This application claims priority of Chinese Patent Application No. 201911247511.2, filed on Dec. 9, 2019, under applicable portions of 35 U.S.C. § 119. The entire content of the priority application is incorporated herein by reference in their entirety.
The present disclosure relates to the technical field of motor drive for electric vehicles, and in particular, to a method for dual-motor control on an electric vehicle based on adaptive dynamic programming.
At present, drive structures of pure electric vehicles can be classified into centralized drives and distributed drives. On centralized-drive electric vehicles, drive motors are used to provide total power, and the power is transmitted to wheels through drive shafts, reducers, and differentials. This drive structure is similar to a drive structure of a conventional vehicle with relatively mature technologies and wide application, such as an integrated motor power transmission type, a single-motor direct drive type, and a dual-motor coupling drive type. However, for a single-motor direct drive, there are high requirements on torque performance parameters of a motor system, leading to high motor costs.
A dual-motor drive uses two drive motors. A mechanical device implements total power coupling. Then a transmission shaft, a reducer, etc. implement torque coupling and output. Torque and power are reduced by increasing the number of motors, thereby relaxing requirements for core components such as an insulated gate bipolar transistor (IGBT). A distributed dual-motor drive is a relatively novel type of drive. A vehicle is driven by a wheel or hub motor. A distributed drive system is arranged flexibly, occupies a small space, and has a simple structure without a conventional differential. Electronic differential is implemented through motor control, which delivers better dynamic performance than conventional motor control. However, battery-powered electric buses still have the disadvantages of long charging time, short battery life, etc. Therefore, reducing energy costs and improving energy utilization are very important and necessary for further application of battery-powered electric vehicles. To achieve this objective, it is crucial to determine an optimal and appropriate power management strategy for a multi-motor control system. There are various types of motor drive systems. The efficiency of the drive system, which is a core power component of a vehicle, largely determines the energy consumption level of the whole vehicle. The overall efficiency of the drive system depends on the efficiency characteristics of the component itself on the one hand, and on the control strategy at the system level on the other.
Regarding the power or torque distribution control at the system level, in development and research of most hybrid vehicles in and outside China, methods such as fuzzy control and dynamic programming are used to study motor power distribution. An optimization objective is usually the minimum energy consumption or optimal integrated economy of electronic vehicles. A corresponding control strategy is used to achieve optimal battery life of pure electric vehicles. In addition, attention is paid to the stability of distributed-drive vehicles. When dynamic programming or fuzzy control is conducted to distribute a motor operating mode and torque, the application of the fuzzy control strategy can optimize torque and energy distribution to a certain extent as non-linearity of motor control and operating conditions are increasingly complex. However, operating conditions, load, battery performance, etc. cannot be fully covered during rule formulation for fuzzy control. Therefore, some vulnerabilities are prone to appear. In addition, after the efficiency of system components changes during driving, corresponding rules and parameters cannot be promptly adjusted for the control strategy. A desired control effect cannot be achieved due to system errors accumulating for a long period of time. When dynamic programming is conducted to distribute torque, the “dimensional disaster” problem is inevitable as nonlinear complexity of the system is increasingly high, and the controller cannot run online in real time. In the prior art, usually, only the motor mode of a motor is considered for energy distribution of the multi-motor system, and the engine mode of the motor during braking is rarely considered.
A large number of achievements related to dynamic programming accumulated in practical application in terms of these problems, but dynamic programming has obvious disadvantages. Its backward-in-time algorithm produces a huge amount of calculation.
Therefore, the algorithm is suitable only for optimal control of small-scale simple nonlinear systems. Many ideas and specific technical methods for solving nonlinear system optimization problems were proposed with the development and enrichment of reinforcement learning, artificial neural networks, fuzzy systems, evolutionary computing, etc. in the field of artificial intelligence.
In view of this, the research on a method for dual-motor control on an electric vehicle based on adaptive dynamic programming is a technical problem to be solved urgently by a person skilled in the art.
An objective of the present disclosure is to provide a method for dual-motor control on an electric vehicle based on adaptive dynamic programming. The method aims to optimize dual-motor operating points and dual-motor drive torque distribution by using an adaptive dynamic programming method, so as to resolve a problem of dual-motor torque distribution on the electric vehicle under complex operating conditions. In this way, it is ensured that dual-motor operating points of the electric vehicle under various driving conditions deliver the optimal efficiency. In addition, it is ensured that efficiency output of the dual-motor power system of the electric vehicle under different driving conditions is an optimal control law. This resolves a conflict between power and efficiency of the electric vehicle, and improves power performance and efficiency of the dual-motor system of the electric vehicle.
To achieve the above objective, the present disclosure provides a method for dual-motor control on an electric vehicle based on adaptive dynamic programming, including the following steps:
Preferably, the data information corresponding to the two motors of the electric vehicle under various driving conditions in S1 are obtained by a torque sensor, a rotational speed sensor, a voltage sensor, and a current sensor.
Preferably, the total torque Te required by the two motors of the electric vehicle in S2 can be expressed by the following formula:
T
e
=T′
e
+T″
e (1), wherein
T′e denotes total output torque of the motors of the electric vehicle under a current operating condition, and T′e=F/k, wherein F denotes driving force required by the electric vehicle under the current operating condition, and k denotes efficiency during kinetic energy transmission; T″e denotes torque that is calculated from the opening and closing of the accelerator pedal of the electric vehicle, and T″e=A*Temax, wherein A denotes an accelerator pedal opening of the electric vehicle per unit time, and Temax denotes maximum torque of the two motors; and F=Froll+Fair+Faccel+Fgrad, wherein Froll denotes rolling resistance of the electric vehicle, Fair denotes air resistance when the electric vehicle is driving, Faccel denotes acceleration resistance when the electric vehicle accelerates, and Fgrad denotes gradient resistance when the electric vehicle drives uphill.
Preferably, the total torque required by the two motors of the electric vehicle in S2 is further related to vehicle-mounted battery information soc, specifically including:
Preferably, S2 specifically includes:
S21. conducting torque distribution for the two motors based on the total torque that is required by the two motors of the electric vehicle under different driving conditions and calculated in S1, which can be expressed by the following formula:
T
e
=T
e1
+T
e2 (2), wherein
Te1 and Te2 denote output torque of the two motors of the electric vehicle, respectively;
S22. establishing the execution network and the evaluation network for the electric vehicle, and conducting offline training on the execution network and the evaluation network based on the data information of the electric vehicle obtained in S1;
S23. establishing a minimum energy consumption function for a high-efficiency operating point of the two motors of the electric vehicle to minimize energy consumption of dual-motor operation of the electric vehicle, thereby obtaining a data set of high-efficiency dual-motor operation of the electric vehicle under different driving conditions, which can be expressed by the following formula:
minAIM=α(P1−PTe1)+β(P2−PTe2) (3), wherein
P1 and P2 denote drive system output power of the two motors, PTe1 and PTe2 denote actual output power of the two motors, α and β denote weighting coefficients, α and β are proportional to rated power of the two motors, and α+β=1; and
S24. establishing the efficiency MAP database of the dual-motor high-efficiency operating area of the electric vehicle, and generating a controller signal based on the data set of high-efficiency dual-motor operation obtained in S23.
Preferably, the execution network training in S22 can be expressed by the following formula:
c
l+1(xk)=min{U(xk,uk)+J(xk+1,cl)} (4), wherein
J(xk,cl)≥J(xk,cl+1), J denotes a cost function, U denotes a utility function, xk denotes input of the execution network at a current moment, xk+1 denotes input of the execution network at a next moment, uk denotes output of the execution network at the current moment, cl denotes a control law at the current moment, and cl+1 denotes a control law at the next moment; and
the evaluation network training can be expressed by the following formula:
J
l+1(xk,c)=U(xk,uk)+Jl(xk+1,c) (5), wherein
Jl+1(xk,c)≤Jl(xk+1,c), Jl denotes a cost function at the current moment, Jl+1 denotes an updated cost function, and C denotes a given control law.
Preferably, S3 specifically includes:
S31. obtaining, by the controller, data information of the electric vehicle in real time, and initializing a system control parameter; and
S32. inputting the obtained real-time data information into the execution network and the evaluation network, and finding the optimal control law for the electric vehicle by using iteration and online update methods to optimize the dual-motor control on the electric vehicle.
Preferably, S32 specifically includes:
S321. inputting the real-time data information of the electric vehicle into the execution network to obtain optimal torque distribution of the two motors, and calculating differences ΔTe1 and ΔTe2 between optimal output torque of the two motors and actual output torque of the two motors at the current moment, wherein the real-time data information comprises torque Te, motor efficiency map, rotational speed n, vehicle-mounted battery information soc, difference ΔTe between current torque and target torque, difference Δn between a current rotational speed and a target rotational speed, difference Δsoc between current vehicle-mounted battery information and target vehicle-mounted battery information, and ΔTe(t−1), ΔTe(t−2), map(t−1), map(t−2), Δn(t−1), Δn(t−2), Δsoc(t−1), and Δsoc(t−2) that are obtained through delay;
S322. obtaining differences ΔTe1(t−1), ΔTe1(t−2), ΔTe2(t−1), and ΔTe2(t−2) between optimal output torque and actual output torque of the two motors at moment t−1 and moment t−2 through delay based on differences ΔTe1 and ΔTe2 between optimal output torque of the two motors and actual output torque of the two motors at the current moment that are obtained in S321;
S323. inputting the real-time data information ΔTe1, ΔTe2, ΔTe1(t−1), ΔTe1(t−2), ΔTe2(t−1), ΔTe2(t−2), map, map(t−1), map(t−2), Δsoc, Δsoc(t−1), and Δsoc(t−2) obtained in S321 and S322 into the evaluation network to obtain a value of cost function ĵ(t) of the evaluation network at moment t;
S324. obtaining real-time data information ΔTe1(t−3), map(t−3), and Δsoc(t−3) at moment t−3 through delay, and inputting the obtained real-time data information ΔTe1(t−1) ΔTe1(t−2), ΔTe1(t−3), ΔTe2(t−1), ΔTe2(t−2), map(t−1), map(t−2), map(t−3), Δsoc(t−1), Δsoc(t−2), and Δsoc(t−3) into evaluation network to obtain a value of cost function Ĵ(t−1) of the evaluation network at moment t−1;
S325. updating weights of the evaluation network and the execution network based on the results obtained in the foregoing steps; and
S326. repeating S321 to S325 until the optimal cost function and the optimal control law are found.
Preferably, an equation for updating the weight of the evaluation network in S325 can be expressed as follows:
W
c(t+1)=Wc(t)+ΔWc(t) (6), wherein
Wc(t) denotes a weight matrix of the evaluation network at moment t, and ΔWc(t) denotes a weight change value of the evaluation network from moment t to moment t+1; and
equations for updating the weight of the execution network can be expressed as follows:
wherein
Wa denotes a weight matrix of the execution network, ΔWa(t) denotes a weight change value of the execution network from moment t to moment t+1, J(t) denotes a cost function at moment t, u(t) denotes output of the execution network at moment t, and η(η>0) denotes a learning rate.
Compared with the prior art, the present disclosure aims to optimize dual-motor operating points and dual-motor drive torque distribution by using the adaptive dynamic programming method. In this way, it is ensured that dual-motor operating points of the electric vehicle under various driving conditions deliver the optimal efficiency. In addition, it is ensured that efficiency output of the dual-motor power system of the electric vehicle under different driving conditions is the optimal control law. This resolves the conflict between power and efficiency of the electric vehicle, and improves power performance and efficiency of the dual-motor system of the electric vehicle.
To enable a person skilled in the art to better understand technical solutions of the present disclosure, the present disclosure is further described below in detail with reference to the accompanying drawings.
As shown in
S1. A controller obtains data information of the electric vehicle under various driving conditions, and calculates total torque required by two motors of the electric vehicle based on the obtained data information and a corresponding accelerator pedal opening and/or brake pedal opening.
S2. Establish an execution network and an evaluation network for the electric vehicle, conduct offline training based on the data information obtained in S1, and dynamically distribute total torque of the two motors of the electric vehicle under various driving conditions by using an adaptive dynamic programming method to obtain an efficiency MAP database of a dual-motor high-efficiency operating area of the electric vehicle.
S3. Obtain data information of the electric vehicle under a real-time driving condition, and conduct online learning on the execution network and the evaluation network based on the obtained real-time data information of the electric vehicle to find an optimal control law of the electric vehicle under the real-time driving condition, and optimize the dual-motor control on the electric vehicle.
In this embodiment, the controller obtained the data information of the electric vehicle under various driving conditions and calculated the total torque required. Then offline training was conducted on the execution network and the evaluation network based on the obtained data information. In addition, total torque was dynamically distributed for the two motors of the electric vehicle under various driving conditions by using the adaptive dynamic programming method to obtain the efficiency MAP database of the dual-motor high-efficiency operating area of the electric vehicle. Finally, data information of the electric vehicle under different driving conditions was obtained in real time, and iteration and online learning were conducted on the execution network and the evaluation network based on the obtained real-time data information. In this way, the optimal control law of the electric vehicle under the real-time driving condition was found, and the dual-motor control on the electric vehicle was optimized. In this embodiment, dual-motor operating points and dual-motor drive torque distribution were optimized by using the adaptive dynamic programming method. In this way, it was ensured that dual-motor operating points of the electric vehicle under various driving conditions delivered the optimal efficiency. In addition, it was ensured that efficiency output of the dual-motor power system of the electric vehicle under different driving conditions was the optimal control law. This resolved a conflict between power and efficiency of the electric vehicle, and improved power performance and efficiency of the dual-motor system of the electric vehicle.
As shown in
As shown in
T
e
=T′
e
+T″
e (1).
In formula (1), T′e denotes total output torque of the motors of the electric vehicle under a current operating condition, and T′e=F/k, wherein F denotes driving force required by the electric vehicle under the current operating condition, and k denotes efficiency during kinetic energy transmission; T″e denotes torque that is calculated from the opening and closing of the accelerator pedal of the electric vehicle, and T″e=A*Temax, wherein A denotes an accelerator pedal opening of the electric vehicle per unit time, and Temax denotes maximum torque of the two motors; and F=Froll+Fair+Faccel+Fgrad, wherein Froll denotes rolling resistance of the electric vehicle, Fair denotes air resistance when the electric vehicle is driving, Faccel denotes acceleration resistance when the electric vehicle accelerates, and Fgrad denotes gradient resistance when the electric vehicle drives uphill.
In this embodiment, when the driving force and total torque were analyzed and calculated based on the accelerator pedal opening or brake pedal opening, the current vehicle attitude and operating condition needed to be determined by the sensors first, and then the total torque required by the electric vehicle was calculated based on the actual situation and the amount of loss. This was because there were rolling resistance, air resistance, acceleration resistance, and gradient resistance during uphill driving when the electric vehicle was driving, and there was a corresponding loss k during kinetic energy transmission.
As shown in
In this embodiment, since a pedal instruction may be closely related to the vehicle-mounted battery information soc, torque that is calculated from the opening and closing of the accelerator pedal of the electric vehicle in cases of different vehicle-mounted battery information soc may be optimized, analyzed, and calculated to obtain more accurate total torque required.
As shown in
S21. Conduct torque distribution for the two motors based on the total torque that is required by the two motors of the electric vehicle under different driving conditions and calculated in S1, which can be expressed by the following formula:
T
e
=T
e1
+T
e2 (2).
In formula (2), Te1 and Te2 denote output torque of the two motors of the electric vehicle (to be specific, Te1 denotes the output torque of one motor of the electric vehicle, and Te2 denotes the output torque of the other motor of the electric vehicle).
S22. Establish the execution network and the evaluation network for the electric vehicle, and conduct offline training on the execution network and the evaluation network based on the data information of the electric vehicle obtained in S1.
S23. Establish a minimum energy consumption function for a high-efficiency operating point of the two motors of the electric vehicle to minimize energy consumption of dual-motor operation of the electric vehicle, thereby obtaining a data set of high-efficiency dual-motor operation of the electric vehicle under different driving conditions, which can be expressed by the following formula:
minAIM=α(P1−PTe1)+β(P2−PTe2) (3).
In formula (3), P1 and P2 denote drive system output power of the two motors, PTe1 and PTe2 denote actual output power of the two motors, α and β denote weighting coefficients, α and β are proportional to rated power of the two motors, and α+β=1.
S24. Establish the efficiency MAP database of the dual-motor high-efficiency operating area of the electric vehicle, and generate a controller signal based on the data set of high-efficiency dual-motor operation obtained in S23.
In this embodiment, the obtained total torque required by the two motors of the electric vehicle was dynamically distributed by using the adaptive dynamic programming method first. In addition, offline training was conducted on the execution network and the evaluation network to obtain weights of the execution network and the evaluation network. Then the efficiency MAP database, which included the rotational speed and torque, of the dual-motor high-efficiency operating area of the electric vehicle was established with the objective of minimizing energy consumption of dual-motor operation of the electric vehicle, and the controller signal was generated.
As shown in
c
l+1(xk)=min{U(xk,uk)+J(xk+1,cl)} (4).
In formula (4), J(xk,cl)≥J(xk,cl+1), J denotes a cost function, U denotes a utility function, xk denotes input of the execution network at a current moment, (that is, a state constraint), xk+1 denotes input of the execution network at a next moment, (that is, a state constraint), uk denotes output of the execution network at the current moment, (that is, a state constraint), cl denotes a control law at the current moment, and cl+1 denotes a control law at the next moment.
The evaluation network training can be expressed by the following formula:
J
l+1(xk,c)=U(xk,uk)+Jl(xk+1,c) (5), wherein
In formula (5), Jl+1(xk,c)≤Jl(xk+1,c), Jl denotes a cost function at the current moment, Jl+1 denotes an updated cost function, and c denotes a given control law.
In this embodiment, the execution network aimed to achieve an extreme value of output of the evaluation network. Therefore, the execution network training was determined by the evaluation network, that is, cost function Ĵ(t) was learned. The input of the execution network can be expressed as:
In the formulas, ah1j(t) denotes input of a jth neuron in the hidden layer of the execution network, ah2j(t) denotes output of the jth neuron in the hidden layer of the execution network, i denotes the number of inputs, Wa1 denotes a weight matrix from an input layer to the hidden layer of the execution network, and Wa2 denotes a weight matrix from the hidden layer to the output layer of the execution network.
In this embodiment, a matrix weight may be adjusted by using a gradient descent method during the execution network training to minimize the cost function Ĵ(t), which may be expressed as
In formula, u(t) denotes the output of the execution network at moment t, and η(η>0) denotes the learning rate. In this embodiment, there are a total of 15 inputs of the execution network.
The output of the evaluation network may be an estimated value of J(t) a performance indicator). The evaluation network training may be implemented by minimizing an error function of the following formula:
In the formula, Ĵ(jt)=J[x(t), u(t), t, Wc], Wc denotes a parameter of the evaluation network, and the utility function U(t)=U[x(t), u(t), t]. For all tS,
when Ec(t)=0, that is, there is no need to substitute Wc into calculation, where 0<γ<1. In tracking control design for the two motors of the electric vehicle, a control objective is to minimize the finite sum of U(t) from the current moment to the infinite future, and the utility function
In this embodiment, the evaluation network and the execution network are both designed as a three-layer feedforward neural network including an input layer, a hidden layer, and an output layer. The input of the evaluation network may be the actual output values (Te1 and Te2) of the motors, an actual motor operating point MAP, required torque values (T*e1 and T*e2) that are read from the database and that needs to be tracked by the current learning control algorithm, a high-efficiency motor operating area MAP that needs to be tracked (when a motor runs in a constant torque area, a rotational speed of the motor is relatively low, and output torque is relatively large, which meets the requirements of the electric vehicle for fast starting, acceleration, climbing, etc.; when the motor runs in a constant power area, the rotational speed of the motor is relatively high, which meets the requirements of the electric vehicle for high-speed driving, overtaking, etc. on flat roads), the vehicle-mounted battery state SOC, a vehicle-mounted battery status SOC* tracked by the algorithm, and ΔTe1(t−1), ΔTe1(t−2), ΔTe2(t−1), ΔTe2(t−2), map(t−1), map(t−2), Δsoc(t−1), and Δsoc(t−2) obtained through delay in practice. The evaluation network training includes forward calculation and error back propagation, and during the error back propagation, the weight matrix of the evaluation network is updated by the error feedback.
The forward calculation of the evaluation network may include the following:
The input InputC(t) of the evaluation network can be expressed as
A relationship between the input layer and the hidden layer can be expressed as
In the formula, Ch1j denotes the input of the jth neuron in the hidden layer, Wc1 denotes the weight matrix from the input layer to the hidden layer of the evaluation network, and Ch2j denotes the output of the jth neuron in the hidden layer, and can be expressed as
In this case
denotes the weight matrix from the hidden layer to the output layer of the evaluation network. In this embodiment, there are a total of 12 inputs of the evaluation network.
In this embodiment, the evaluation network may be trained by using the gradient descent method. A process of updating the weight of the evaluation network may be as follows:
Weight matrix Wc2 from the hidden layer to the output layer:
Weight matrix Wc1 from the input layer to the hidden layer:
In the formulas, lc denotes the learning rate, ec(t)=Ĵ(t)−U(t)−γĴ(t+1), and C(k) denotes a state vector input at the current moment.
In this embodiment, the hidden layer of the evaluation network adopts a bipolar sigmoidal function, and the output layer adopts a purelin linear function. The gradient descent algorithm (traingdx) is applied to the evaluation network training. In addition, batch processing can also be used to train the evaluation network. In other embodiments, other algorithms such as tradingd, tradingda, tradingdm, and trainlm are also applicable.
As shown in
S31. The controller obtains data information of the electric vehicle in real time, and initializes a system control parameter.
S32. Input the obtained real-time data information into the execution network and the evaluation network, and find the optimal control law for the electric vehicle by using iteration and online update methods to optimize the dual-motor control on the electric vehicle.
As shown in
S321. Input the real-time data information of the electric vehicle into the execution network to obtain optimal torque distribution of the two motors, and calculate differences ΔTe1 and ΔTe2 between optimal output torque of the two motors and actual output torque of the two motors at the current moment, where the real-time data information includes torque Te, motor efficiency map, rotational speed n, vehicle-mounted battery information soc, difference ΔTe between current torque and target torque, difference Δn between a current rotational speed and a target rotational speed, difference Δsoc between current vehicle-mounted battery information and target vehicle-mounted battery information, and ΔTe(t−1), ΔTe(t−2), map(t−1), map(t−2), Δn(t−1), Δn(t−2), Δsoc(t−1), and Δsoc(t−2) that are obtained through delay.
S322. Obtain differences Δte1(t−1), Δte1(t−2), Δte2(t−1), and Δte2(t−2), between optimal output torque and actual output torque of the two motors at moment t−1 and moment t−2 through delay based on the obtained differences ΔTe1 and ΔTe2 between optimal output torque of the two motors and actual output torque of the two motors at the current moment.
S323. Input ΔTe1, ΔTe2, Δte1(t−1), Δte1(t−2), Δte2(t−1), Δte2(t−2), map, map(t−1), map(t−2), Δsoc, Δsoc(t−1), and Δsoc(t−2) obtained in S321 and S322 into the evaluation network to obtain a value of cost function ĵ(t) of the evaluation network.
S324. Obtain real-time data information ΔTe1(t−3), map(t−3), and Δsoc(t−3) at moment t−3 through delay, and inputting the obtained real-time data information ΔTe1(t−1), ΔTe1(t−2), ΔTe1(t−3), ΔTe2(t−1), ΔTe2(t−2), map(t−1), map(t−2), map(t−3), Δsoc(t−1), Δsoc(t−2), and Δsoc(t−3) into evaluation network to obtain a value of cost function Ĵ(t−1) of the evaluation network.
S325. Update the weights of the evaluation network and the execution network based on an equation for updating the weight of the evaluation network and an equation for updating the weight of the execution network.
S326. Repeat steps S321 to S325 until the optimal cost function and the optimal control law are found.
In this embodiment, to optimize the dual-motor control on the electric vehicle, the real-time data information obtained by the controller was input to the execution network, and the online learning method was used to continuously optimize performance indicators of the evaluation network, thereby updating the weights of the execution network and the evaluation network. Network selection was conducted to promote rapid convergence of performance indicator functions. Optimal torque distribution for the two motors was rapidly implemented based on a real-time environment change. The optimal control law was output, and real-time online control was optimized. In this way, the two motors of the electric vehicle deliver good performance in dynamic torque distribution, motor response speed, and velocity jump smoothing. In this embodiment, the optimal output torque of the two motors refers to the optimal output torque distribution of the two motors corresponding to the high-efficiency operating area in the MAP database. The target torque refers to the motor torque corresponding to the high-efficiency operating area in the MAP database. The target rotational speed refers to the motor rotation speed corresponding to the high-efficiency operating area in the MAP database. The target vehicle-mounted battery information refers to the vehicle-mounted battery corresponding to the high-efficiency operating area in the MAP database. The real-time data information refers to the data information of the electric vehicle at the current moment.
As shown in
W
c(t+1)=Wc(t)+ΔWc(t) (6), wherein
In equation (6), Wc(t) denotes the weight matrix of the evaluation network at moment t, and ΔWc(t) denotes a weight change value of the evaluation network from moment t to moment t+1.
Equations for updating the weight of the execution network can be expressed as follows:
In formula (7) and formula (8), Wa denotes the weight matrix of the execution network, ΔWa(t) denotes a weight change value of the execution network from moment t to moment t+1, J(t) denotes a cost function at moment t, u(t) denotes output of the execution network at moment t, and η(η>0) denotes the learning rate.
In this embodiment, the evaluation network may be trained by using the gradient descent method. A process of updating the weight of the evaluation network may include the following:
(1) Update the weight matrix Wc2 from the hidden layer to the output layer, which can be expressed by the following formula:
In formula (9),
(2) Update the weight matrix Wc1 from the input layer to the hidden layer, which can be expressed by the following formula:
In formula (10).
The foregoing describes in detail the method for dual-motor control on an electric vehicle based on adaptive dynamic programming provided in the present disclosure. Several examples are used for illustration of the principles and implementation methods of the present disclosure. The description of the embodiments is used to help understand core principles of the present disclosure. It should be noted that, several improvements and modifications may be made by a person of ordinary skill in the art without departing from the principle of the present disclosure, and these improvements and modifications shall fall within the protection scope of the present disclosure.
Number | Date | Country | Kind |
---|---|---|---|
201911247511.2 | Dec 2019 | CN | national |