A multi-actuator collaborative control method for a whole vehicle thermal management system of an electric vehicle

Abstract

The application discloses a kind of electric vehicle whole car thermal management system multi-actuator collaborative control methods, comprising: establishing whole car battery management model, the whole car battery management model includes passenger cabin air conditioner model and power battery model;Establish vehicle speed prediction model, and obtain the vehicle speed prediction value;Based on the vehicle speed prediction value, the whole car battery management model is controlled in combination with PID controller and distributed fuzzy predictive controller.The present application adopts distributed model predictive control, compared with traditional centralized model prediction, reduces the requirement to single-chip microcomputer computing capacity, improves production economy.The present application realizes efficient and accurate control to thermal management system actuator, improves comfort and battery safety, reduces system energy consumption, while improving the economy of production.

Description

Technical Field

[0001] This invention belongs to the field of vehicle thermal management technology, and particularly relates to a multi-actuator collaborative control method for the thermal management system of an electric vehicle. Background Technology

[0002] Currently, the focus on electric vehicles is no longer solely on driving range; passenger cabin comfort and battery safety are also receiving increasing attention. High energy consumption of thermal management systems remains a significant challenge in the development of electric vehicles.

[0003] In current thermal management system control, most systems adopt PID control. This control method is mostly independent control of actuators, and the control coordination between actuators is not high. This can lead to problems such as high system energy consumption and unstable temperature control.

[0004] To address this issue, existing technologies have proposed a unified management approach for the battery and passenger compartment. However, the lack of effective linkage control strategies among the actuators leads to high energy consumption in the thermal management system and poor passenger compartment comfort. A few studies have employed centralized model predictive control, which solves some of the aforementioned problems. However, centralized model predictive control places high demands on the computing power of the microcontroller and results in high production costs. Therefore, there is an urgent need for a multi-actuator collaborative control method for the thermal management system of electric vehicles. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a multi-actuator collaborative control method for the thermal management system of an electric vehicle. This method combines model predictive control and PID control to perform collaborative control on different actuators, thereby improving the performance of the thermal management system.

[0006] To achieve the above objectives, this invention proposes a multi-actuator cooperative control method for the thermal management system of an electric vehicle, comprising:

[0007] A vehicle battery management model is established, which includes a passenger compartment air conditioning model and a power battery model.

[0008] Establish a vehicle speed prediction model and obtain predicted vehicle speed values;

[0009] Based on the predicted vehicle speed, the vehicle battery management model is controlled using a PID controller and a distributed fuzzy predictive controller, respectively.

[0010] Optionally, establishing the passenger cabin air conditioning model includes:

[0011] A one-dimensional model of the air conditioning system and a heat load model of the passenger compartment are established. The one-dimensional model and the heat load model of the passenger compartment are coupled using an evaporator module to obtain the air conditioning model of the passenger compartment.

[0012] The one-dimensional model includes a compressor module, a condenser module, and an electronic expansion module, while the thermal load model of the crew compartment includes a crew compartment module.

[0013] Optionally, establishing the power battery model includes:

[0014] Establish a battery electrical characteristic model, and based on the electrical characteristic model, construct a battery heat generation model;

[0015] A battery cooling circuit model is established, and the power battery model is composed based on the battery heat generation model and the battery cooling circuit model.

[0016] Optionally, obtaining the predicted vehicle speed includes:

[0017] Extract historical vehicle speed features and input the historical vehicle speed features into the vehicle speed prediction model to obtain the vehicle speed prediction value;

[0018] The historical vehicle speed characteristics include: historical vehicle speed, historical average vehicle speed, acceleration, average acceleration, idling time ratio, historical speed variance, and historical acceleration variance.

[0019] The predicted vehicle speed value is:

[0020]

[0021] Among them, V car Let ω0 be the predicted vehicle speed. n0 ω1 n1 b0 represents the adjusted weights for the first and second layer networks. n0 b1 n1 To adjust the bias value.

[0022] Optionally, controlling the vehicle battery management model using a PID controller and a distributed fuzzy predictive controller respectively includes:

[0023] The PID controller is used to control the blower, condenser, and water pump respectively, and the control results are obtained.

[0024] The control results and vehicle speed prediction values ​​are used as inputs to the distributed fuzzy predictive controller to achieve coordinated control of the compressor, the first electronic expansion valve, and the second electronic expansion valve.

[0025] Optionally, controlling the compressor includes:

[0026] The compressor speed is controlled by the temperature of the crew compartment and the condenser fan speed, wherein the compressor's state vector x1, control vector u1, disturbance amount v1, and output vector y1 are:

[0027]

[0028] Among them, T air P represents the air temperature. e For the pressure of the evaporator, l e T is the length of the evaporator wall. we P is the temperature of the evaporator wall. c For the pressure of the condenser, l c T is the length of the condenser wall. wc T is the condenser wall temperature. carbin For the crew cabin temperature, N fan_e N is the blower speed. fan_c V is the condenser fan speed. car The speed is the vehicle speed.

[0029] Optionally, controlling the first electronic expansion valve includes:

[0030] The opening degree, state vector, control vector, disturbance amount, and output variable of the No. 1 electronic expansion valve are controlled by the temperature of the crew cabin. Specifically, the state vector x2, control vector u2, disturbance amount v2, and output variable v2 of the No. 1 electronic expansion valve are:

[0031]

[0032] Optionally, controlling the second electronic expansion valve includes:

[0033] The opening degree γ2 of the second electronic expansion valve is controlled by the battery temperature. The state vector x3, control vector u3, disturbance amount v3, and output variable y3 are:

[0034]

[0035] Optionally, coordinated control of the compressor, the first electronic expansion valve, and the second electronic expansion valve includes:

[0036] The dynamic equations of the compressor, the first electronic expansion valve, and the second electronic expansion valve are constructed.

[0037] Solve the dynamic equations to obtain the equilibrium point and achieve coordinated control;

[0038] The dynamic equation is:

[0039]

[0040]

[0041]

[0042] Where F(j1) is the compressor, F(j2) is the first electronic expansion valve, F(j3) is the second electronic expansion valve, j1 is the compressor cost function, j2 is the first electronic expansion valve cost function, j3 is the second electronic expansion valve cost function, w1, w2, w3, w4, w5 and w6 are the influence factors of the control quantity on the system, and T c_g Let be the target temperature of the crew cabin, k be the kth time point, p and c be the iteration numbers, i be the ith time point, and t be the time.

[0043] Compared with the prior art, the present invention has the following advantages and technical effects:

[0044] This invention employs different controllers for coordinated control of different control objectives. PID control is used for the condenser fan, evaporator blower, and electric water pump. The control results are used as disturbance inputs for distributed model predictive control (DMCC) to achieve reasonable control of compressor speed and expansion valve opening. This allows for real-time adjustment of the valve openings when external factors such as vehicle speed cause significant fluctuations, ensuring stable passenger compartment temperature and keeping battery temperature within a safe range while reducing system energy consumption. Furthermore, the use of distributed model predictive control, compared to traditional centralized model predictive control, reduces the computational requirements of the microcontroller, improving production economics. This invention achieves efficient and accurate control of the thermal management system actuators, improving comfort and battery safety, reducing system energy consumption, and simultaneously enhancing production economics. Attached Figure Description

[0045] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0046] Figure 1 This is a flowchart illustrating a multi-actuator collaborative control method for an electric vehicle thermal management system according to an embodiment of the present invention.

[0047] Figure 2 This is a structural diagram of the thermal management system according to an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of a vehicle neural network speed prediction model according to an embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of the vehicle thermal management control strategy according to an embodiment of the present invention. Detailed Implementation

[0050] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0051] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0052] This invention proposes a multi-actuator cooperative control method for the thermal management system of an electric vehicle, such as... Figure 1 As shown, it specifically includes:

[0053] Establish a vehicle battery management model, which includes a passenger compartment air conditioning model and a power battery model;

[0054] Establish a vehicle speed prediction model and obtain predicted vehicle speed values;

[0055] Based on the predicted vehicle speed, the vehicle battery management model is controlled using a PID controller and a distributed fuzzy predictive controller, respectively.

[0056] Specifically, the present invention is based on Figure 2 The thermal management system shown is controlled by a compressor, condenser, fan, evaporator, electronic expansion valve 1, electronic expansion valve 2, plate heat exchanger, electric water pump, expansion tank, low temperature water tank, three-way valve, power battery and other components.

[0057] The air conditioning system regulates the passenger compartment temperature via the evaporator, specifically through a refrigerant loop: compressor-condenser-electronic expansion valve 1-evaporator-compressor. The battery cooling loop can be divided into two paths: one is natural heat dissipation via a water pump-low temperature water tank-expansion tank; the other is heat exchange via a refrigerant loop (compressor-condenser-electronic expansion valve 2-plate heat exchanger-compressor) and a coolant loop (electronic water pump-power battery-plate heat exchanger). This invention primarily considers the second cooling method.

[0058] Furthermore, the establishment of the passenger cabin air conditioning model includes:

[0059] Establish a one-dimensional model of the air conditioning system and a heat load model of the passenger compartment. Use the evaporator module to couple the one-dimensional model and the heat load model of the passenger compartment to obtain the air conditioning model of the passenger compartment.

[0060] The one-dimensional model includes a compressor module, a condenser module, and an electronic expansion module, while the thermal load model of the crew compartment includes a crew compartment module.

[0061] Specifically, compressor module modeling includes:

[0062] d m =η v ·ρs ·N·disp

[0063] Where, η v For volumetric efficiency, d m ρ is the compressor mass flow rate. s Where N is the intake density, N is the compressor speed, and disp is the compressor displacement.

[0064]

[0065] Where, η is For isentropic efficiency, h dis For isentropic exhaust enthalpy, h s For the inhalation enthalpy, h d This is the exhaust enthalpy.

[0066]

[0067] Where τ is For torque, η m For mechanical efficiency.

[0068] Condenser module modeling includes:

[0069] First, the length of the heat exchange section in both zones is calculated, as shown in the following formula:

[0070]

[0071] Where, ρ lc h is the density of the liquid refrigerant. lgc A represents the potential enthalpy released by the phase transition under the current pressure. C The area of ​​the flat tube of the condenser. It is the average steam ratio of the two regions, l c It is the length of the heat exchange section, h in and h out It is the enthalpy value of the inlet and outlet refrigerant, α ic Convection heat transfer coefficient, D ic It is the hydraulic diameter of the flat water pipe, T wc T rc These represent the temperatures of the condenser wall and the refrigerant, respectively.

[0072] According to the law of conservation of mass, the change in refrigerant pressure can be solved using the following formula:

[0073]

[0074] Where, ρ gc L is the flow rate of the gaseous refrigerant. c It is the length of the flat tube, P c It is the pressure of the refrigerant.

[0075] Based on the law of conservation of energy, the condenser wall temperature is analyzed as shown in the following equation:

[0076]

[0077] Among them, C p，wc The specific heat capacity of the condenser wall is m. wc It is the mass of the condenser wall, α ish T is the convective heat transfer coefficient between the refrigerant and the wall. ic It is the refrigerant temperature at the condenser inlet, A oc It is the heat exchange area on the air side, T ac This is the temperature on the air side of the condenser. Where T... ac The calculation method is as follows:

[0078]

[0079] in, C p,air It is the mass flow rate and specific heat capacity of air, T air It is the air temperature, in the formula Related to vehicle speed, the specific relationship is as follows:

[0080]

[0081] Where ρ air V is the air density, and V is the car's speed.

[0082] The modeling of the electronic expansion valve module includes:

[0083]

[0084] in C is the mass flow rate at the outlet of the electronic expansion valve. q Let A be the flow coefficient. v Let ρ be the area of ​​the opening. v For density, P c P e This is due to import and export pressures.

[0085] Evaporator module modeling includes:

[0086]

[0087] Where, ρ le h is the density of the refrigerant in the evaporator. lge A is the latent enthalpy of phase change of the refrigerant in the evaporator. e It is the area of ​​the flattened water pipe on the refrigerant side of the evaporator. It is the average steam ratio, l eIt is the length of the two-phase heat exchange section in the evaporation process, d me For the flow h ge -h ie α represents the enthalpy of imports and exports. ie D is the convective heat transfer coefficient. ie For the hydraulic diameter, T we T re This refers to the temperature of the evaporator wall and the refrigerant.

[0088]

[0089] Where L e It is the length of the evaporator flat tube, ρ ge P is the refrigerant density under the current pressure condition. e It is the pressure of the refrigerant. It is the mass flow rate at the evaporator outlet.

[0090]

[0091] Among them, C p，we The specific heat capacity of the evaporator wall is m. we It is the mass of the evaporator wall, α ish T is the convective heat transfer coefficient between the refrigerant and the wall. ie It is the refrigerant temperature at the evaporator inlet, A oe It is the heat exchange area on the air side, T ae It is the temperature on the air side of the condenser.

[0092] The heat load of the crew compartment module can be composed of four parts, as shown below:

[0093] Q cab =Q rad +Q conv +Q m +Q cool

[0094] Among them, Q cab For the heat load of the crew cabin, Q rad For the heat load of solar radiation, Q conv It is the heat load of convective heat transfer in the vehicle's interior components, Q m It is the heat load of the passengers and the driver themselves, Q cool It refers to the cooling power of the air conditioning system.

[0095] Furthermore, establishing a power battery model includes:

[0096] Establish a battery electrical characteristic model, and based on the electrical characteristic model, construct a battery heat generation model;

[0097] A battery cooling circuit model is established, and a power battery model is formed based on the battery heat generation model and the battery cooling circuit model.

[0098] Specifically, the heat generation model of the power battery is established as follows:

[0099] The battery electrical characteristic model is established using the Thevenin model of the equivalent circuit:

[0100] U bat =U ocv -I bat R0-U p

[0101] Among them, U ocv R0 is the battery voltage, I is the battery internal resistance in ohms, and I is the battery voltage. bat U is the battery current. p This is the polarization voltage of the battery.

[0102] The battery heat generation model is as follows:

[0103]

[0104] Where T represents the battery temperature. U represents the temperature entropy coefficient of the battery. ocv This indicates the open-circuit voltage.

[0105] The battery cooling circuit is as follows:

[0106] Water pump modeling:

[0107] P pump =U pump I pump

[0108] Where P pump For the power of the water pump, I pump This is the voltage of the water pump.

[0109]

[0110] in This represents the flow rate of the water pump.

[0111] Furthermore, the battery cooling circuit is composed of a compressor, condenser, second electronic expansion valve, plate heat exchanger, compressor circuit, electronic water pump, and power battery heat exchanger coolant circuit.

[0112] Furthermore, obtaining the predicted vehicle speed includes:

[0113] Extract historical vehicle speed features and input them into the vehicle speed prediction model to obtain predicted vehicle speed values.

[0114] Historical vehicle speed characteristics include: historical vehicle speed, historical average vehicle speed, acceleration, average acceleration, idling time ratio, historical speed variance, and historical acceleration variance;

[0115] Specifically, such as Figure 3 As shown, the BP neural network consists of an input layer, a hidden layer, and an output layer. This invention uses a two-layer neural network model to train historical data, and obtains a vehicle speed prediction model by extracting and training historical vehicle speed features.

[0116] The historical vehicle speed features are extracted and used as the input vector X, as shown below:

[0117]

[0118] Among them, v p Indicates historical vehicle speed. The speed represents the historical average vehicle speed, and 'a' represents acceleration. Represents average acceleration, t i Indicates the proportion of idling time. Represents the historical velocity variance. This represents the historical acceleration variance.

[0119] The model outputs the vehicle speed V. car Since the model used in this invention has a two-layer structure, the output can be expressed as follows:

[0120]

[0121] Where ω0 n0 ω1 n1 b0 represents the adjusted weights for the first and second layer networks. n0 b1 n1 To adjust the bias values, the neural network automatically adjusts the weights during training to obtain accurate speed predictions.

[0122] Furthermore, the vehicle battery management model is controlled using a PID controller and a distributed fuzzy predictive controller, respectively, including:

[0123] The blower, condenser, and water pump are controlled separately using a PID controller, and the control results are obtained.

[0124] The control results and vehicle speed prediction values ​​are used as inputs to a distributed fuzzy predictive controller to achieve coordinated control of the compressor, the first electronic expansion valve, and the second electronic expansion valve.

[0125] Specifically, such as Figure 4 As shown, a PID controller is used to control the blower and condenser fan;

[0126] PID control takes the difference e(t) between the target value r(t) and the current state value y(t) as input and passes it to the controller. The controller then calculates the output value based on e(t), as shown below:

[0127] e(t) = r(t) - y(t)

[0128]

[0129] Where u(t) is the system output, k p k i k d These represent the proportional, integral, and derivative coefficients of the controller, respectively.

[0130] When performing PID control on the blower, the temperature of the passenger compartment is used as the current state value y(t), the target temperature of the passenger compartment is used as the target value r(t), and the air volume is used as the output u(t) for control.

[0131] When performing PID control on the condenser fan speed, the condenser subcooling is used as the current state value y(t), the optimal subcooling of the condenser is used as the target value r(t), and the fan speed is used as the output u(t) for control.

[0132] When performing PID control on the water pump speed, the battery temperature is used as the current state value y(t), the optimal battery operating temperature is used as the target value r(t), and the speed is used as the output u(t) for control.

[0133] Furthermore, the distributed model predictive controller can be specifically divided into three controllers: MPC1, MPC2, and MPC3, which respectively control the compressor speed, electronic expansion valve 1 (electronic expansion valve number one), and electronic expansion valve 2 (electronic expansion valve number two). These three controllers need to coordinate with each other, which is achieved using a three-way evolutionary game strategy. Specifically, considering that excessive evaporator airflow can lead to wall icing and insufficient condenser fan airflow can result in poor condensation, if the compressor speed cannot be adjusted in time, it will lead to increased system energy consumption. Therefore, when designing the distributed model predictive controller, the airflow of the condenser fan and blower is considered as system disturbances. In addition, by using distributed model predictive control to coordinate the control of the two electronic expansion valves, even with large fluctuations in vehicle speed, the temperature of the passenger compartment and battery can be kept stable, thus achieving coordinated control between the controllers, as detailed below:

[0134] In this invention, the controlled object of MPC1 is to control the compressor speed through disturbances such as the temperature of the crew compartment and the speed of the condenser fan. The state vector x1, control vector u1, disturbance v1, and output variable y1 are shown below:

[0135]

[0136] Among them, T air P represents the air temperature. e For the pressure of the evaporator, l e T is the length of the evaporator wall. we P is the temperature of the evaporator wall. c For the pressure of the condenser, l c T is the length of the condenser wall. wc T is the condenser wall temperature. carbin For the crew cabin temperature, N fan_e N is the blower speed. fan_c V is the condenser fan speed. car The speed is the vehicle speed.

[0137] The cost function j1 ​​corresponding to controller MPC1 is shown below:

[0138]

[0139] Where w i T is the influence factor of the control quantity on the system. c_g The target temperature for the crew cabin.

[0140] MPC2 controls the opening γ1 of the electronic expansion valve 1 via the crew cabin temperature control. The state vector x2, control vector u2, disturbance amount v2, and output variable y2 are shown below:

[0141]

[0142] The cost function j2 corresponding to controller MPC2 is shown below:

[0143]

[0144] MPC3 controls the opening γ2 of the electronic expansion valve 2 via battery temperature control. The state vector x3, control vector u3, disturbance amount v3, and output variable y3 are shown below:

[0145]

[0146] Among them, P chiller For the pressure of the plate heat exchanger, l chiller T is the length of the plate heat exchanger. Wchiller This refers to the wall temperature of the plate heat exchanger.

[0147] The cost function j3 corresponding to controller MPC3 is shown below:

[0148]

[0149] In the MPC model above, p is the model prediction time domain and c is the control time domain.

[0150] In this invention, a three-way game strategy is used to coordinate the controllers of the aforementioned sub-models, as follows:

[0151] The control variables of the three MPCs act as three game participants, replicating the dynamic equations through the game process, as follows:

[0152]

[0153]

[0154]

[0155] Solving the above dynamic equations for the equilibrium point allows for the coordination of the three MPCs, minimizing their cost function and reducing system energy consumption while ensuring battery safety and passenger cabin temperature comfort.

[0156] J = min(|j1| + |j2| + |j3|)

[0157] Based on step S4 above, the present invention combines a PID controller and a model predictive controller to achieve coordinated control of the various controllers of the electric vehicle thermal management system, thereby reducing the energy consumption of the thermal management system while ensuring the comfort of the passenger compartment temperature and the safety of the battery temperature.

[0158] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-actuator collaborative control method for an electric vehicle thermal management system, characterized in that, include: A vehicle battery management model is established, which includes a passenger compartment air conditioning model and a power battery model. Establish a vehicle speed prediction model and obtain predicted vehicle speed values; Based on the predicted vehicle speed, the vehicle battery management model is controlled by a combination of a PID controller and a distributed fuzzy predictive controller. The control of the vehicle battery management model by combining a PID controller and a distributed fuzzy predictive controller includes: The PID controller is used to control the blower, condenser, and water pump respectively, and the control results are obtained. The control results and vehicle speed prediction values ​​are used as inputs to the distributed fuzzy predictive controller to achieve coordinated control of the compressor, the first electronic expansion valve, and the second electronic expansion valve. Controlling the compressor includes: The compressor speed is controlled by the temperature of the crew compartment and the condenser fan speed, wherein the compressor's state vector... Control vector Disturbance and output vector They are respectively: ； in, For air temperature, For the pressure of the evaporator, The length of the evaporator wall. This refers to the evaporator wall temperature. For the pressure of the condenser, The length of the condenser wall. This refers to the condenser wall temperature. Temperature of the crew cabin. This refers to the blower speed. For condenser fan speed, For vehicle speed; Controlling the No. 1 electronic expansion valve includes: The opening of the No. 1 electronic expansion valve is controlled by the temperature of the crew cabin. The components include a state vector, a control vector, a disturbance quantity, and an output variable, wherein the state vector of the first electronic expansion valve is... Control vector Disturbance and output variables They are respectively: ； Controlling the second electronic expansion valve includes: The opening degree of the No. 2 electronic expansion valve is controlled by the battery temperature. State vector Control vector Disturbance and output variables The state vector of the second electronic expansion valve Control vector Disturbance and output variables for: ； in, For the pressure of the plate heat exchanger, The length of the plate heat exchanger. The wall temperature of the plate heat exchanger; Coordinated control of the compressor, electronic expansion valve No. 1, and electronic expansion valve No. 2 includes: The dynamic equations of the compressor, the first electronic expansion valve, and the second electronic expansion valve are constructed. Solve the dynamic equations to obtain the equilibrium point and achieve coordinated control; The dynamic equation is as follows: in, For compressors, This is the No. 1 electronic expansion valve. This is the No. 2 electronic expansion valve. For the compressor cost function, The cost function of the first electronic expansion valve. The cost function for the second electronic expansion valve. , , , , and To control the influence of variables on the system, Let be the target temperature of the crew cabin, k be the kth time point, p and c be the iteration numbers, i be the ith time point, and t be the time. The air conditioning system regulates the temperature of the passenger compartment through the evaporator. Specifically, the refrigerant passes through the compressor-condenser-electronic expansion valve No. 1-evaporator-compressor loop. The battery cooling loop exchanges heat with the coolant loop of the electric water pump-power battery-plate heat exchanger through the refrigerant loop of the compressor-condenser-electronic expansion valve No. 2-plate heat exchanger.

2. The multi-actuator collaborative control method for an electric vehicle thermal management system according to claim 1, characterized in that, Establishing the crew cabin air conditioning model includes: A one-dimensional model of the air conditioning system and a heat load model of the passenger compartment are established. The one-dimensional model and the heat load model of the passenger compartment are coupled using an evaporator module to obtain the air conditioning model of the passenger compartment. The one-dimensional model includes a compressor module, a condenser module, and an electronic expansion module, while the thermal load model of the crew compartment includes a crew compartment module.

3. The multi-actuator coordinated control method for an electric vehicle thermal management system according to claim 1, characterized in that, Establishing the power battery model includes: Establish a battery electrical characteristic model, and based on the battery electrical characteristic model, construct a battery heat generation model; A battery cooling circuit model is established, and the power battery model is composed based on the battery heat generation model and the battery cooling circuit model.

4. The multi-actuator collaborative control method for an electric vehicle thermal management system according to claim 1, characterized in that, Obtaining the predicted vehicle speed includes: Extract historical vehicle speed features and input the historical vehicle speed features into the vehicle speed prediction model to obtain the vehicle speed prediction value; The historical vehicle speed characteristics include: historical vehicle speed, historical average vehicle speed, acceleration, average acceleration, idling time ratio, historical speed variance, and historical acceleration variance. The predicted vehicle speed value is: in, This is the predicted vehicle speed. , These are the adjustment weights for the first and second layers of the network, respectively. , These are the values ​​to be adjusted for the bias.

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