Electric vehicle battery temperature control method based on adaptive dynamic programming

Abstract

The present invention discloses a temperature control method for an electric vehicle battery based on adaptive dynamic programming, comprising the following steps: Step 1: establishing a battery model, a refrigerant cooling model and a battery electrothermal coupling model respectively, and outputting the battery temperature and the battery state of charge (SOC) according to the battery electrothermal coupling model; Step 2: establishing a global optimization problem for thermal management of an electric vehicle battery; 21) establishing a dynamic programming mathematical model, and determining the constraints of the optimization problem; 22) discretizing the driving conditions, state variables and decision variables respectively to obtain a set of state variables for any stage; 23) inversely solving the optimal objective function and corresponding decision variables for each stage; 24) given the initial state variables, forwardly solving the global optimal solution; Step 3: establishing an online thermal management optimization controller; 31) constructing an optimal thermal management strategy data set under multiple working conditions; 32) training a neural network to construct an online thermal management optimization controller.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery thermal management, and specifically provides a temperature control method for an electric vehicle battery based on adaptive dynamic programming. Background Art

[0002] With the rapid growth in sales of new energy vehicles, lithium-ion batteries are becoming increasingly popular as a means of storing and utilizing electrical energy. However, lithium-ion batteries generate significant heat during the charging and discharging process, which can cause the battery temperature to rise. Excessive temperatures can cause accidents such as internal short circuits, casing ruptures, and battery leakage and fire. Furthermore, prolonged exposure to high temperatures can significantly degrade lithium-ion battery performance. Therefore, to maximize the performance of lithium-ion batteries and ensure optimal charge and discharge efficiency, they must be maintained at a temperature between 15 and 40°C.

[0003] To control battery temperature, a battery thermal management system is required. However, since the battery thermal management system's energy is derived from the power battery's charge, ensuring the power battery operates within the optimal temperature range also increases the vehicle's power-consuming components. Furthermore, the energy consumed to regulate the power battery's temperature is often greater than the charge generated by temperature regulation, resulting in a reduction in the power battery's driving range.

[0004] Traditional control strategies for battery thermal management include logic threshold strategies, fuzzy logic control strategies, and global optimal energy management strategies. While the logic threshold control strategy is simple and stable, it cannot achieve optimal energy distribution within the power battery pack. Beyond the threshold management range, the power battery pack's energy utilization rate decreases. While the use of fuzzy control strategies can optimize energy distribution to a certain extent, the specification of fuzzy control rules is often influenced by the designer's own subjective factors, which can easily lead to loopholes. When the efficiency loss of system components changes during use, the control strategy cannot promptly adjust the corresponding rules and parameters, which can easily lead to local optimality. This, coupled with long-term accumulated system errors, can result in suboptimal control results. Among control strategies that consider dynamic programming optimization, conventional dynamic programming can only optimize specific driving conditions, cannot consider all vehicle driving conditions, and cannot operate in real-time online. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an electric vehicle battery temperature control method based on adaptive dynamic programming, which not only ensures that the battery temperature is reduced to a reasonable range, but also reduces the energy consumption of the thermal management system.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for controlling the battery temperature of an electric vehicle based on adaptive dynamic programming comprises the following steps:

[0008] Step 1: Build a control-oriented electric vehicle battery thermal management system model

[0009] Establish a battery model, a refrigerant cooling model, and a battery electrothermal coupling model respectively, and output the battery temperature and battery state of charge (SOC) according to the battery electrothermal coupling model;

[0010] Step 2: Establish a global optimization problem for electric vehicle battery thermal management

[0011] 21) Establish a dynamic programming mathematical model and determine the constraints of the optimization problem;

[0012] 22) Discretize the driving conditions, state variables, and decision variables to obtain the state variable set at any stage;

[0013] 23) Inversely solve the optimal objective function and corresponding decision variables at each stage;

[0014] 24) Given the initial state variables, forward solve the global optimal solution;

[0015] Step 3: Establish an online thermal management optimization controller

[0016] 31) Constructing a dataset of optimal thermal management strategies under various working conditions

[0017] Based on the dynamic programming optimization problem of electric vehicle battery thermal management for different driving conditions, the optimal decision sequence and optimal state sequence of the control variables are obtained, and the optimal thermal management strategy dataset is constructed based on the optimal decision sequence and optimal state sequence;

[0018] 32) Training a neural network

[0019] The constructed optimal thermal management strategy dataset is used to train the neural network and construct an online thermal management optimization controller.

[0020] Furthermore, in the step 1, in the refrigerant cooling model, the heat transferred from the refrigerant to the battery pack Expressed as:

[0021]

[0022] m r =λV c η c n c ρ c / 60

[0023] Where: C r is the heat capacity of the refrigerant; m ris the refrigerant flow rate flowing into the battery circuit; h n,in is the inlet enthalpy of the battery cold plate; h h,out is the outlet enthalpy of the battery cold plate; λ is the opening of the electronic expansion valve of the battery circuit; V c is the displacement of the compressor; η c is the volumetric efficiency of the compressor; n c is the compressor speed; ρ c is the density of the refrigerant.

[0024] Furthermore, in the step 1, in the battery model, the charge and discharge state SOC is expressed as:

[0025]

[0026] Among them, U oc is the external voltage; P bat Indicates battery power; P hp is the energy consumption of the thermal management system; R bat is the internal resistance; C bat is the total charge of the battery.

[0027] Furthermore, in the battery electrothermal coupling model in step 1, the influence of heat generated and dissipated by the refrigerant flowing around the battery module on the battery temperature is used to obtain the thermodynamic balance equation of the battery electrothermal coupling model:

[0028]

[0029] Where: m bat is the battery mass; C bat is the total charge of the battery; is the temperature change of the battery; T bat is the battery temperature; The heat generated inside the battery pack; The heat transferred from the refrigerant to the battery pack; bat is the charge and discharge current; R bat is the internal resistance; U oc is the external voltage.

[0030] Furthermore, in step 21), a dynamic programming mathematical model is constructed to minimize the cumulative energy consumption of battery thermal management within a global time range while satisfying the system terminal constraints:

[0031] J * =minJ=min{∑L(x(k),u(k))}

[0032] L(x(k),u(k))=ω1P hp +ω2|T bat ―T * bat |

[0033] Among them: J * is the optimal objective function; J is the objective function; ω1 and ω2 are weight coefficients; P hp is the energy consumption of the thermal management system; T bat is the battery temperature; is the target battery temperature; x(k) and u(k) are the state variable and control variable respectively, and:

[0034]

[0035] The constraints of the optimization problem are:

[0036]

[0037] Where: Q bc_max The maximum cooling capacity allocated to the battery circuit for the air conditioning system; SOC min and SOC max is the lower and upper limits of battery SOC; T bat_min and T bat_max are the lower and upper limits of the battery temperature.

[0038] Furthermore, in step 22), the driving condition is discretized into N stages by time, and the battery temperature T in the state variable is bat and battery state of charge SOC are discretized separately, and the number of discretized grids is a and b respectively; then the state variable set of any k stage is:

[0039]

[0040] Where: X k represents the set of state variables at stage k; represents a state variable in the kth stage; T bat_i represents the battery temperature at the i-th grid point; SOC j represents the battery state of charge at the jth grid point.

[0041] Furthermore, in step 23), the optimal objective function of each stage is reversely solved starting from stage N-1;

[0042] When k=N-1 stage, the objective function is:

[0043]

[0044] When k∈[1,…,N―2] stage, the objective function is:

[0045]

[0046] Where: F(·) is the state transfer function.

[0047] Furthermore, in step 24), the method for forward solving the global optimal solution is:

[0048] When k=1, according to the result of the reverse solution, find the decision variable u(1) of the initial state, make it act on the state variable, and use the state transfer equation to calculate the battery temperature T of the next stage. bat and battery state of charge SOC;

[0049] When k∈[2,…,N―1] stage, the state variable T of the current stage calculated according to the previous stage is bat (k) and SOC(k), find the corresponding optimal decision variable; if the optimal decision variable found is not the state variable of the current stage, the interpolation method is used to obtain the optimal decision variable u(k) of the current stage; this is gradually recursively carried out to the final stage to obtain the optimal control sequence {u * (1),u * (2),...,u * (k)} and the optimal state sequence {x * (1),x * (2),...,x * (k)}, that is, the optimal battery temperature trajectory and optimal battery state trajectory SOC * .

[0050] Furthermore, the battery temperature T bat and battery state of charge SOC as state variables, with battery circuit cooling capacity Q bc (k) is used as the control variable, and the state transfer equation is obtained as follows:

[0051]

[0052] Among them: U oc is the external voltage; P bat Indicates battery power; P hp is the energy consumption of the thermal management system, which is determined by the vehicle speed v and the battery circuit cooling capacity Q bc The values ​​of the two are obtained by looking up the table; R bat is the internal resistance; C bat is the total charge of the battery; The heat generated inside the battery pack; The heat transferred from the refrigerant to the battery pack; m bat is the battery mass; C bat is the total charge of the battery; T bat is the battery temperature.

[0053] The beneficial effects of the present invention are:

[0054] The electric vehicle battery temperature control method of the present invention, based on adaptive dynamic programming, improves the economy of the battery thermal management system compared to the temperature control strategy based on rules, and solves the problem of lack of real-time performance compared to the temperature control strategy based on dynamic programming algorithm. By designing a controller based on neural network and dynamic programming to adjust the cooling capacity of the battery circuit, and utilizing the characteristics of battery and heat coupling, it can simultaneously meet the temperature requirements and energy-saving requirements of the battery, ensuring that the battery temperature is reduced to a reasonable range and reducing the energy consumption of the thermal management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention provides the following drawings for illustration:

[0056] Figure 1 This is a flow chart of the electric vehicle battery temperature control method based on adaptive dynamic programming of the present invention;

[0057] Figure 2 This is a schematic diagram of the battery thermal management system for electric vehicles;

[0058] Figure 3 It is the steady-state model and working point cloud diagram of the air-conditioning system;

[0059] Figure 4 It is the battery SOC curve;

[0060] Figure 5 Schematic diagram of adaptive dynamic programming control for electric vehicle battery thermal management;

[0061] Figure 6 is the vehicle speed curve diagram of the selected vehicle driving cycle (WLTC);

[0062] Figure 7 Battery temperature optimization trajectory diagram for global optimization of electric vehicle battery thermal management;

[0063] Figure 8 A zoomed-in view of the battery temperature optimization trajectory for global optimization of electric vehicle battery thermal management. DETAILED DESCRIPTION

[0064] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0065] like Figure 1 As shown, the electric vehicle battery temperature control method based on adaptive dynamic programming in this embodiment includes the following steps.

[0066] Step 1: Build a control-oriented electric vehicle battery thermal management system model

[0067] like Figure 2 As shown, the electric vehicle battery thermal management system includes a battery thermal management system for providing refrigerant to the battery. The battery thermal management system includes a condenser, evaporator, compressor, and expansion valve. In this embodiment, when establishing a control-oriented electric vehicle battery thermal management system model, it is necessary to separately establish a battery model, a refrigerant cooling model, and a battery electrothermal coupling model. The battery electrothermal coupling model then outputs the battery temperature and battery state of charge (SOC).

[0068] (1) Refrigerant cooling model

[0069] like Figure 3 As shown in Figure 1, the steady-state model and working point cloud of the air-conditioning system are shown. Specifically, in the refrigerant cooling model, the refrigerant flow rate m flowing into the battery circuit is r for:

[0070] m r =λV c η c n c ρ c / 60

[0071] Heat transferred from the refrigerant to the battery pack Expressed as:

[0072]

[0073] Where: C r is the heat capacity of the refrigerant; m r is the refrigerant flow rate flowing into the battery circuit; h n,in is the inlet enthalpy of the battery cold plate; h h,out is the outlet enthalpy of the battery cold plate; λ is the opening of the electronic expansion valve of the battery circuit; V c is the displacement of the compressor; η c is the volumetric efficiency of the compressor; η c is the compressor speed; ρ c is the density of the refrigerant.

[0074] (2) Battery model

[0075] like Figure 4 As shown in the figure, it is a battery SOC curve. Specifically, in the battery model, the equivalent circuit model is composed of the external voltage U oc and internal resistance R bat Composition, where the charge and discharge current I bat Expressed as:

[0076] I bat =P bat / Ubat

[0077] Where: I bat is the charge and discharge current; P bat Indicates battery power; U bat is the external voltage;

[0078] Battery power P bat Expressed as:

[0079] P bat =P mot +P hp

[0080] Where: P mot is the motor power; P hp Energy consumption of thermal management system;

[0081] External voltage U bat Expressed as:

[0082] U bat =U oc ―I bat R bat

[0083] Where: External voltage U oc and R bat Affected by battery temperature and SOC, their values ​​can be obtained through offline testing, that is, U is obtained by table interpolation. oc and R bat The actual value of .

[0084] Based on the above formula, the charge and discharge current I bat It can be expressed as:

[0085]

[0086] The charge and discharge state SOC is expressed as:

[0087]

[0088] Then the charge and discharge state SOC can be expressed as:

[0089]

[0090] Among them, U oc is the external voltage; P bat Indicates battery power; P hp is the energy consumption of the thermal management system; R bat is the internal resistance; C bat is the total charge of the battery.

[0091] (3) Battery electrothermal coupling model

[0092] In the battery electrothermal coupling model, the heat generated inside the battery pack is for:

[0093]

[0094] The influence of heat generated and dissipated by the refrigerant flowing around the battery module on the battery temperature leads to the thermodynamic balance equation of the battery electrothermal coupling model:

[0095]

[0096] Where: m bat is the battery mass; C bat is the total charge of the battery; is the temperature change of the battery; T bat is the battery temperature; The heat generated inside the battery pack; The heat transferred from the refrigerant to the battery pack; bat is the charge and discharge current; R bat is the internal resistance; U oc is the external voltage.

[0097] Step 2: Establish a global optimization problem for electric vehicle battery thermal management. Select appropriate control inputs, establish a thermal management optimization problem, and determine the state and control constraints of the optimization problem.

[0098] 21) Establish a dynamic programming mathematical model and determine the constraints of the optimization problem.

[0099] In this embodiment, the dynamic programming mathematical model is constructed to minimize the cumulative energy consumption of battery thermal management within the global time range while satisfying the system terminal constraints:

[0100] J * =min J=min{∑L(x(k),u(k))}

[0101] L(x(k),u(k))=ω1P hp +ω2|T bat ―T * bat |

[0102] Among them: J * is the optimal objective function; J is the objective function; ω1 and ω2 are weight coefficients; P hp is the energy consumption of the thermal management system; T bat is the battery temperature; is the target battery temperature; x(k) and u(k) are the state variable and control variable respectively, and:

[0103]

[0104] The constraints of the optimization problem are:

[0105]

[0106] Where: Q bc_max The maximum cooling capacity allocated to the battery circuit for the air conditioning system; SOC min and SOC max is the lower and upper limits of battery SOC; T bat_min and T bat_max are the lower and upper limits of the battery temperature.

[0107] 22) The driving conditions, state variables and decision variables are discretized separately to obtain the state variable set at any stage.

[0108] Specifically, the driving condition is discretized into N stages based on time. For example, if the time interval of the discretization is defined as 1 second, then a driving condition with N seconds will have N stages. bat The state of charge (SOC) and the battery state of charge (SOC) are discretized separately, and the number of discretized grids is a and b respectively. In this way, an N×a×b time-varying state variable grid is formed. The state variable set for any k stage is:

[0109]

[0110] Where: X k represents the set of state variables at stage k; represents a state variable in the kth stage; T bat_i represents the battery temperature at the i-th grid point; SOC j represents the battery state of charge at the jth grid point.

[0111] In this embodiment, the state quantity and control quantity are divided into grids. When the state quantity electric vehicle battery SOC increases from 0 to 100, the number of grids is 100; the state quantity battery temperature T bat From 32℃ to 36℃, the number of grids is 1000; the control variable Q bc From 0w to 1000w, the number of grids is 5.

[0112] 23) Reverse solve the optimal objective function and corresponding decision variables at each stage.

[0113] The reverse solution is to calculate the optimal objective function and decision variables for each stage from the final stage to the initial stage. The reverse solution of dynamic programming regards the cost of the final stage as 0 and reversely solves the optimal objective function of each stage starting from the N-1 stage.

[0114] When k=N-1 stage, the objective function is:

[0115]

[0116] When k∈[1,…,N―2] stage, the objective function is:

[0117]

[0118] Where: F(·) is the state transfer function.

[0119] Specifically, when k=N-1 stage, all discrete decision variables Q bc Acting on each state variable battery temperature T bat and battery SOC, calculate the optimal objective function, and record the corresponding decision variable sequence, recorded as u(N―1).

[0120] When stage k∈[1,…,N―2], all decision variables are traversed and the objective function is calculated for all state variable grid points. Unlike stage k=N―1, the objective function includes not only the objective function of stage k but also the optimal objective function of stage k+1. This allows the optimal objective function of the entire stage to be calculated, and also the optimal objective function of the system at that stage. After the calculation, the sequence of decision variables used must be recorded, denoted as u(k).

[0121] 24) Given the initial state variables, forward solve the global optimal solution.

[0122] Forward calculation is to first give the initial value of the state variable, use the optimal decision variable obtained by reverse calculation, calculate from the initial stage to the final stage, and finally obtain the global optimal state variable sequence. Specifically, the method of forward solving the global optimal solution is:

[0123] When k=1, according to the result of the reverse solution, find the decision variable u(1) of the initial state, make it act on the state variable, and use the state transfer equation to calculate the battery temperature T of the next stage. bat And the battery state of charge SOC. In this embodiment, the initial state variable battery temperature T bat (0) is 35°C and the battery SOC(0) is 0.9.

[0124] When k∈[2,…,N―1] stage, the state variable T of the current stage is calculated according to the state transfer equation of the previous stage. bat (k) and SOC(k), find the corresponding optimal decision variable; if the optimal decision variable found is not the state variable of the current stage, the interpolation method is used to obtain the optimal decision variable u(k) of the current stage; this is gradually recursively carried out to the final stage to obtain the optimal control sequence {u* (1),u * (2),...,u * (k)} and the optimal state sequence {x * (1),x * (2),...,x * (k)}, that is, the optimal battery temperature trajectory and optimal battery state trajectory SOC * .

[0125] In this embodiment, the battery temperature T bat and battery state of charge SOC as state variables, with battery circuit cooling capacity Q bc (k) is used as the control variable, and the state transfer equation is obtained as follows:

[0126]

[0127] Among them: U oc is the external voltage; P bat Indicates battery power; P hp is the energy consumption of the thermal management system, which is determined by the vehicle speed v and the battery circuit cooling capacity Q bc The numerical values ​​of the two are obtained by looking up the table, and the MAP diagram is obtained by high-simulation model identification; R bat is the internal resistance; C bat is the total charge of the battery; The heat generated inside the battery pack; The heat transferred from the refrigerant to the battery pack; m bat is the battery mass; C bat is the total charge of the battery; T bat is the battery temperature.

[0128] Step 3: Establish an online thermal management optimization controller

[0129] 31) Constructing a dataset of optimal thermal management strategies under various working conditions

[0130] like Figure 5 As shown in the figure, the offline optimization program is solved for different representative driving cycle data sets to obtain the input and output required for training the neural network. Specifically, first obtain the standard driving cycle data set, including UDDS, NEDC, WLTC and other driving conditions. For each driving cycle of different driving conditions, the dynamic programming optimization problem of electric vehicle battery thermal management is solved and the optimal decision sequence and optimal state sequence of the control variables are obtained. Then, the optimal thermal management strategy data set is constructed based on the driving condition data, the optimal decision sequence and the optimal state sequence. Figure 6 As shown, it is a vehicle speed curve diagram of the vehicle driving cycle (WLTC) selected in this embodiment.

[0131] 32) Training a neural network

[0132] The constructed optimal thermal management strategy dataset is used to train the neural network and construct an online thermal management optimization controller.

[0133] In this embodiment, driving condition data is used as the input of the neural network model, and the optimal state quantity sequence is used as the output of the neural network model. The neural network model is designed and trained using neural network training software. Specifically, in order to train the neural network, this embodiment adopts the Levenberg-Marquardt algorithm because it has the characteristics of fast convergence and robustness; the neural network model uses the mean quadratic error (MSE) as the fitness function. The trained and verified neural network model is used as an intelligent online thermal management controller. By obtaining real-time data of the neural network input, the neural network outputs the optimal trajectory of the battery temperature and the optimal trajectory of the battery SOC, such as Figure 7-8 shown.

[0134] The above embodiments are merely preferred embodiments for the purpose of fully illustrating the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are within the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims.

Claims

1. A method for controlling the battery temperature of an electric vehicle based on adaptive dynamic programming, characterized in that: The steps include: Step 1: Build a control-oriented electric vehicle battery thermal management system model Establish battery model, refrigerant cooling model and battery electrothermal coupling model respectively, and output battery temperature and battery state of charge according to the battery electrothermal coupling model ; Step 2: Establish a global optimization problem for electric vehicle battery thermal management 21) Establish a dynamic programming mathematical model and determine the constraints of the optimization problem; 22) Discretize the driving conditions, state variables, and decision variables to obtain the state variable set at any stage; 23) Inversely solve the optimal objective function and corresponding decision variables at each stage; 24) Given the initial state variables, forward solve the global optimal solution; Step 3: Establish an online thermal management optimization controller 31) Constructing a dataset of optimal thermal management strategies under various working conditions Based on the dynamic programming optimization problem of electric vehicle battery thermal management for different driving conditions, the optimal decision sequence and optimal state sequence of the control variables are obtained, and the optimal thermal management strategy dataset is constructed based on the optimal decision sequence and optimal state sequence; 32) Training a Neural Network The constructed optimal thermal management strategy dataset is used to train a neural network and construct an online thermal management optimization controller. In the step 1, in the refrigerant cooling model, the heat transferred from the refrigerant to the battery pack Expressed as: in: is the heat capacity of the refrigerant; is the refrigerant flow rate flowing into the battery circuit; is the inlet enthalpy of the battery cold plate; is the outlet enthalpy of the battery cold plate; The opening degree of the electronic expansion valve of the battery circuit; is the displacement of the compressor; is the volumetric efficiency of the compressor; is the compressor speed; is the density of the refrigerant; In the step 1, in the battery model, the charge and discharge state Expressed as: in, is the external voltage; Indicates battery power; Energy consumption of thermal management system; is the internal resistance; is the total charge of the battery; In step 1, in the battery electrothermal coupling model, the influence of the heat generated and dissipated by the refrigerant flowing around the battery module on the battery temperature is obtained to obtain the thermodynamic balance equation of the battery electrothermal coupling model: in: is the battery quality; is the total charge of the battery; is the temperature change of the battery; is the battery temperature; The heat generated inside the battery pack; The heat transferred from the refrigerant to the battery pack; is the charge and discharge current; is the internal resistance; is the external voltage; In step 21), the dynamic programming mathematical model is constructed to minimize the cumulative energy consumption of battery thermal management within the global time range while satisfying the system terminal constraints: in: is the optimal objective function; is the objective function; ω1 and ω2 are weight coefficients; Energy consumption of thermal management system; is the battery temperature; is the target battery temperature; and are state variables and control variables respectively, and: The constraints of the optimization problem are: in: The maximum cooling capacity allocated to the battery circuit for the air conditioning system; and For batteries the lower and upper limits of and are the lower and upper limits of the battery temperature; In step 22), the driving condition is discretized into N stages by time, and the battery temperature in the state variable is and battery state of charge Discretize them separately, and the number of discretized grids is a and b respectively; then any The state variable set of the stage is: in: Indicates the The set of state variables of the stage; Indicates the A state variable of the phase; Indicates in Battery temperature at each grid point; Indicates in Battery state of charge at each grid point; In step 23), the optimal objective function of each stage is solved inversely starting from stage N-1; when In the stage, the objective function is: when In the stage, the objective function is: in: is the state transition function; In step 24), the method for forward solving the global optimal solution is: when In the stage, according to the results of the reverse solution, find the decision variables of the initial state , so that it acts on the state variable and uses the state transfer equation to calculate the battery temperature of the next stage and battery state of charge ; when In the current stage, the state variables of the current stage are calculated based on the previous stage and , find the corresponding optimal decision variable; if the optimal decision variable found is not the state variable of the current stage, the interpolation method is used to obtain the optimal decision variable of the current stage ; This is gradually recursively carried out to the final stage to obtain the optimal control sequence and the optimal state sequence , that is, the optimal battery temperature trajectory and optimal battery state trajectory ; Battery temperature and battery state of charge As a state variable, the battery circuit cooling capacity As the control quantity, the state transfer equation is obtained as: in: is the external voltage; Indicates battery power; is the energy consumption of the thermal management system, which is determined by the vehicle speed Battery circuit cooling capacity The values ​​of the two are obtained by looking up the table; is the internal resistance; is the total charge of the battery; The heat generated inside the battery pack; The heat transferred from the refrigerant to the battery pack; is the battery quality; is the total charge of the battery; is the battery temperature.

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