that the method searches synchronously from the starting point and the end point, combines dynamic heuristic functions, can reduce redundant calculation, improves instantaneity, integrates heuristic functions into road network levels, energy consumption and traffic conditions, and can improve accuracy of searching directions.
When the existing method (such as A) searches in the full-quantity road network, the real-time requirement is difficult to meet due to the huge number of nodes and edges. The scheme can be used for implementing bidirectional searching on the layered road network and rapidly positioning the global low-energy-consumption path.
And S3, dynamically adjusting the search level and range according to the residual energy of the vehicle in the path search process, triggering an energy supplementing decision and re-planning the path if the residual energy is lower than a safety threshold, namely, prioritizing a high level when the energy is sufficient, ensuring high efficiency and energy conservation, and forcing a low level when the energy is insufficient, and searching for an energy supplementing point or a shortcut.
Conventional path planning (e.g., dijkstra, a) is mostly based on distance or time optimization, without considering the energy limitations of new energy vehicles. For example, a full loaded heavy truck may be anchored halfway through an unplanned energy replenishment point during long haul transport.
And S4, updating the energy consumption model in real time based on the load change of the vehicle or the change of the external environment, dynamically correcting the predicted energy consumption of the non-driving road section, and starting the path re-planning to generate an optimized energy-saving path.
The change of the load of the vehicle or the external environment (such as congestion and weather) triggers the update of the real-time energy consumption model, so that the path is ensured to be always optimized based on the latest state. For example, when the load is increased by 20%, the system can automatically avoid a high-gradient road section, and the energy consumption prediction error is reduced to be within 5%. In the traditional method, the technical path planning is static, and the abrupt change of the state or road condition of the vehicle cannot be dealt with.
The method systematically solves the problems of energy consumption constraint, calculation complexity and dynamic adaptability in new energy re-card path planning through a layered road network structure, a high-efficiency searching algorithm, a dynamic adjustment strategy and a real-time optimization mechanism, deeply couples an energy consumption model with a road network level, and realizes the balance of global energy conservation and local feasibility through the cooperative optimization of a bidirectional A-algorithm and dynamic weights. Compared with the prior art, the scheme has obvious improvements in energy saving effect, path reliability and calculation efficiency.
In some embodiments of the invention, the comprehensive weight formula includes:
Wherein, AndIs a nonlinear index for reinforcing or weakening the effects of node properties and edge properties (e.g., reinforcing the negative effects of high-energy road segments when θ > 1);
the road network grade evaluation value is used for dividing the road network grade, wherein the larger the value is, the lower the road network grade is, and the lower the priority is selected in the path planning;
The node attribute weight, in particular the weight of node traffic capacity and speed limit, is set As the traffic capacity of a node (which can be quantified as the maximum number of vehicles passing per unit time),Maximum traffic capacity among all nodes; for the speed limit of the node, For the highest speed limit among all nodesNodes with lower traffic capacity and lower speed limit, and corresponding nodesThe larger the value is, the more the road network grade evaluation value is improved, which means that the road network grade is probably lower;
The weight representing the road length and the energy consumption coefficient, wherein the length of the edge is L and the weight of the edge determined based on the energy consumption, time and other factors Affecting edge attribute weights. Is provided withFor the maximum length in all of the sides,Larger indicates that the "cost" of the edge is higher, then. The longer the edge the higher the "cost",The larger the influence on the road network grade evaluation value is, the larger the influence is;
Representing distance-related weights, considering the influence of the distance of the current road segment from the start point or the end point on the road network level. Let D be the distance from the midpoint of the current road segment to the destination (the distance to the origin may also be selected according to the actual situation or a combination of both), The maximum distance from the destination for all road segments in the road network. Then. The further away from the destination the further away,The larger the road network grade evaluation value is, the more the road network grade evaluation value is improved;
representing the weight of traffic condition by calculating the traffic flow data in real time As the actual traffic flow of the road segment,The traffic capacity is designed for the road section. Then. The greater the traffic flow, the closer to or beyond the road traffic capacity,The larger the road traffic jam is reflected, the road network grade evaluation value is increased, the road network grade is possibly lowered,The maximum traffic flow or the maximum traffic capacity of all road sections in the road network;
representing energy consumption weight, calculating load M of combined vehicle and unit distance energy consumption E of road section, and setting For the maximum load of the vehicle,Is the maximum energy consumption per unit distance under specific conditions. Then. The larger the load is, the higher the energy consumption per unit distance is,The larger the road network grade evaluation value is, the more obvious the road network grade evaluation value is lifted, which means that the grade of the road network is possibly reduced due to energy consumption factors;
、、 the maximum values of the factors are respectively used for normalization processing and eliminating dimension differences;
the weight coefficients of the factors are respectively The coefficients can be set according to the importance degree of each factor in the actual application scene, and updated by real-time data (such as current traffic flow and vehicle residual energy)。
In the technical scheme, the node attribute is used for evaluating the traffic capacity and efficiency of key nodes (such as intersections, high-speed entrances and exits, traffic junctions and the like) in the road network and mainly comprises the following parameters of traffic capacityAnd speed limitWhile the present method tends to avoid high levelsThe node of the system reduces the increase of energy consumption caused by traffic bottleneck or low-speed limit;
the edge attribute is used for describing the physical characteristics and energy consumption characteristics of a road segment (road segment), and mainly comprises the following parameters of a road segment length L and an energy consumption coefficient The method preferably selectsThe road section with small value reduces the whole energy consumption;
in the comprehensive weight formula, θ and As a nonlinear index, the node attribute and the edge attribute weights are respectively acted, and the design intent is as follows:
Theta node attribute index:
enhancement effect if theta >1, the weight of the node property is amplified (e.g., when theta = 1.2, >) The avoidance of low traffic capacity nodes is further enhanced;
Weakening effects if theta <1, the weight of the node property is weakened (e.g., when theta = 0.8, <) The influence of the road network classification is reduced;
For example, in urban congestion areas, θ=1.5 is set, =0.8, Strengthening avoidance of low traffic node (θ1), and weakening influence of edge attribute< 1), Allows to choose a path that bypasses shorter but with a slightly higher energy consumption.
Edge attribute index:
Enhancement of the effect if >1, The weight of the edge property is amplified (e.g.When the number of the samples is =1.5,>) The long-distance or high-energy-consumption road sections are avoided preferentially;
Attenuation of influence if <1, The weight of the edge attribute is weakened (e.g.When the value of the ratio is =0.7,<) Allowing the system to more flexibly select a part of high-energy-consumption but short-distance paths;
For example, in the long-distance high-speed transportation process, θ=0.9 is set, =1.3, Weakening the influence of node properties (θ < 1), but strengthening avoidance of high-energy road segments1), A highway with long distance but low energy consumption is preferentially selected.
The method can flexibly balance the passing efficiency and the energy consumption cost of the path under different scenes by dynamically adjusting the weights of the node and the edge attribute through the nonlinear index, and realize the global energy-saving target, namely theta and thetaThe introduction of the model enhances the adaptability of the model to different road network characteristics (such as the urban dense road network vs inter-urban sparse road network) and avoids static parameter setting of one cut.
As one example, a high-speed road segment: The number of times is =2000/hour, =120 Km/h, l=50 km, unit energy consumption e=0.8 kWh/km;
Certain rural road section: the number of times per hour is =500, =40 Km/h, l=10 km, unit energy consumption e=1.5 kWh/km;
then, the comprehensive weight formula is adopted for calculation, so that =0.2;θ=1.2,=0.8 (Enhanced node attribute effect, weakened edge attribute effect),=300 Km (road network maximum distance),=2000 Vehicles/hour (maximum traffic flow),=2.0 KWh/km (maximum energy consumption under full load extreme road conditions);
the method comprises the following steps of:
=1.42,=0.05,=0.65;
Conclusion: =0.65, belonging to a low-level road network (threshold setting: 0.5 is low level).
In the path planning process, the method switches from a high level to a low level road network step by step (or searches in parallel), and has the following core advantages:
The low-energy-consumption main road is quickly locked through the high-level road network, the low-level road network is optimized in supplementary details, if the low-energy-consumption main road is directly searched in the full low-level road network, the number of nodes and edges is huge, the calculation complexity is high, the low-level road network comprises a large number of high-energy-consumption road sections (such as urban roads which are frequently started and stopped), the direct search can lead to a locally optimal path with higher global energy consumption, and the layered search greatly compresses the search space to meet the real-time requirement. In summary, when the energy is sufficient, global optimization is performed to select a long-distance low-energy-consumption path, and the energy is insufficient, local optimization is performed to select a short-distance feasible path, even if the energy consumption is high, so as to complete the corresponding conveying task.
In some embodiments of the present invention, the dynamic heuristic function of the bi-directional a-algorithm is:
()=()
Wherein, Is the product of Euclidean distance and energy consumption coefficient;、、 The dynamic weight coefficients are respectively road network level coefficients (high level values are small, long-distance searching is encouraged), energy consumption coefficients (penalty values of high-energy road sections are increased) and real-time traffic coefficients (penalty values of congestion road sections are increased);
for the current road network level, For the total number of layers,For the average energy consumption of the road segment,For the real-time traffic time period,Based on historical average speed or time under ideal traffic conditions.
In the path searching process, the heuristic value is reduced when searching the high-level road network, the algorithm tends to search for a long distance, and the heuristic value is increased when searching the low-level road network, and the algorithm refines the local search.
In some embodiments of the present invention, dynamically adjusting the search level and scope specifically includes the steps of:
let the remaining energy of the vehicle be Estimated energy consumption of a vehicle reaching a destination isThe safety threshold of the vehicle is;
If the energy remainsSearching in a high-level road network preferentially;
if the energy remains And forcedly switching to low-level road network searching.
As an example, when(E.g., 200kWh for residual energy, 150kWh for estimated energy consumption, 50kWh for safety threshold):
At this time, searching is preferentially performed on the high-level road network, the weight of the heuristic function is reduced, long-distance jump is encouraged, and when the residual energy is insufficient (for example, the residual energy is 120kWh and the estimated energy consumption is 150 kWh), the method is forced to switch to the low-level road network, so that the local searching precision is increased, and energy supplementing points are conveniently found to supplement energy.
In some embodiments of the invention, dynamic weighting coefficients、、The adjustment rules of (2) are:
When the vehicle is in the course of traveling, =(1-/);Adjusting the search priorities of the high-level and low-level road networks,The smaller the value, the more prone the long-distance search of the high-level road network, whereas, when the remaining energy is sufficient,The increase allows for refined searches in low-level road networks;
Wherein, For the total energy of the vehicle,Is thatIs a reference value of (2);
When the load of the vehicle is increased, According to=(1+ΔW/) Updating; Reflecting the influence of load change on energy consumption, and increasing the load to strengthen the avoidance of the high-energy-consumption road section;
Wherein, In order to change the amount of the load,Is a reference load;
When a traffic jam is detected, Pressing the button=(1+/) Updating; reflecting the influence of traffic congestion on the route selection, and increasing the traffic congestion to preferentially select a smooth road section;
Wherein, Is that,Is real-time traffic congestion time (unit: minutes) or congestion index (e.g., normalized value of 0-1).
Dynamic weight coefficient、、The following table is summarized as the parameter definition of (a):
In some embodiments of the present invention, if the predicted energy consumption of the remaining energy minus the non-driven road segment falls below a set safe energy threshold, the system will initiate the path re-planning process and make intelligent energy replenishment decisions. On the basis, the system can re-optimize the path according to the latest condition so as to ensure that the vehicle can reach the final destination efficiently and safely on the premise of meeting the energy demand, and the energy supplementing decision comprises the following steps:
(a) Generating a candidate energy supplementing point set { according to the current position of the vehicle };
(B) Calculating the comprehensive cost of each energy supplementing point, wherein,For the current energy consumption rate,In order to achieve the energy consumption rate after energy supplement,In order to supplement the cost of the energy-saving time,As the time-weighting factor is used,Is thatDistance to endpoint;
(c) Selecting the energy supplementing point with the minimum total cost And plan from the current location toAndHierarchical path to endpoint.
As an example, the energy supplementing point A is the vehicle residual energy 120kWh, the safety threshold value 50kWh, the current energy consumption rate 2kWh/km, the distance 20km, the energy consumption rate after energy supplementing 1.8kWh/km and the energy supplementing time 15 minutes (mu=0.5), wherein
≈45
The energy supplementing point B is at a distance of 30km, the energy consumption rate after energy supplementing is 1.6kWh/km, and the energy supplementing time is 20 minutes, at the moment
=50
Then selecting the energy supplementing point A, planning the path from the current position to the A to the end point.
As an example, the energy-supplementing time cost [ ]) The method is influenced by dynamic changes of the type of the charging pile and the queuing time, and the traditional static model has large prediction error, so that the path planning is inaccurate, and therefore, the real-time data (such as idle state, queuing length and charging power) of the charging pile can be accessed, and the charging pile can be updated dynamically. For example:
=+
Wherein, The number of vehicles in line is the current number; Average charging time for a bicycle; the number of the charging piles.
In addition, the Q-learning model can be trained, the optimal energy supplementing period is predicted according to the historical charging station congestion mode, and the waiting time is reduced.
In some embodiments of the present invention, the candidate set of energy points is generated by:
Based on residual energy And a safety thresholdScreening meetsIs provided.
As an example, a vehicle remaining energy of 120kWh, a safety threshold of 50kWh, a current energy consumption rate of 2kWh/km, may be obtained=35 Km, i.e. the energy point within 35km is screened.
It should be noted that in remote areas or during peak traffic, an insufficient number of energy replenishment points meeting the energy constraint may result in a planning failure. At the moment, the mobile energy supplementing resource scheduling can be performed, namely the mobile charging vehicle is in butt joint with a mobile charging vehicle service platform, and when no fixed energy supplementing point exists, the mobile charging vehicle is scheduled to a preset position.
Further refinement of the above solution, the re-planning process of the path is triggered when the key parameters (such as the load weight of the vehicle) change during the running of the vehicle. The mechanism ensures that path selection is always optimized based on up-to-date operating conditions and constraints, thereby maintaining timeliness, effectiveness, and optimality of the path planning scheme. Specifically, when the vehicle load increases resulting in an increase in energy demand, the system will evaluate whether a replenishment or adjustment of a replenishment strategy is required, whereas if the vehicle load decreases resulting in a decrease in energy consumption, the previously planned replenishment may no longer be necessary. At this time, the system will recalculate the optimal energy consumption route under the road section for the changed condition to ensure that the path planning is both efficient and energy-saving. By implementing the dynamic adjustment mechanism, the system can continuously provide an optimized path planning solution in a complex and changeable environment, and adapt to energy demand change under different conditions.
The method can be summarized in that in the path re-planning process, if the load of the vehicle is detected to be reduced and the original planned energy supplementing point is not necessary, the path is recalculated and redundant energy supplementing points are deleted. For example, if the load of the vehicle is reduced (e.g. from 30 tons to 20 tons), the original energy-supplementing point A is not necessary, the energy consumption is recalculated, the energy-supplementing point A is deleted, and the low-level road network path is directly planned.
As an example, after the load is relieved, if the following road segment is suddenly jammed or the energy consumption rises, the energy supplementing point is deleted to possibly cause energy shortage, so that the energy supplementing point can be classified according to the risk of the following road segment by adopting a dynamic priority marking method in the running process of the vehicle for a long time and a month, and more standby points are reserved for the high-risk road segment.
In some embodiments of the invention, during the path execution phase, every preset time intervalRe-evaluating the remaining energy and the energy consumption of the non-driving road section, if the energy consumption is satisfied-∑Immediately triggering local path re-planning, wherein ΣThe total estimated energy consumption of the non-driving road section.
It should be noted that since frequent triggering of local re-planning results in frequent switching of paths, possibly affecting driving stability, sliding window optimization may be employed, only for the most recentAnd re-planning the non-driving road sections in time, so that the global influence is reduced.
In some embodiments of the present invention, the connection rules between the levels in the layered road network are that the low-level road network is connected to the middle-level road network through key nodes (such as arterial road entrances), and the middle-level road network is connected to the high-level road network through arterial road hinges (such as expressway ramp).
While the key nodes may become congestion bottlenecks, resulting in path disruption, virtual connection nodes are temporarily created during traffic congestion or construction, allowing for cross-level hops. For example, the road condition is monitored in real time by the unmanned aerial vehicle, and a temporary path is dynamically generated. In addition, the weight of the connection node can be adjusted according to the real-time traffic flow (such as in the comprehensive weight formula) The priority is lowered when congestion occurs.
As one example, if a trunk entrance is congested, the algorithm automatically selects the suboptimal connection node.
In some embodiments of the present invention, a specific way of updating the energy consumption model in real time comprises the steps of:
step 1, real-time load monitoring and rolling resistance dynamic adjustment
The vehicle load data are collected in real time through the vehicle-mounted sensor, when the load change exceeds a threshold value (such as delta M is more than 10 percent of rated load), the energy consumption model is triggered to update, rolling resistance is recalculated according to the new load, and the energy consumption parameter of unit distance is adjusted.
Step 2, multi-sensor data fusion and environmental parameter correction
Fusing sensor data such as GPS, IMU and the like, and acquiring environmental parameters such as road gradient, wind speed and the like in real time;
and dynamically correcting the energy consumption model according to environmental parameters (such as gradient and upwind), and adjusting the energy consumption per unit distance.
Step 3, machine learning driven model parameter optimization
Automatically optimizing energy consumption model parameters (such as rolling resistance coefficient and air resistance coefficient) by utilizing a historical path database and a reinforcement learning algorithm;
And the long-term prediction accuracy is improved through a continuous iteration model of a reward function (such as the deviation between actual energy consumption and predicted energy consumption).
Step 4, dynamic weight adjustment and path planning linkage
When the load is increased, the energy consumption weight coefficient is dynamically increased (such as) Guiding path planning to avoid a high-energy-consumption road section;
when the external environment changes (such as congestion), the search weight is adjusted, and the low-energy consumption or low-congestion path is selected preferentially.
Step5, real-time data-driven local path re-planning
Monitoring the residual energy and the energy consumption of the non-driving road section in real time when-∑Triggering local re-planning, dynamically deleting redundant energy supplementing points, re-planning paths and ensuring energy safety.
The system breaks through the limitation of a single data source in the prior art by integrating the vehicle state, the road attribute, the environmental factors and the historical data, and then realizes the continuous evolution of the energy consumption model, the local re-planning and the incremental updating by real-time feedback and machine learning, so that the real-time performance is ensured, and the prior art relies on global computing, has slow response, and deeply couples the energy consumption model and the path planning, so that the dynamic balance of energy consumption, time and safety is realized. The method has the remarkable advantages in the field of new energy heavy truck energy consumption management, and provides a key technical support for realizing full-scene energy conservation.
Corresponding to the embodiment, the invention also provides electronic equipment.
Referring to fig. 3, an electronic device 200 according to the present invention includes a processor 201 and a memory 203. Wherein the processor 201 is coupled to the memory 203, such as via a bus 202. Optionally, the electronic device 200 may also include a transceiver 204. It should be noted that, in practical applications, the transceiver 204 is not limited to one, and the structure of the electronic device 200 is not limited to the embodiment of the present invention.
The Processor 201 may be a CPU (Central Processing Unit ), general purpose Processor, DSP (DIGITAL SIGNAL Processor ), ASIC (Application SPECIFIC INTEGRATED Circuit), FPGA (Field Programmable GATE ARRAY ) or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various exemplary logical blocks, modules, and circuits described in connection with the present disclosure. The processor 201 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Bus 202 may include a path to transfer information between the aforementioned components. Bus 202 may be a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture ) bus, or the like. The bus 202 may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 3, but not only one bus or one type of bus.
The memory 203 is used for storing a computer program corresponding to the new energy re-card energy saving path planning method according to the above embodiment of the present invention, and the computer program is controlled by the processor 201 to be executed. The processor 201 is arranged to execute computer programs stored in the memory 203 for implementing what is shown in the foregoing method embodiments.
Among them, the electronic device 200 includes, but is not limited to, a mobile terminal such as a notebook computer, a PAD (tablet computer), etc., and a fixed terminal such as a desktop computer, etc. The electronic device 200 shown in fig. 3 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the invention.
The electronic device 200 of the embodiment of the invention acquires the vehicle state (load and residual energy) and road condition data (traffic flow and gradient) in real time, updates the layered road network weight once every 5 seconds, triggers the bidirectional a algorithm to search again, starts the energy supplementing decision when the residual energy is lower than the threshold value, and generates a new path within 10 seconds. Through mechanisms such as layered road network construction, dynamic weight adjustment, bidirectional A algorithm optimization and the like, the problems of energy consumption constraint, calculation complexity and dynamic adaptability in new energy heavy truck path planning are systematically solved, a driver is helped to select the most energy-saving driving route, and the whole energy consumption level of a new energy vehicle is reduced.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered as a ordered listing of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include an electrical connection (an electronic device) having one or more wires, a portable computer diskette (a magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (11)
1. The new energy heavy truck energy-saving path planning method is characterized by comprising the following steps of:
constructing a layered road network based on an energy consumption model, wherein the layered road network divides roads into a plurality of layers through a comprehensive weight formula, a high-layer road network comprises low-energy-consumption main roads, and a low-layer road network comprises all road types;
Based on real-time state parameters and destination information of a vehicle, adopting a bidirectional A-algorithm to search paths in the layered road network, wherein the bidirectional A-algorithm is synchronously expanded from a starting point and a terminal point, and optimizing a searching direction based on a dynamic heuristic function;
In the path searching process, dynamically adjusting a searching level and a searching range according to the residual energy of the vehicle, and triggering an energy supplementing decision and re-planning a path if the residual energy is lower than a safety threshold value;
And updating the energy consumption model in real time based on the change of the load of the vehicle or the change of the external environment, dynamically correcting the predicted energy consumption of the non-driving road section, and starting the path re-planning to generate an optimized energy-saving path.
2. The new energy heavy truck energy-saving path planning method according to claim 1, wherein the comprehensive weight formula comprises:
Wherein, AndIs a nonlinear index used for enhancing or weakening the influence of node attributes and edge attributes;
The method comprises the steps of selecting a road network with a higher priority in a path planning, and selecting a road network with a lower priority in a path planning;
the node attribute weight is represented, and specifically, the node traffic capacity and speed limit weight is represented; The weight of the road section length and the energy consumption coefficient is represented; representing distance-dependent weights; the weight of the traffic condition is represented, Representing energy consumption weights; the weight coefficients of the factors are respectively ;、、The maximum value of each factor is respectively shown.
3. The new energy re-truck energy-saving path planning method according to claim 1, wherein the dynamic heuristic function of the bidirectional a-algorithm is:
()=()
Wherein, Is the product of Euclidean distance and energy consumption coefficient;、、 The dynamic weight coefficients are road network level coefficients, energy consumption coefficients and real-time traffic coefficients respectively;
for the current road network level, For the total number of layers,For the average energy consumption of the road segment,For the real-time traffic time period,Based on historical average speed or time under ideal traffic conditions.
4. The new energy heavy truck energy-saving path planning method according to claim 1, wherein the dynamic adjustment of the search level and range specifically comprises the following steps:
let the remaining energy of the vehicle be Estimated energy consumption of a vehicle reaching a destination isThe safety threshold of the vehicle is;
If the energy remainsSearching in a high-level road network preferentially;
if the energy remains And forcedly switching to low-level road network searching.
5. The new energy heavy truck energy-saving path planning method according to claim 3, wherein the dynamic weight coefficient、、The adjustment rules of (2) are:
When the vehicle is in the course of traveling, =(1-/) Updating;
Wherein, For the total energy of the vehicle,Is thatIs a reference value of (2);
When the load of the vehicle is increased, According to=(1+ΔW/) Updating;
Wherein, In order to change the amount of the load,Is a reference load;
When a traffic jam is detected, Pressing the button=(1+/) Updating;
Wherein, Is that,Is a real-time traffic congestion time or congestion index.
6. The new energy heavy truck energy-saving path planning method according to claim 1, wherein the energy supplementing decision comprises the following steps:
(a) Generating a candidate energy supplementing point set { according to the current position of the vehicle };
(B) Calculating the comprehensive cost of each energy supplementing point;
Wherein, For the current energy consumption rate,In order to achieve the energy consumption rate after energy supplement,In order to supplement the cost of the energy-saving time,As the time-weighting factor is used,Is thatDistance to endpoint;
(c) Selecting the energy supplementing point with the minimum total cost And plan from the current location toAndHierarchical path to endpoint.
7. The new energy heavy truck energy-saving path planning method according to claim 6, wherein the generation mode of the candidate energy compensating point set is as follows:
Based on residual energy And a safety thresholdScreening meetsIs provided.
8. The new energy re-truck energy-saving path planning method according to claim 1, wherein in the path re-planning process, if the load of the vehicle is detected to be reduced and the original planned energy-supplementing point is not necessary, the path is recalculated and redundant energy-supplementing points are deleted.
9. The new energy heavy truck energy-saving path planning method according to claim 1, characterized in that in the path execution stage, every preset time intervalRe-evaluating the remaining energy and the energy consumption of the non-driving road section, if the energy consumption is satisfied-∑Immediately triggering local path re-planning, wherein ΣThe total estimated energy consumption of the non-driving road section.
10. The new energy re-truck energy-saving path planning method according to claim 1, wherein the connection rule between each level in the layered road network is that a low-level road network is connected with a middle-level road network through a key node, and the middle-level road network is connected with a high-level road network through a main road junction.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory, which when executed by the processor, implements the new energy re-card energy saving path planning method according to any one of claims 1-10.
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