TWI504525B - A design method for managing the power of a range-extended electric vehicle - Google Patents

Description

Method for designing an energy management strategy for an extended-range electric vehicle

A method, strategy and application for designing an energy management strategy for an extended-range electric vehicle, particularly for protecting a battery of an electric vehicle.

An extended-range electric vehicle, generally an electric vehicle having a range extender, wherein the range extender refers to all sources of energy that can extend the travel distance of the electric vehicle, such as an engine, a generator, and a controller; Vehicles generally refer to all traffic vehicles that use batteries to drive electric motors to travel, such as electric vehicles and electric vehicles.

After the extended electric vehicle travels for a distance, its own battery will be consumed until it can not provide enough power to drive the electric motor to rotate. At this time, the range extender installed in the vehicle controls the engine to be turned on via the controller, and the generator generates electricity. By giving the battery power to drive the electric motor to rotate or to charge the battery, the electric vehicle can continue to travel. Wherein, the controller controls whether the engine is turned on or off, whether the electric motor is fully supplied after being turned on, whether the electric motor power is supplied together with the battery after being turned on, or whether the battery is charged after being turned on, and the parameters are increased by each manufacturing. The engineer in the manufacturer of the program electric vehicle is calibrated. The guidelines for engineers to adjust are based on their own work experience and driving experience, or use the trial error method, and refer to the fuel consumption and pollution regulations, as long as they can meet the fuel consumption and pollution regulations and experience, you can complete a control strategy.

Manual adjustment is not only time-consuming but not economical and different The incremental motorized vehicle component specifications (eg battery size, motor power, generator power, engine power, etc.) are not the same, and the various driving conditions are different. A single control strategy may not be widely used. Various extended-range electric vehicles or driving conditions.

In addition, please refer to the eighth figure. The control strategy of the traditional extended-range electric vehicle is mainly the thermostat control strategy. The main reason is that the extended-range electric vehicle is driven before the pure electric mode. When the residual power is consumed to a manually adjusted threshold (about 0.22 remaining battery residual power), the range extender is activated to generate electricity. The range extender is only fixed in certain situations according to the driver's power demand (for example) : The highest efficiency or the largest power output). When the residual battery power rises to another threshold value (about 0.27 of the remaining battery capacity), the range extender is turned off, so from the eighth figure Knowing the technical management curve will show that the battery residual capacity floats within a range. If the driver's power demand is large during the start-up of the range extender, the energy management strategy will enable the range extender to operate in the mode with the highest power output to ensure that the battery residual capacity does not continue to drop. Because the range extender engine is less efficient, the engine fuel consumption is also higher.

Furthermore, as shown in the control strategy of Figure 8, since the extended-range electric vehicle is initially driven in pure electric mode, all power is supplied by the battery, so the battery discharge current is large; and since the range extender is started The power is generated in a fixed operation. If the driver's power demand is small, the excess generated power of the range extender will be recharged to the battery, which will result in a larger average charging current of the battery. Therefore, such a control strategy will easily cause damage to the battery due to excessive charging and discharging current. Among them, the battery used in the electric vehicle is expensive, and if it is often damaged, it will be disadvantageous to the consumer, and is not conducive to the manufacturer of the electric vehicle.

Finally, the conventional electric vehicle control strategy does not take into account the impact of the vibration noise of the range engine operation on the driver. Wherein, when the driver of the electric vehicle operates the electric vehicle, since the battery of the electric vehicle is close to the state of no power, the range extender starts to help the battery Power supply. At this time, if the driver waits for a red light or other vehicle to keep the vehicle at a low speed, the driver can clearly hear the sound of the range extender, which is a noisy noise for the driver.

The object of the present invention is to solve the problems that are conventionally generated, including: the method of manual adjustment is deeply influenced by personal experience, there is no certain rule, the method cannot be applied to various vehicle models, and its control strategy makes the engine consume too much under certain circumstances. The energy, the method of causing the battery of the electric vehicle to be easily damaged, and causing annoying noise, utilizes an energy management strategy for designing an extended-range electric vehicle to generate a strategy for improving all of the shortcomings of the prior art, including the following steps :

Step 1: Select a plurality of vehicle models according to the driving condition of the electric vehicle, and drive the electric vehicle to drive in the vehicle mode.

Step 2: Using a dynamic programming method to determine the optimal energy management plan for the electric vehicle to travel in the vehicle mode.

Step 3: According to the optimized energy management plan, the suboptimal energy management plan of the electric vehicle under the vehicle mode is separately obtained.

Step 4: Identify the actual driving condition of the electric vehicle by means of a vehicle type identification means, and determine that the actual driving condition of the electric vehicle is closest to the driving condition in which the electric vehicle is driven.

Step five: Controlling the range extender of the electric vehicle by using the suboptimal energy management plan of the electric vehicle that is closest to the actual driving condition of the electric vehicle, and repeating steps 4 and Fives.

Wherein, after the electric vehicle is driven in the vehicle mode, respectively, a power demand average value and a power demand standard difference value are respectively calculated, and the vehicle type identification means is based on the power demand average value and the power demand. The standard deviation determines whether the actual running condition of the electric vehicle is closest to the driving condition in which the electric vehicle is traveling.

Wherein, in a preferred implementation method, when the dynamic energy planning method is used to obtain an optimized energy management plan, a condition for limiting the charging and discharging current of the battery of the electric vehicle is additionally added.

In another preferred implementation method, when the dynamic energy planning method is used to obtain an optimized energy management plan, and when the speed of the electric vehicle is lower than the first speed limit threshold, the range extender cannot be turned on. limitation factor.

Wherein, in a preferred implementation method, the results of the optimized energy management plan are utilized, and the battery is charged and discharged according to a power distribution ratio, a driver power demand, and a battery residual capacity via the electric vehicle. The sub-optimized energy management plan containing a first threshold, a second threshold, a third threshold, and a fourth threshold is summarized.

In a preferred implementation method, the vehicle type identification means includes a register and a processor.

Wherein, in a preferred implementation method, a certain period of time elapses when the last two steps are repeatedly performed.

A strategy is generated via steps 1 through 5 above.

A rule-based multi-mode switching energy management strategy, via the above strategy produce.

An electric vehicle having a range extender, wherein the range extender is provided by a method using steps 1 to 5, or a controller based on a regular multi-mode switching energy management strategy control.

The strategy of the present invention utilizes a dynamic programming method to summarize the management strategy generated by the optimized energy management plan, so that the manual adjustment method is more regular and systematic and saves the range engine. Fuel consumption. In addition, by selecting a plurality of vehicle models and allowing the controller to change the different vehicle modes at any time, the electric vehicle of the present invention can be driven with better control strategies under various road conditions. Furthermore, by adding restrictions on the magnitude of the charge and discharge current of the battery, the present invention can protect the battery more, thereby saving the cost of the electric vehicle manufacturer. Finally, by adding a limit when the speed of the electric vehicle is lower than the first speed limit threshold, the range extender cannot be opened, so that the driver will not be disturbed by the operating noise of the extended program engine when the electric vehicle is idling.

A‧‧‧ 行型型A

B‧‧‧ 行型型B

C‧‧‧Cable model C

D‧‧‧Line Model D

E‧‧‧ vehicle type E

F‧‧‧Foot model F

G‧‧‧Wing model G

H‧‧‧ 行型型H

I‧‧‧ 行型型I

J‧‧‧ 行车型J

V1‧‧‧ first threshold

V2‧‧‧ second threshold

V3‧‧‧ third threshold

V4‧‧‧ fourth threshold

M‧‧‧ vehicle type identification means

The first picture shows the list of the average power demand and the power demand standard deviation of the target vehicle driving in the vehicle type A~G.

The second picture shows the engine opening point and closing point distribution diagram under the condition that the battery is discharged according to the battery residual power and the driver power demand after the target vehicle is driven in the vehicle mode A.

The third picture shows the engine opening point distribution map under the condition that the battery is charged according to the two factors of battery residual power and driver power demand.

The fourth picture shows the battery residual power after dynamic planning based on the target vehicle driving in the vehicle mode A. The engine opening point distribution map of the three variables of quantity, driver power demand and power distribution ratio.

The fifth picture shows the engine opening point distribution map considering the power distribution ratio and the driver power demand after the target vehicle is driven in the vehicle mode A.

The sixth picture shows the list of the first threshold, the second threshold, the third threshold and the fourth threshold of the optimized energy management plan after the target vehicle is driven by the vehicle model A~G. .

The seventh picture shows the flow chart of the vehicle identification method.

The eighth figure is a graph drawn by optimization planning management, sub-optimization based on rule management, and conventional technology management.

The ninth figure is a list of the costs of the invention, the dynamic planning, and the conventional technology in the vehicle type H~J.

The embodiments of the present invention will be described in more detail below with reference to the drawings and the <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt;

The method of the present invention for designing an energy management strategy for an extended-range electric vehicle can produce a strategy that can be applied to an electric vehicle, wherein the electric vehicle has a range extender as mentioned in the prior art. And the range extender has an engine and a generator. The inventor first uses a target vehicle to establish a target vehicle simulation model. After the model is verified and the result is quite close to that of the actual vehicle, the simulation model is used to verify the experiment of the present invention, and finally the design steps of the present invention are obtained. The following is a description of the steps involved in the present invention with the accompanying drawings, wherein the target vehicle is referred to as an electric vehicle having a range extender as in the prior art.

Step 1: Select a plurality of vehicle models according to the driving condition of the electric vehicle, and drive the electric vehicle to drive in the vehicle mode.

First, according to the driving condition of the target car, a plurality of vehicle models are selected. Among them, the state of the vehicle may be formulated by a national or local administrative organization in order to meet the vehicle emission standards or the driving pattern of the vehicle. The selected vehicle type must be able to cover various operating conditions of the target vehicle (eg high speed, low speed, rapid acceleration, constant speed, deceleration, etc.). In order to be able to express the characteristics of various vehicle models, the present invention calculates the average power demand ( P _{dem, mean} ) and the power demand standard deviation ( P _{dem, std} ) of the target vehicle when driving in various vehicle _modes , wherein The electric vehicle is driven in the steps of the vehicle type, and can be actually driven by referring to the vehicle type or by analog means, as shown in the first figure.

Among them, the vehicle model A is the model state of the European Union; the vehicle model B is the model of the vehicle model established by the United States; the vehicle model C is the model of the vehicle model established by the United States; The vehicle type is the model of the Taiwanese government in response to the road conditions in the southern suburbs of Taiwan; the vehicle model F is the model of the Taiwanese government in response to the driving conditions of the Taipei metropolitan area; The state of the vehicle set by the government in response to the driving conditions of the bus. Due to these vehicle models, their respective mileages are not the same, and the present invention assumes that the target extended-range electric vehicle has a mileage of about 105 km, so the vehicle mode is repeated multiple times (as shown by the * in the figure). The number of times, the total mileage of each line model is about 105km.

In addition, due to the average power demand of each vehicle type, it can be used to indicate the load characteristics of the vehicle model. From the first figure, it can be found that these vehicle models contain high driver power demand average (H), The average driver power demand (M) and the low driver power demand average (L), where the driver's power demand standard deviation for each representative vehicle type can be used to indicate the load change of the vehicle model. To the extent, it can be seen from the first figure that these representative vehicle models include high driver power demand standard deviation (H) and low driver power demand standard deviation (L). Taking the model state G as an example, it can be found that its power demand is low, but the standard deviation of power demand is very high, which means that when the target vehicle is driven according to the standard G of the vehicle model, it does not need much power, but because it is like a bus. Stop and go, so the power does not have a large amplitude between the standard values. And then the standard deviation is high.

It should be noted that the target vehicle is an electric vehicle with a range extender in this embodiment, and in another embodiment, it can also be an electric motor vehicle with a range extender. However, the driving state must comply with various operating conditions of the locomotive ( For example: high speed, low speed, rapid acceleration, constant speed, deceleration, etc.) to pick. For example, if a line of vehicles requires more than 180 km/h, it is not suitable for use in locomotives.

Step 2: Using a dynamic programming method to determine the optimal energy management plan for the electric vehicle to travel in the vehicle mode.

The dynamic programming method can be used for nonlinear systems. Under various constraints, it is easier to find the optimal control strategy planning method. Therefore, the present invention utilizes a dynamic programming method to determine an optimized energy management plan for a target vehicle traveling under various vehicle modes.

Firstly, a reverse time simulation model is established for the target vehicle. In the modeling process, the model is simplified into only one state variable, that is, the state of charge (SOC), and the model input is defined as the range extender of the target vehicle. Output power ( P _{gs_elec} ). Depending on the vehicle model, the pure electric driving range of the target vehicle is only about 50km, and the target driving range assumed by the present invention is about 105km, which means that the range extender must operate to provide power during the driving of the target vehicle. In addition, according to the conventional energy management control strategy, when the driving distance is known, the residual power of the battery at the end of the driving distance just reaches the preset lower limit, so the present invention expresses the dynamic programming as the two ends are known. For the optimization problem, the two end points are the state variables of the starting point of the running distance and the state variables of the end of the running distance (that is, the SOC of the starting point of the running distance and the SOC of the end of the running distance).

The purpose of using the dynamic programming method is to find out the optimal control input at each time point when the target vehicle is driving in various vehicle _modes , that is, how much power ( P _{gs_elec} ) the range extender needs to output at each time point. Thereby, the value calculated by the cost function defined by equation (1) is minimized.

Where N is the total running time of the representative vehicle type; k is the time step; L _inst is the instantaneous cost function, which is defined by the present invention as the range engine oil consumption; x is the state variable, which is defined by the present invention as the battery residual capacity; U is the control input, according to the present invention, which is defined as the output power range extender _(P gs_elec). In addition, in the solution process of dynamic programming, the constraints of the operation of each component must be considered at the same time, as follows: Where T _m is the torque of the electric motor; T _e is the torque of the range extender engine; T _gs is the torque of the range extender; ω _e is the engine speed of the range extender; ω _gs is the operating speed of the range extender.

In a preferred embodiment of the present invention, in order to enable the optimized energy management plan to be solved to have the ability to protect the battery, the charge and discharge current of the battery used for traveling the target vehicle is expressed as a limiting condition for reducing Excessive battery charge and discharge current damage to battery life. After adding this restriction condition, the target vehicle must be operated during the driving process to provide a power-assisted battery-driven electric motor to drive the target vehicle to ensure that the discharge current of the battery can not be too large within the limit. The output current of the processor must also be controlled to avoid excessive current exceeding the limit when the range extender charges the battery.

In another preferred embodiment of the present invention, in order to reduce noise during the operation of the range extender The effect of sound and vibration on the driver increases the limit on limiting the range extender when the vehicle speed is below the first speed threshold. Among them, because there is no noise when the vehicle speed is low, for example: wind resistance sound or tire rolling sound, if the range extender operates at this time, the driver is easily disturbed by noise and vibration.

With the above two restrictions, although the target vehicle needs to meet more restrictions when driving, the dynamic programming method can still find the optimal control input at each time point under these constraints, so that the defined cost function can be minimized. (The range extender has the least fuel consumption).

The dynamic programming optimization problem known at both endpoints can be subdivided into a number of similar optimization problems for each time step, as shown in equations (8) to (10), respectively, corresponding to the final time step. , intermediate time steps, and initial time steps. At step N-1: In step n and in accordance with 2 When n < N -1: At step 1:

Step 3: According to the optimized energy management plan, the suboptimal energy management plan of the electric vehicle under the vehicle mode is separately obtained.

In this embodiment, in terms of the architecture of the sub-optimized energy management plan, the power slip ratio (PSR), the driver power demand (Power Demand), and the battery residual power (SOC) are used to organize the three variables. Optimize energy management planning. It should be noted that in other embodiments, this step may also organize the optimized energy management plan via other variables (vehicle speed, accumulated time of vehicle travel, distance accumulated by vehicle travel, etc.).

Firstly, according to the driver's power demand and battery residual capacity, the timing of the start/stop of the range extender engine found by the dynamic planning is separately summarized into two situations: battery discharge and battery charging, as shown in the second and third figures. When the target vehicle is in the battery mode (Battery Discharging), and the driver power demand is greater than the first threshold value V1, the range extender engine will start, and when the range extender engine is started, the range extender will start to provide power to When the electric motor drives the target car or recharges the battery, the battery enters the charging mode (Battery Charging). If the driver's power demand is lower than the second threshold V2 and the third threshold V3, the range extender engine will stop. Running.

In addition, when the range extender engine is started, the ranger output power demand will be determined according to the power distribution ratio, as shown in the following equation: As shown in the fourth figure, it is the relationship between the PSR, the driver's power demand, and the three variables of the battery residual capacity under the one-vehicle mode (this figure takes the vehicle model A as an example). In addition, from the fifth picture only considering the two variables of PSR and driver power demand, it can be found that the two can use the least square method to summarize two curves, corresponding to high driver power demand and low driver power demand, respectively. The threshold C1 is set to the lowest value of the high driver power demand.

In addition, in order to avoid frequent start/stop loss of the range extender engine and increase fuel consumption, the present invention additionally formulates a fourth threshold V4 for determining that a few seconds must elapse after the rangeter engine is started. The range extender engine can be stopped.

The optimal energy management plan identified by the dynamic programming method uses battery charge and discharge classification, and according to the power distribution ratio, driver power demand and battery residual capacity, it can be found that the first to four thresholds V1~V4 can be designed. A sub-optimal energy management plan is achieved, and such threshold values will have a large impact on the control results. In order to find the best first to fourth thresholds V1~V4, the present invention utilizes the lattice search method and cooperates with the cost function defined by equation (12). Find the best first to four thresholds V1~V4 for the suboptimal energy management plan summarized in all vehicle models, as shown in the sixth figure.

Where Σ m _{f , rule} is to use the optimized secondary energy management plan to control the fuel consumption of the range extender engine accumulated after the target car drives in one of the vehicle modes; Σ m _{f , DP} is to use dynamic programming to find out The optimized energy management plan controls the fuel consumption of the range extender engine accumulated after the target vehicle travels in one of the vehicle modes. In addition, in the cost function, Σ m _{f , rule is} divided by Σ m _{f , DP} is the hope Σ m _{f , the rule is} as small as possible. Where Σ S _{tr , rule} is to use the suboptimized energy management plan to control the driving distance accumulated by the target car after driving in one of the vehicle modes; Σ S _{tr , DP} is the best to find out by using dynamic programming The energy management plan controls the travel distance accumulated by the target vehicle after driving in a specific representative vehicle mode. In addition, the cost function divides _tr S _{tr , DP} by Σ S _{tr , and the rule} is to hope that _tr S _{tr , rule} The bigger the better. The SOC _{fl , rule} is to use the optimized secondary energy management plan to control the final battery residual capacity of the target car after driving in one of the vehicle modes. Where ρ and ν are weights, the present invention assumes 10 and 10000.

Through Step 3, the target vehicle can be driven in all the vehicle modes and the optimized energy management plan found by the dynamic programming method is summarized into the optimized energy management plan. Among them, the strategy structure and the first to fourth thresholds The values V1 to V4 are the results of the dynamic planning results as a design reference. Therefore, the correctness of the control strategy architecture can be ensured, which not only greatly reduces the adjustment time of engineers, but also improves the shortcomings of the prior art engineers using their own work and driving experience.

Step 4: Identify the actual driving condition of the electric vehicle by using a line of vehicle state identification means, and determine the actual driving condition of the electric vehicle and which one the electric vehicle is driven on. The driving condition of the driving state is the closest.

Due to the above-mentioned suboptimal energy management plan for each vehicle model, the best results can be obtained only when the energy management control is performed for each vehicle mode. If it is applied to other vehicle models, the control effect is obtained. Will be greatly discounted. Therefore, in order to optimize the energy management plan identified by these vehicle models, the target vehicle can have the best control effect when actually driving. The present invention proposes a vehicle type identification means, and its architecture is as follows. The figure shows.

First, a first-in-first-out (FIFO) register is used to store the driver's power demand of the target vehicle within a certain number of seconds while the target vehicle is driving, and then a line of vehicle state recognition processor (DPR processor) The driver power demand stored in the register is calculated as the average power demand and the power demand standard deviation of the target vehicle traveling within a certain number of seconds. After the average power demand and the standard deviation of the power demand are calculated, the equation (13) is used to determine the actual vehicle mode of the current driving and the power demand average and power of the target vehicle. The standard deviation of demand (refer to the first figure) is the closest.

Among them, RDP _{i is} the final model that determines the closest vehicle model to the current model; P _{dem, mean} is the average driver power demand calculated by the actual running processor; P _{dem, std} is the actual driving time The standard deviation of the driver's power demand calculated by the device; P _{dem, mean, i} is the average power demand of each vehicle model, as shown in the first figure; P _{dem, std, i} are the representative vehicle models. The power demand standard is poor, as shown in the first figure.

Step 5: controlling the range extender of the electric vehicle by using the suboptimal energy management plan of the electric vehicle that is closest to the actual driving condition of the electric vehicle in the vehicle mode, and repeating the execution One step and this step.

By using the vehicle type recognition means, after determining the actual driving mode of the target vehicle into one of the vehicle models, switching to the representative vehicle type corresponding to the discriminating The sub-optimized energy management plan utilizes the optimized energy management plan to control the target vehicle's range extender to operate in accordance with the suboptimal energy management plan. Finally, by repeating the fourth and fifth steps, the current driving condition of the target vehicle can be continuously judged when the target vehicle actually travels, and then the multi-mode switching control range extender is optimized for the energy management plan.

In other embodiments, in order to avoid the sub-optimal energy management planning switching too frequently to damage the engine of the range extender during actual driving, the present invention sets a switching of the optimized energy management plan after a certain time.

The above is an experiment in which a target vehicle is actually or simulated, and the design method of the strategy claimed by the present invention is proved to be feasible through the actual progress of the experiment, the formula used, the algorithm, and the experimental data. Therefore, the strategies generated by the above design methods are also within the scope of the invention.

However, via the above description, a strategy generated by using a controller to perform the design method of the present invention would require a very powerful controller, and it is conceivable that the cost required will be considerable. Therefore, the inventors have devised another strategy which utilizes a rule-based rule-based multi-mode switching energy management strategy developed by the above-described design method, so that the present invention can be practically applied, not As with other energy management strategies in the prior art, although the control effect is good, it requires an expensive controller to calculate huge data, and thus cannot be practically applied to the electric vehicle. Wherein, the so-called rule-based rule is that the controller executes an instruction via a rule, and the operation content only needs to determine what is the current situation, and performs what actions should be performed in the case, therefore, if the strategy of the present invention becomes a rule-based The strategy, that is, the strategy produced by the design method of the present invention, can be performed without the need for a powerful controller.

The following uses a diagram and an experiment to verify that the rule-based strategy of the present invention approaches the optimal energy management plan of the dynamic programming method and can be implemented, wherein the rule-based strategy is found using a similar figure. The first to four thresholds V1~V4 write the controller as The if-else control architecture is implemented by repeating the steps of four to five modes switching.

Referring to the eighth figure, the control result for the vehicle type A is shown. Using the five steps of the above systematic design method, firstly, the sub-optimal energy management plan is found through steps 1 to 3, and then the steps 4-5 are used. Judging and multi-mode switching sub-optimal energy management planning, and finally comparing the dynamic planning method to find the optimal energy management plan. From the eighth picture, it can be found that the sub-optimal rule-based result is quite close to the optimized optimal energy management plan control result (Optimal DP Result) found by the dynamic plan. . In addition, the eighth figure plots a prior art management curve to distinguish the present invention from the prior art.

In order to confirm the rule-based multi-mode switching energy management strategy established by the present invention, it can be applied to the target vehicle and perform energy management control during its actual driving. The present invention additionally selects three types of vehicle modes: Kaohsiung suburban vehicle type H, Taichung The model of the suburban trip and the model of the suburb of Taipei. Among them, since the three vehicle models are not used in the process of designing the above energy management strategy, the verification result can be made more authentic.

The verification results are summarized as shown in the ninth figure. In order to simultaneously know the energy management strategy for the range controller engine fuel consumption and battery protection control performance, the present invention additionally develops a cost function, as shown in equation (14).

Where Σ m _f is a rule-based energy management strategy established by the present invention to control the fuel consumption of the range extender engine accumulated after the target vehicle is driven in a vehicle mode state; Σ m _{f , TCS} is a traditional thermostat energy management strategy, after the target vehicle traveling in the driving control patterns accumulated extender engine fuel consumption, the cost will be divided by function Σ m _f Σ m _{f, TCS} purpose is to Σ m _f better. Σ E _{b_loss} is a rule-based energy management strategy established by the present invention to control the battery energy loss accumulated after the target vehicle is driven in a vehicle mode state; Σ E _{b_loss , TCS} is a traditional thermostat energy management strategy, and the control target is used. carrier after driving patterns with accumulated battery energy loss, loss due to higher battery energy, the more representative of the generated heat, high heat will affect the battery life, and therefore in the cost function divided by Σ E _Σ E b_loss _{B_loss , TCS} aims to hope that Σ E _{b_loss is} as _small as possible. I _{b_avg} is a rule-based energy management strategy established by the invention to control the average battery charge and discharge current calculated after the target vehicle is driven in a vehicle mode state, and I _{b_avg , TCS} is a traditional thermostat energy management strategy. calculated after traveling to the target vehicle driving control patterns for the average cell charge and discharge currents, due to the high average charge-discharge current of the battery, also affect the battery life, and therefore in the cost function divided by _I b_avg _{I b_avg, TCS} object I hope that the I _{b_avg is as} small as possible.

If the numerator denominators of the J ₁ ~J ₃ project are the result of the traditional thermostat energy management strategy, the cost function J _tot will be 3, and the three items J ₁ ~ J ₃ are all 1. It can be seen from the ninth figure that the rule-based energy management strategy designed by the systematic design method proposed by the present invention has better fuel consumption of the range extender engine and control performance of the battery protection than the traditional thermostat energy management strategy. In addition, the ninth figure also shows the control results of the optimized energy management plan obtained by using dynamic programming as a comparison benchmark for another control performance.

The cost function J _tot of the dynamic programming (DP) control result is compared with the rule energy management strategy control result cost function J _tot proposed by the present invention, and the control of the rule-based multi-mode switching energy management strategy established by the invention can be found. Performance is already close to optimal control performance and is superior to conventional thermostat energy management strategies. Taking the model of the car in the suburbs of Taipei as an example, the accumulated range extender engine fuel consumption J ₁ is reduced by 7% compared with the conventional technology; in terms of energy loss, J ₂ is reduced by 53% compared with the conventional technology; In terms of current, J ₃ is reduced by 14% compared to conventional techniques.

In addition, the strategy generated by the systematic design method proposed by the present invention and the rule-based strategy generated by the strategy can be written into a controller in a programmatic manner, and the controller can be applied to all commercially available Electric vehicle for the range extender.

In conclusion, it can be seen from the experimental results that the present invention can reduce the fuel consumption of the engine and save resources compared with the prior art; the battery can be protected by the prior art, wherein the protection is less because the heat loss is less and the charge and discharge current is less. The purpose of the battery to increase its life. In addition, the condition of the speed limit threshold value added in step 2 is such that when the driver's driving vehicle speed is lower than a certain threshold value, the range extender operation is restricted (ie, the range extender engine is not activated), so the present invention can reduce The impact of the vibration and noise of the range engine operation on the driver.

The above is only a preferred embodiment for explaining the present invention, and is not intended to limit the present invention in any way, and any modifications or alterations to the present invention made in the spirit of the same invention. All should still be included in the scope of the intention of the present invention.

Claims (6)

A method for designing an energy management strategy for an extended-range electric vehicle for generating a strategy for applying to an electric vehicle having a range extender, the method comprising: selecting a plurality of numbers based on driving conditions of the electric vehicle a vehicle model state, and the electric vehicle is driven in the vehicle mode; the dynamic energy planning method is used to determine the optimal energy management plan of the electric vehicle in the vehicle mode, wherein The dynamic programming method is for a nonlinear system, and the state variable of the starting point of a driving distance and the state variable of the end point of a running distance are represented as known optimization problems at both ends; respectively, according to the optimized energy management plan The electric vehicle is driven by the suboptimal energy management plan in the vehicle mode; the actual driving condition of the electric vehicle is identified by a vehicle type identification means, and the actual driving condition of the electric vehicle is determined The driving condition in which the electric vehicle is driven in the driving state is the closest; and the electric vehicle that is closest to the actual driving condition of the electric vehicle travels on the motor vehicle The suboptimal energy management plan in the vehicle mode controls the range extender of the electric vehicle, and repeats the previous step and the step; wherein the electric vehicle is driven in the vehicle mode, respectively Calculating a power demand average value and a power demand standard deviation value, the vehicle type identification means determines the actual driving condition of the electric vehicle and the electric load according to the average value of the power demand and the standard deviation of the power demand. The driving situation with which driving state is closest is the closest. The method for designing an energy management strategy for an extended-range electric vehicle according to the first aspect of the patent application, wherein when the dynamic energy planning method is used to obtain an optimized energy management plan, a battery charge for the electric vehicle is additionally added. The condition that the discharge current is limited in size. For example, the method for designing an energy management strategy for an extended-range electric vehicle according to the second aspect of the patent application, wherein when the dynamic planning method is used to obtain an optimized energy management plan, the speed of the electric vehicle is additionally added. The limit condition that the range extender cannot be turned on when the first speed limit threshold is depreciated. A method for designing an energy management strategy for an extended-range electric vehicle as described in claim 3, wherein the results of the optimized energy management plan are utilized, and based on a power distribution ratio, a driver power demand, and A case where the residual battery power is charged and discharged via the battery of the electric vehicle, and the suboptimal energy management including a plurality of first threshold values, a second threshold value, a third threshold value, and a fourth threshold value is summarized. planning. The method of designing an energy management strategy for an extended-range electric vehicle according to claim 4, wherein the vehicle type identification means comprises a register and a processor. A method for designing an energy management strategy for an extended-range electric vehicle as described in claim 5, wherein a certain period of time elapses when the last two steps are repeatedly performed.