Battery thermal management optimal control method, device, electronic device and storage medium

Abstract

The embodiments of the present application provide a battery thermal management optimal control method, device, electronic device, and storage medium, relating to the field of battery system thermal management technology. The method uses a pre-trained proxy model to determine whether there is a water inlet temperature range that satisfies the constraint conditions for any water inlet flow rate; if so, it uses a binary search method to iteratively optimize and obtain the optimal control parameters for the water inlet flow rate and water inlet temperature. The method can optimally control the water inlet flow rate and water inlet temperature of the battery thermal management system to reduce energy consumption and improve endurance. It solves the problem that the existing method uses an unreasonable combination of water inlet flow rate and water inlet temperature, which causes unnecessary waste of energy consumption for the entire vehicle, thereby hindering the improvement of the endurance of the electric vehicle.

Description

Technical Field

[0001] The present application relates to the technical field of battery system thermal management, and more specifically, to a battery thermal management optimal control method, device, electronic device, and storage medium. Background Art

[0002] Reducing energy consumption is an important aspect of improving the endurance of electric vehicles. The energy consumption of existing electric vehicles under various operating conditions is directly related to the battery thermal management system, and the effective operation of the battery thermal management system is regulated by the water inlet flow and water temperature of the liquid cooling system. Existing battery thermal management systems adjust the water inlet flow and water inlet temperature so that the monitored physical quantities of the liquid-cooled battery system under various operating conditions (such as heating time and maximum temperature difference of the battery cell under low-temperature heating conditions) meet the target requirements. However, an unreasonable combination of water inlet flow and water inlet temperature will cause unnecessary waste of energy consumption for the entire vehicle, which is not conducive to improving the endurance of electric vehicles. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a battery thermal management optimal control method, device, electronic device and storage medium, which can optimally control the water inlet flow and water inlet temperature of the battery thermal management system to reduce energy consumption and improve endurance. It solves the problem that the existing method uses an unreasonable combination of water inlet flow and water inlet temperature, which causes unnecessary waste of energy consumption of the entire vehicle and is not conducive to improving the endurance of the electric vehicle.

[0004] The present invention provides a method for optimal control of battery thermal management, the method comprising:

[0005] Use the pre-trained proxy model to determine whether any inlet flow rate has an inlet water temperature range that meets the constraints;

[0006] If it exists, the bisection method is used to iteratively search for the optimal control parameters of the inlet water flow rate and inlet water temperature;

[0007] The proxy model is trained by using the water inlet flow and water inlet temperature of the cooling system as input parameters and the battery physical quantities corresponding to the constraint conditions as output parameters.

[0008] In the above implementation process, the proxy model is used to obtain the optimal control parameters corresponding to the water inlet flow rate and water inlet temperature, so that the battery system is thermally managed based on the optimal control parameters. While meeting the thermal management requirements, energy consumption can be reduced and the endurance can be improved. This solves the problem that the existing method uses an unreasonable combination of water inlet flow rate and water inlet temperature, which causes unnecessary waste of energy consumption of the entire vehicle, and is not conducive to improving the endurance of electric vehicles.

[0009] Furthermore, the obtaining of optimal control parameters of the water inlet flow rate and water inlet temperature corresponding to the current operating condition based on the battery heating time curve and the maximum temperature difference curve between battery cells includes:

[0010] Determine the water flow range of the cooling system based on the current operating conditions and water pump power;

[0011] Based on the maximum water inlet flow rate in the water inlet flow rate interval, using the agent model to determine whether there is a first water inlet temperature interval that meets the constraint condition;

[0012] Based on the minimum inlet water flow rate in the inlet water flow rate interval, using the agent model to determine whether there is a second inlet water temperature interval that meets the constraint condition;

[0013] If the first inlet water temperature interval exists and the second inlet water temperature interval does not exist, the bisection method is used to iteratively search for the optimal control parameters of the inlet water flow rate and the inlet water temperature;

[0014] The constraints are expressed as follows: the battery heating time and the maximum temperature difference between battery cells are lower than the preset values.

[0015] In the above implementation process, when the inlet water flow rate is respectively the maximum value and the minimum value, the proxy model can be used to determine whether the inlet water temperature range exists. If the first inlet water temperature range exists and the second inlet water temperature range does not exist, the dichotomy method can be used to optimize the first inlet water temperature range to obtain the corresponding optimal control parameters of the inlet water flow rate and the inlet water temperature.

[0016] Furthermore, if the first inlet water temperature interval exists and the second inlet water temperature interval does not exist, an iterative optimization is performed using a bisection method to obtain optimal control parameters of the inlet water flow rate and the inlet water temperature, including:

[0017] If the first water inlet temperature range exists and the second water inlet temperature range does not exist, calculate the maximum water inlet flow rate:

[0018] Q1=(Qmax+Qmin) / 2;

[0019] Among them, Q max Indicates the maximum water inflow rate, Q min Indicates the minimum value of water inflow;

[0020] Taking the larger water inlet flow rate as input of the proxy model, calculating whether a third water inlet temperature range that satisfies the constraint condition exists;

[0021] If the third water inlet temperature range exists, determine whether the cutoff condition for terminating the optimization is met:

[0022] Q i -Q i+1 <D;

[0023] Among them, Q i Indicates the previous maximum inflow rate that meets the conditions, Q i+1 Indicates the current maximum water inflow, and D indicates the preset value;

[0024] If the conditions are met, the optimization is terminated, and the current larger inlet flow rate is taken as the optimal inlet flow rate, and the lower limit of the inlet temperature corresponding to the optimal inlet flow rate is taken as the optimal inlet temperature;

[0025] If not satisfied, update the maximum water inflow:

[0026] Q=(Q i+1 +Qmin) / 2;

[0027] The updated larger water inlet flow rate is reused as the input of the proxy model to calculate whether a water inlet temperature range that meets the constraint conditions exists.

[0028] In the above implementation process, an iterative optimization process using the binary search method is given to obtain the optimal value of the inlet water flow rate and the corresponding optimal value of the inlet water temperature.

[0029] Furthermore, the method further comprises:

[0030] If the third water inlet temperature range does not exist, the maximum water inlet flow rate is updated:

[0031] Q=(Q i +Q i+1 ) / 2;

[0032] The updated larger water inlet flow rate is reused as the input of the proxy model to calculate whether a water inlet temperature range that meets the constraint conditions exists.

[0033] In the above implementation process, if the third inlet water temperature interval does not exist, because the second inlet water temperature interval corresponding to the minimum inlet water flow rate does not exist at this time, the inlet water flow rate can be increased to a larger extent to make it close to the maximum inlet water flow rate to find the existing inlet water temperature interval.

[0034] Furthermore, the method further comprises:

[0035] If the second inlet water temperature range exists, the minimum inlet water flow rate is used as the optimal inlet water flow rate, and the lower limit of the inlet water temperature corresponding to the optimal inlet water flow rate is used as the optimal inlet water temperature.

[0036] In the above implementation process, if the second inlet water temperature range exists, the minimum inlet water flow rate is the optimal value. At this time, the water pump requires the minimum power, which is the most energy-efficient.

[0037] Furthermore, the method further comprises:

[0038] If neither the first inlet water temperature interval nor the second inlet water temperature interval exists, then there is no optimal value of the inlet water flow rate and the optimal value of the inlet water temperature.

[0039] In the above implementation process, if the water inlet temperature ranges corresponding to the maximum and minimum water inlet temperature do not exist, it means that there is no optimal value.

[0040] Furthermore, before the step of using a pre-trained proxy model to obtain, under any operating condition, a battery heating time curve corresponding to different water flow rates that vary with the water temperature, and a maximum temperature difference curve between battery cells corresponding to different water flow rates that vary with the water temperature, the method further includes:

[0041] Generating training data using the calibrated 3D CAE model of the battery system, and dividing the training data into a training set and a validation set. The training data includes battery heating time and maximum temperature difference between cells under different battery states of charge, battery aging states, charge and discharge rates, ambient temperatures, battery external heat transfer coefficients, cooling system water flow rates, and water inlet temperatures.

[0042] The training set is input into the proxy model for training, and the validation set is used for validation.

[0043] In the above implementation process, the calibrated three-dimensional CAE model of the battery system is used to generate training data to train the proxy model. The trained proxy model is obtained and used to obtain the corresponding inlet water temperature range based on the inlet water flow rate and constraint conditions.

[0044] The present application also provides a battery thermal management optimal control device, the device comprising:

[0045] The water inlet temperature interval judgment module is used to use the pre-trained proxy model to determine whether any water inlet flow rate exists in a water inlet temperature interval that meets the constraint conditions;

[0046] The optimization module is used to perform iterative optimization using the binary search method to obtain the optimal control parameters of the inlet water flow rate and inlet water temperature if there is an inlet water temperature range;

[0047] The proxy model is trained by using the water inlet flow and water inlet temperature of the cooling system as input parameters and the battery physical quantities corresponding to the constraint conditions as output parameters.

[0048] In the above implementation process, the proxy model can be used to calculate whether there is a reasonable water inlet temperature range for a certain water inlet flow rate of the liquid cooling system, and the combination of water inlet flow rate and water inlet temperature with the lowest energy consumption can be found through binary iterative optimization, thereby saving energy consumption and improving the vehicle's endurance.

[0049] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned battery thermal management optimal control method.

[0050] An embodiment of the present application further provides a readable storage medium, wherein the readable storage medium stores computer program instructions. When the computer program instructions are read and executed by a processor, the above-mentioned battery thermal management optimal control method is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A flowchart of a battery thermal management optimal control method provided in an embodiment of the present application;

[0053] Figure 2 A schematic diagram showing the relationship between the entropy thermal coefficient and the battery state of charge (SOC) provided in an embodiment of the present application;

[0054] Figure 3 A flowchart of the initial input and optimal determination of the inlet water flow rate and inlet water temperature provided in an embodiment of the present application;

[0055] Figure 4 A flowchart for obtaining the optimal control parameters of the inlet water flow rate and inlet water temperature corresponding to the current working conditions provided in an embodiment of the present application;

[0056] Figure 5 A schematic diagram of a battery heating time curve corresponding to different water flow rates as the water temperature changes, and a maximum temperature difference curve between battery cells corresponding to different water flow rates as the water temperature changes, provided in an embodiment of the present application;

[0057] Figure 6 A flowchart of an iterative update and a binary search method for finding the optimal values ​​of the inlet water temperature and the inlet water flow rate provided in an embodiment of the present application;

[0058] Figure 7 A flowchart for the specific implementation of the battery thermal management optimal control method provided in an embodiment of the present application;

[0059] Figure 8 A training flow chart of the proxy model provided in the embodiment of the present application;

[0060] Figure 9A flowchart for establishing and verifying a three-dimensional CAE model of a battery system according to an embodiment of the present application;

[0061] Figure 10 A schematic diagram of a three-dimensional CAE model of a battery system provided in an embodiment of the present application;

[0062] Figure 11 A schematic diagram illustrating the accuracy verification of the three-dimensional CAE model of the battery system provided in an embodiment of the present application;

[0063] Figure 12 Flowchart of agent model training and verification provided in the embodiment of this application;

[0064] Figure 13 A schematic diagram of a DNN neural network modeling provided in an embodiment of the present application;

[0065] Figure 14 Flowchart of proxy model operation, cloud-based intensive training, and OTA upgrade provided in the embodiment of this application;

[0066] Figure 15 A structural block diagram of a battery thermal management optimal control device provided in an embodiment of the present application;

[0067] Figure 16 This is a structural block diagram of another battery thermal management optimal control device provided in an embodiment of the present application.

[0068] icon:

[0069] 100 - water inlet temperature interval judgment module; 200 - optimization module; 110 - water inlet flow interval determination module; 120 - first water inlet temperature interval judgment module; 130 - second water inlet temperature interval judgment module; 140 - first optimization module; 150 - second optimization module; 160 - third optimization module; 300 - model training module. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0071] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0072] Example 1

[0073] Please see Figure 1 , Figure 1A flow chart of a battery thermal management optimal control method provided in an embodiment of the present application. The water inlet flow rate and water inlet temperature required for the battery physical quantities (such as the heating time and the maximum temperature difference of the battery cell under low-temperature heating conditions) under various operating conditions to meet the target requirements (such as the constraints described below) are an interval range, that is, as long as the water inlet flow rate and the water inlet temperature are within the corresponding interval range, they can meet the thermal management requirements of the battery. However, within the interval range, different water inlet flow rates and water inlet temperatures consume different amounts of energy. For example, if both the maximum and minimum water inlet flow rates are used to meet the thermal management requirements, the water pump power required for the minimum water inlet flow rate is also the smallest, so the least energy can be consumed. Compared with other water inlet flow rates within the interval range, unnecessary energy waste can be reduced. At this time, the minimum water inlet flow rate is the optimal value sought by this application.

[0074] However, in actual application, the optimal value of the inlet flow rate is often a value between the maximum inlet flow rate and the minimum inlet flow rate, rather than the minimum inlet flow rate. In this case, the optimal value of the inlet flow rate can be obtained by binary search.

[0075] Therefore, by obtaining the optimal control parameters of the water inlet flow rate and water inlet temperature within the interval range, the thermal management system can achieve the target requirements based on the minimum energy consumption, achieve the purpose of saving energy, and thus improve the endurance.

[0076] The method specifically comprises the following steps:

[0077] Step S100: using a pre-trained proxy model to determine whether any inlet water flow rate has an inlet water temperature range that satisfies the constraint conditions;

[0078] Step S200: If so, perform iterative optimization using a binary search method to obtain the optimal control parameters of the inlet water flow rate and the inlet water temperature;

[0079] The proxy model is trained using the cooling system's water inlet flow and temperature as input parameters and the battery physical quantities corresponding to the constraints as output parameters.

[0080] In one embodiment, for example, under low-temperature heating conditions, the battery physical quantities may be heating time and maximum inter-cell temperature difference. A pre-trained proxy model is used to obtain battery heating time curves corresponding to different water flow rates as the water temperature changes, as well as maximum inter-cell temperature difference curves corresponding to different water flow rates as the water temperature changes. Input parameters for training the proxy model include, but are not limited to, the cooling system's water flow rate and water temperature, while the battery heating time and maximum inter-cell temperature difference can be used as output parameters.

[0081] The proxy model that can be used in this application obtains the battery heating time curve corresponding to different water inlet flow rates that change with the water inlet temperature, as well as the maximum temperature difference curve between battery cells corresponding to different water inlet flow rates that change with the water inlet temperature. By inputting a certain water inlet flow rate into the proxy model, it can be determined whether there is a reasonable water inlet temperature range that meets the battery heating time and the maximum temperature difference of the battery cell. If so, the optimal value of the water inlet flow rate that meets the water inlet temperature range can be obtained through iterative optimization using binary search based on the water inlet flow rate, and then the corresponding optimal value of the water inlet temperature can be obtained based on the optimal water inlet flow rate.

[0082] Example 2

[0083] An embodiment of the present application provides a battery thermal management optimal control method. Based on Example 1, as one implementation method, the input parameters of the training agent model also include battery state of charge, battery aging state, charge and discharge rate, ambient temperature, and battery external heat exchange coefficient.

[0084] The trained proxy model can be embedded into the control software of the BMS (Battery Management System). Under specific operating conditions, the battery system's parameters, such as battery state of charge, battery aging, charge and discharge rate, ambient temperature, and battery external heat transfer coefficient, are non-adjustable parameters. However, the cooling system's inlet water flow rate and inlet water temperature are adjustable parameters, as follows:

[0085] When the battery system is in operation, if current flows through the circuit, each cell will generate heat. The heat generation power q is determined by the Bernadi model and is expressed as follows:

[0086]

[0087] Where I represents the charge and discharge current, R is the internal resistance of the battery, and T is the battery temperature. The entropy thermal coefficient is the current I in the battery system. It is related to the charge rate C and can be read in real time from the BMS system. The temperature T can be collected in real time by the battery NTC sensor. The internal resistance R is an inherent physical property of the battery. The quantitative relationship between the internal resistance and the temperature T, the battery state of charge and the aging state SOH is usually calibrated before the battery is offline. When the value is actually called, it is read in the form of a real-time table lookup. The battery is obtained through OCV test when it leaves the factory, usually The relational expression of Figure 2 The figure shows the relationship between the entropy thermal coefficient and the battery state of charge (SOC). When reading the entropy thermal coefficient, it is usually read by looking up a table.

[0088] In addition to heat generation, ambient temperature is determined by the environment in which the battery system operates. Temperature sensors can be deployed to measure real-time data. The external heat transfer coefficient represents heat transfer capacity and is an inherent physical property of the material.

[0089] Taking low-temperature heating conditions as an example, the battery system is evaluated from the perspective of heat generation and heat exchange. Under low-temperature heating conditions, the independent control parameters mainly include seven factors. Specifically, the heating time Δt and the maximum temperature difference ΔT between the battery cells max Mainly related to battery charge state SOC, battery aging state SOH, charge rate C, ambient temperature T amb , battery external heat transfer coefficient h, cooling system water flow Q fiow and water inlet temperature T inlet Related, that is:

[0090]

[0091] Among them, the first two variables SOC and SOH are related to the usage status of the battery itself, C and T amb It is related to the working condition of the battery, h is related to the external heat exchange factor of the battery, and the water flow rate Q of the cooling system fiow and water inlet temperature T inlet Related to the battery cooling system. The first five factors are related to the battery status or usage environment and are generally not controlled by the battery thermal management strategy. The last two factors are related to the cooling system and are controlled by the battery thermal management strategy.

[0092] Since the water inflow and water temperature are controlled by the battery BMS system, and the cooling system water inflow and water temperature have a greater impact on the battery temperature distribution. Therefore, the battery BMS system controls the cooling system water inflow and water temperature to make the cell heating time Δt and the single cell maximum temperature T max The maximum temperature difference ΔT between the battery and the max Satisfy a certain preset constraint that meets safety standards.

[0093] Taking the low-temperature heating condition of the battery system as an example, its safety standard is the monitored battery physical quantity, namely the heating time Δt and the maximum temperature difference ΔT between the cells. max Are lower than a preset value, the expression is as follows:

[0094]

[0095] Where ΔT max =MAX{|T i -T j |},(i,j∈N), where N is the total number of cells in the battery system.

[0096] In one implementation, if the current operating condition is low-temperature heating mode, the battery system's thermal management control strategy adjusts the cooling system's inlet water flow and temperature to meet the control conditions for heating time and maximum cell temperature difference (expressed above), regardless of whether the selected inlet water flow and temperature represent the system's minimum energy consumption. In other words, existing BMS control systems are unable to find the optimal control parameters for inlet water temperature and flow.

[0097] Therefore, when performing thermal management on the battery, the present application can achieve optimal control of the water inlet flow rate and water inlet temperature through the proxy model, so that when the control conditions of the heating time and the maximum temperature difference of the battery cell are met, the required water inlet flow rate and water inlet temperature are the lowest energy consumption, thereby achieving the purpose of saving energy.

[0098] In the process of optimizing the inlet flow rate and inlet water temperature, the maximum and minimum inlet water flow rates can be obtained based on the upper and lower limits of the water pump power. If the minimum inlet water flow rate exists in an inlet water temperature range that meets the constraints, then the minimum inlet water flow rate is the optimal inlet water flow rate, because at this time the pumping flow rate is the smallest and the energy is the most energy-efficient. Therefore, the optimization process can be divided into three cases: initially, the maximum inlet water flow rate input has a corresponding inlet water temperature range and the minimum inlet water flow rate does not. In this case, it is necessary to continue iterative optimization; the minimum inlet water flow rate has a corresponding inlet water temperature range; and the maximum and minimum inlet water flow rates do not have a corresponding inlet water temperature range. Please refer to Figure 3 , Figure 3 This is the initial input and optimal judgment flow chart of the inlet water flow rate and inlet water temperature.

[0099] For the first case: see Figure 4 , Figure 4 The flowchart of obtaining the optimal control parameters of the inlet water flow rate and inlet water temperature corresponding to the current working conditions provided in the embodiment of the present application is based on Example 1 and specifically includes the following steps:

[0100] Step S210: determining the water inlet flow range of the cooling system based on the current working conditions and water pump power;

[0101] Step S220: Based on the maximum inlet flow rate of the inlet flow rate interval, using the proxy model to determine whether there is a first inlet water temperature interval that meets the constraint condition;

[0102] Step S230: Based on the minimum inlet water flow rate in the inlet water flow rate interval, using the proxy model to determine whether there is a second inlet water temperature interval that meets the constraint condition;

[0103] Step S240: If the first inlet water temperature range exists and the second inlet water temperature range does not exist, iterative optimization is performed using a bisection method to obtain optimal control parameters of the inlet water flow rate and the inlet water temperature;

[0104] The constraints are expressed as follows: the battery heating time and the maximum temperature difference between battery cells are lower than the preset values.

[0105] For example, under low-temperature heating conditions (ambient temperature, such as -15°C), the cooling system's water inlet flow rate range is determined based on the water pump power. The maximum water inlet flow rate is the liquid cooling flow rate driven by the water pump's maximum power; conversely, the minimum water inlet flow rate is the liquid cooling flow rate driven by the water pump's minimum power. For example, the water inlet flow rate is affected by the water pump power, with a flow rate range of [6, 14] L / min. Similarly, the water inlet temperature is affected by the PTC heating power, with the water temperature range being [24, 56]°C.

[0106] For example, please refer to the judgment of whether the first water inlet temperature range exists. Figure 5 , Figure 5 Schematic diagrams of battery heating time and maximum inter-cell temperature difference curves for different water flow rates as they vary with water temperature. Specifically, the battery heating time and maximum inter-cell temperature difference curves are generated when different water flow rates and temperatures are input into the proxy model.

[0107] For example, when the maximum water flow rate is 14L / min, the critical point for the heating time to meet Δt≤50min is Figure 5 A1 in the figure means that the water inlet temperature is greater than the water inlet temperature corresponding to A1 (about 36.5℃) to meet the heating time requirement; on the other hand, the maximum temperature difference ΔT max The critical point of ≤6℃ is A2, that is, the inlet water temperature must be lower than the inlet water temperature corresponding to point A2 (about 45.5℃) to meet the maximum temperature difference requirement. Figure 5 It can be found that under the condition of 14L / min water flow, the water inlet temperature needs to be set within the temperature range of [36.5, 45.5]℃ corresponding to A1 and A2 to simultaneously meet the heating time Δt≤50min and the maximum temperature difference ΔT max Thermal management requirement of ≤6°C. Therefore, there is a first inlet water temperature range of [36.5, 45.5]°C.

[0108] For example, when the water flow rate adopts the minimum water flow rate of 6L / min, the critical point for the heating time to meet Δt≤50min is on D1 ( Figure 5 ), that is, the water inlet temperature must be greater than the water inlet temperature corresponding to point D1 (about 42.5℃) to meet the heating time requirement; on the other hand, the maximum temperature difference ΔTmax The critical point of ≤6℃ is D2( Figure 5 ), that is, the water inlet temperature must be lower than the water inlet temperature corresponding to point D2 (about 37.5℃) to meet the maximum temperature difference requirement. Figure 5 It can be found that under the condition of 6L / min water flow, the water temperature must be >42.5℃ and <37.5℃ at the same time to meet the heating time Δt≤50min and the maximum temperature difference ΔT max ≤6℃. Obviously, there is no reasonable range of inlet water temperature that satisfies both constraints under the low-temperature heating condition, so there is no second inlet water temperature range.

[0109] At this time, the first inlet water temperature interval exists and the second inlet water temperature interval does not exist. It is necessary to continue to search for the optimal value using the binary search method to obtain the optimal inlet water flow rate. The optimal inlet water flow rate here refers to the minimum inlet water flow rate that meets the condition of the existence of the first inlet water temperature interval. Then, the corresponding optimal inlet water temperature value is obtained based on the optimal inlet water flow rate.

[0110] Please see Figure 6 , Figure 6 The flowchart of performing a bisection method to find the optimal values ​​of the inlet water temperature and the inlet water flow rate for iterative update includes the following steps:

[0111] Step S241: If the first water inlet temperature range exists and the second water inlet temperature range does not exist, calculate the maximum water inlet flow rate:

[0112] Q1=(Qmax+Qmin) / 2;

[0113] Among them, Q max Indicates the maximum water inflow rate, Q min Indicates the minimum value of water inflow;

[0114] Step S242: using the larger inlet water flow rate as the input of the proxy model to determine whether a third inlet water temperature range that meets the constraint conditions exists;

[0115] Step S243: If the third water inlet temperature range exists, determine whether the cutoff condition for terminating the optimization search is met:

[0116] Q i -Q i+1 <D;

[0117] Among them, Q i Indicates the previous maximum inflow rate that meets the conditions, Q i+1 Indicates the current maximum water inflow, and D indicates the preset value;

[0118] Step S244: If the conditions are met, the optimization is terminated, and the current larger inlet flow rate is used as the optimal inlet flow rate, and the lower limit of the inlet temperature corresponding to the optimal inlet flow rate is used as the optimal inlet temperature;

[0119] Step S245: If not satisfied, update the larger water inflow rate and execute step S242:

[0120] Q=(Q i+1 +Qmin) / 2;

[0121] The updated larger inlet water flow rate is reused as the input of the proxy model to calculate whether the inlet water temperature range that meets the constraints exists.

[0122] Step S246: If the third inlet water temperature range does not exist, update the larger inlet water flow rate and execute step S242:

[0123] Q=(Q i +Q i+1 ) / 2;

[0124] The updated larger water inlet flow rate is reused as the input of the proxy model to calculate whether a water inlet temperature range that meets the constraint conditions exists.

[0125] In the second case, the minimum inlet flow rate has a corresponding inlet water temperature range:

[0126] If the second inlet water temperature range exists, the minimum inlet water flow rate is taken as the optimal inlet water flow rate, and the lower limit of the inlet water temperature corresponding to the optimal inlet water flow rate is taken as the optimal inlet water temperature.

[0127] If the second inlet water temperature range exists, that is, the minimum inlet water flow rate exists in an inlet water temperature range that meets the constraint conditions, and the minimum inlet water flow rate, that is, the power required by the water pump to pump the minimum inlet water flow rate, is the minimum, because pumping a small flow rate is more energy-efficient. Therefore, at this time, regardless of whether the first inlet water temperature range exists, there is no need to continue optimizing, and the minimum inlet water flow rate can be used as the optimal inlet water flow rate.

[0128] In the third case, there is no corresponding water inlet temperature range for both the maximum and minimum water inlet flow rates:

[0129] If neither the first inlet water temperature interval nor the second inlet water temperature interval exists, then there is no optimal value of the inlet water flow rate and the optimal value of the inlet water temperature, and the optimization search can be terminated.

[0130] In summary, by deploying the proxy model to the BMS system, it is possible to calculate in real time whether a reasonable inlet water temperature range exists for a certain inlet water flow rate in the cooling system. If a reasonable inlet water temperature range exists, the inlet water flow rate and inlet water temperature combination with the lowest energy consumption can be found through iterative optimization using the binary search method, thereby improving the vehicle's endurance. After analysis and comparison, the use of this battery thermal management optimal control method under the CLTC endurance condition (China Light Vehicle Test Cycle) can improve the vehicle's endurance by more than 12%.

[0131] The method also includes using the calibrated three-dimensional CAE (Computer-aided engineering) model of the battery system to train the agent model and conduct enhanced training and upgrades after it is put into use. Figure 7 , Figure 7 This is a flowchart for the specific implementation of the optimal control method for battery thermal management. Figure 8 , Figure 8 The training flow chart of the proxy model includes the following steps:

[0132] Step S310: Generate training data using the calibrated three-dimensional CAE model of the battery system, and divide the training data into a training set and a validation set. The training data includes battery heating time and maximum temperature difference between battery cells under different battery states of charge, battery aging states, charge and discharge rates, ambient temperatures, battery external heat transfer coefficients, cooling system water flow rates and water temperatures;

[0133] Step S320: Input the training set into the proxy model for training, and use the validation set for validation.

[0134] Please see Figure 9 , Figure 9 The following is a flow chart for establishing and verifying a 3D CAE model of a battery system. First, the establishment and verification of a 3D CAE model of a battery system specifically includes the following steps:

[0135] Step S311: Obtain a detailed battery system digital model (CAD model) and perform geometric cleaning on the battery system digital model;

[0136] Step S312: Grid division, setting corresponding boundary conditions (such as ambient temperature, heat transfer coefficient, battery operating conditions, etc.), thermal resistance parameters, etc., and establishing the corresponding battery system simulation analysis CAE model, please refer to Figure 10 , Figure 10 It is a schematic diagram of the 3D CAE model of the battery system, and outputs the temperature distribution and temperature rise curve of each cell in the battery system;

[0137] Step S313: Calibrate the battery system simulation analysis CAE model: After the battery system CAE model is established, the analysis results need to be compared with the temperature data collected by the test. Figure 11 , Figure 11 This diagram shows the accuracy verification of a 3D CAE model for a battery system. This diagram is used to verify the accuracy of the CAE analysis model. If the analysis results differ significantly from the test results, the model parameters must be adjusted to ensure the analysis accuracy meets the requirements.

[0138] Step S314: The 3D CAE model, calibrated with the test results, can be used to analyze the battery heating time and maximum temperature difference under different charging rates, heat transfer coefficients, ambient temperatures, battery states of charge (SOC), battery states of aging (SOH), water flow rates, and water temperatures. The 3D CAE model generates sufficient data to serve as a dataset for subsequent proxy model training.

[0139] Please see Figure 12 , Figure 12 This is a flowchart for agent model training and verification, specifically:

[0140] Due to the long calculation time of the three-dimensional CAE model, only a limited number of levels of input parameter analysis can be performed. The input parameters are usually analyzed by using a 3D battery thermal model to analyze the sensitivity of each input parameter to the results, and then different input levels are designed for different input parameters based on the sensitivity ranking. In the DOE experimental design, 4-5 input levels are generally selected for each parameter, but the input level of input parameters with high sensitivity can be appropriately increased, and the input level of input parameters with low sensitivity can be appropriately reduced. The 3D battery thermal model is run to analyze different experimental designs and generate result data, including the maximum temperature difference and heating time between single cells. The data generated by the 3D battery thermal model is divided into two categories, namely training set and validation set. The training set generally accounts for 80% of the result data and is mainly used to train the proxy model. The remaining result data is classified as the validation set, which is mainly used to verify the effectiveness of the proxy model.

[0141] For example, the proxy model can adopt an artificial neural network model, such as a deep neural network DNN, a convolutional neural network CNN, and a recurrent neural network RNN. Here, DNN is used as an example. Figure 13 , Figure 13 This is a schematic diagram of a DNN neural network model. The input parameters (x1, x2, x3, x4, x5, x6, x7) include the battery state of charge, battery aging state (SOH), charge and discharge rate, ambient temperature, battery external heat transfer coefficient, cooling system inlet water flow rate, and cooling system inlet water temperature. The neural network model can use multiple hidden layers, each with multiple neurons. Figure 13The figure shows the modeling process with two hidden layers, each containing 14 neurons. The output parameters (y1, y2) are the heating time and the maximum temperature difference between the cells for low-temperature heating conditions.

[0142] For intensive training and upgrades after commissioning, please refer to Figure 14 , Figure 14 This is a flowchart for proxy model operation, cloud-based intensive training, and OTA (Over-The-Air technology) upgrades. Specifically:

[0143] After the agent model is deployed to the BMS control system, it can calculate the optimal water inlet flow and temperature for the liquid cooling system at any time based on the battery's operating status and working conditions.

[0144] The NTC (Negative Temperature Coefficient) temperature sensor deployed in the battery system can collect the temperature data of the battery cells in real time. This data can be uploaded to the cloud and used as calibration and comparison data for the proxy model.

[0145] Data such as the water inlet flow rate of the liquid cooling system, the water inlet temperature control range, the current battery status SOC and SOH, the discharge rate, and the ambient temperature can all be uploaded to the cloud in real time. The cloud can further enhance the training of the agent model based on the original training data and the newly uploaded data during operation.

[0146] The model after intensive training can output the predicted temperature of the NTC collection point and compare it with the measured temperature to further improve the model accuracy.

[0147] When the prediction results of the proxy model are compared with the test data and the accuracy meets the requirements, the proxy model deployed on the vehicle side can be upgraded regularly through OTA, so that the model becomes more accurate and up-to-date with frequent use.

[0148] In summary, the vehicle-side operating data can be uploaded to the cloud in real time, which can continuously strengthen the training model and thus improve the accuracy of the proxy model. The vehicle-side model upgraded through OTA can be used frequently and become more accurate with use.

[0149] Example 3

[0150] The present application embodiment provides a battery thermal management optimal control device, which is applied to the battery thermal management optimal control method described in Examples 1-2. Figure 15 , Figure 15 This is a structural block diagram of a battery thermal management optimal control device, which includes but is not limited to:

[0151] The water inlet temperature interval judgment module 100 is used to use a pre-trained proxy model to determine whether any water inlet flow rate has a water inlet temperature interval that meets the constraint conditions;

[0152] The optimization module 200 is used to perform iterative optimization using a binary search method to obtain the optimal control parameters of the inlet water flow rate and the inlet water temperature if there is an inlet water temperature range;

[0153] The proxy model is trained by using the water inlet flow and water inlet temperature of the cooling system as input parameters and the battery physical quantities corresponding to the constraint conditions as output parameters.

[0154] Please see Figure 16 , Figure 16 This is a structural block diagram of another battery thermal management optimal control device, which specifically includes:

[0155] The water inlet flow interval determination module 110 is used to determine the water inlet flow interval of the cooling system based on the current working conditions and water pump power;

[0156] A first water inlet temperature interval determination module 120 is configured to determine, based on the maximum water inlet flow rate in the water inlet flow rate interval, using the proxy model to determine whether there is a first water inlet temperature interval that satisfies the constraint condition;

[0157] A second water inlet temperature interval determination module 130 is configured to determine, based on the minimum water inlet flow rate in the water inlet flow rate interval, using the proxy model to determine whether there is a second water inlet temperature interval that satisfies the constraint condition;

[0158] A first optimization module 140 is configured to perform iterative optimization using a bisection method to obtain optimal control parameters of the inlet water flow rate and the inlet water temperature if the first inlet water temperature range exists and the second inlet water temperature range does not exist;

[0159] The constraints are expressed as follows: the battery heating time and the maximum temperature difference between battery cells are lower than the preset values.

[0160] Specifically:

[0161] If the first water inlet temperature range exists and the second water inlet temperature range does not exist, calculate the maximum water inlet flow rate:

[0162] Q1=(Qmax+Qmin) / 2;

[0163] Among them, Q max Indicates the maximum water inflow rate, Q min Indicates the minimum value of water inflow;

[0164] Taking the larger water inlet flow rate as input of the proxy model, calculating whether a third water inlet temperature range that satisfies the constraint condition exists;

[0165] If the third water inlet temperature range exists, determine whether the cutoff condition for terminating the optimization is met:

[0166] Q i -Q i+1 <D;

[0167] Among them, Q i Indicates the previous maximum inflow rate that meets the conditions, Q i+1 Indicates the current maximum water inflow, and D indicates the preset value;

[0168] If the conditions are met, the optimization is terminated, and the current larger inlet flow rate is taken as the optimal inlet flow rate, and the lower limit of the inlet temperature corresponding to the optimal inlet flow rate is taken as the optimal inlet temperature;

[0169] If not satisfied, update the maximum water inflow:

[0170] Q=(Q i+1 +Qmin) / 2;

[0171] The updated larger water inlet flow rate is reused as the input of the proxy model to calculate whether a water inlet temperature range that meets the constraint conditions exists.

[0172] If the third water inlet temperature range does not exist, the maximum water inlet flow rate is updated:

[0173] Q=(Q i +Q i+1 ) / 2;

[0174] The updated larger water inlet flow rate is reused as the input of the proxy model to calculate whether a water inlet temperature range that meets the constraint conditions exists.

[0175] The second optimization module 150 is configured to use the minimum inlet water flow rate as the optimal inlet water flow rate and the lower limit of the inlet water temperature corresponding to the optimal inlet water flow rate as the optimal inlet water temperature if the second inlet water temperature range exists.

[0176] The third optimization module 160 is configured to determine that there is no optimal value of the inlet water flow rate and the inlet water temperature if both the first inlet water temperature interval and the second inlet water temperature interval do not exist.

[0177] The device also includes a model training module 300:

[0178] Generating training data using the calibrated 3D CAE model of the battery system, and dividing the training data into a training set and a validation set. The training data includes the battery heating time and the maximum temperature difference between the battery cells at the water inlet flow rate and water inlet temperature of the cooling system.

[0179] The training set is input into the proxy model for training, and the validation set is used for validation.

[0180] The specific training process has been described in detail in the above embodiments and will not be repeated here.

[0181] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned battery thermal management optimal control method.

[0182] An embodiment of the present application further provides a readable storage medium, wherein the readable storage medium stores computer program instructions. When the computer program instructions are read and executed by a processor, the above-mentioned battery thermal management optimal control method is executed.

[0183] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0184] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0185] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0186] The foregoing is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included within the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures.

[0187] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0188] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

Claims

1. A battery thermal management optimal control method, characterized in that: The method comprises: A pre-trained proxy model is used to determine whether any inlet water flow rate falls within an inlet water temperature range that satisfies the constraints. Specifically, the following steps are performed: a cooling system inlet water flow range is determined based on the current operating conditions and water pump power; based on the maximum inlet water flow rate within the inlet water flow range, the proxy model is used to determine whether a first inlet water temperature range that satisfies the constraints; based on the minimum inlet water flow rate within the inlet water flow range, the proxy model is used to determine whether a second inlet water temperature range that satisfies the constraints; if the first inlet water temperature range exists and the second inlet water temperature range does not, an iterative optimization method is used to obtain the optimal control parameters for the inlet water flow rate and inlet water temperature; wherein the constraints are expressed as follows: the battery heating time and the maximum temperature difference between battery cells are both lower than preset values; If it exists, the bisection method is used to iteratively search for the optimal control parameters of the inlet water flow rate and inlet water temperature; The proxy model is trained by using the water inlet flow and water inlet temperature of the cooling system as input parameters and the battery physical quantities corresponding to the constraint conditions as output parameters.

2. The battery thermal management optimal control method according to claim 1, characterized in that: If the first inlet water temperature range exists and the second inlet water temperature range does not exist, iterative optimization is performed using a bisection method to obtain optimal control parameters of the inlet water flow rate and the inlet water temperature, including: If the first water inlet temperature range exists and the second water inlet temperature range does not exist, calculate the maximum water inlet flow rate: Q1=(Qmax+Qmin) / 2; Among them, Q max Indicates the maximum water inflow rate, Q min Indicates the minimum value of water inflow; Taking the larger water inlet flow rate as input of the proxy model, determining whether a third water inlet temperature range that satisfies the constraint condition exists; If the third water inlet temperature range exists, determine whether the cutoff condition for terminating the optimization is met: Q i -Q i+1 <D; Among them, Q i Indicates the previous maximum inflow rate that meets the conditions, Q i+1 Indicates the current maximum water inflow, and D indicates the preset value; If the conditions are met, the optimization is terminated, and the current larger inlet flow rate is taken as the optimal inlet flow rate, and the lower limit of the inlet temperature corresponding to the optimal inlet flow rate is taken as the optimal inlet temperature; If not satisfied, update the maximum water inflow: Q=(Q i+1 +Qmin) / 2; The updated larger water inlet flow rate is reused as the input of the proxy model to calculate whether a water inlet temperature range that meets the constraint conditions exists.

3. The battery thermal management optimal control method according to claim 2, characterized in that: The method further comprises: If the third water inlet temperature range does not exist, the maximum water inlet flow rate is updated: Q=(Q i +Q i+1 ) / 2; The updated larger water inlet flow rate is reused as the input of the proxy model to calculate whether a water inlet temperature range that meets the constraint conditions exists.

4. The battery thermal management optimal control method according to claim 1, characterized in that: The method further comprises: If the second inlet water temperature range exists, the minimum inlet water flow rate is used as the optimal inlet water flow rate, and the lower limit of the inlet water temperature corresponding to the optimal inlet water flow rate is used as the optimal inlet water temperature.

5. The battery thermal management optimal control method according to claim 1, characterized in that: The method further comprises: If neither the first inlet water temperature interval nor the second inlet water temperature interval exists, then there is no optimal value of the inlet water flow rate and the optimal value of the inlet water temperature.

6. The battery thermal management optimal control method according to claim 1, characterized in that: Before the step of using the pre-trained proxy model to determine whether any inlet water flow rate has an inlet water temperature range that satisfies the constraint conditions, the method further includes: Generating training data using the calibrated 3D CAE model of the battery system, and dividing the training data into a training set and a validation set. The training data includes the battery heating time and the maximum temperature difference between the battery cells at the water inlet flow rate and water inlet temperature of the cooling system. The training set is input into the proxy model for training, and the validation set is used for validation.

7. A battery thermal management optimal control device, characterized in that: The device comprises: The inlet water temperature interval judgment module is used to use a pre-trained proxy model to determine whether any inlet water flow rate has an inlet water temperature interval that meets the constraint conditions. Specifically, the module determines the inlet water flow interval of the cooling system based on the current operating conditions and water pump power; uses the proxy model to determine whether a first inlet water temperature interval that meets the constraint conditions is present based on the maximum inlet water flow rate in the inlet water flow interval; and uses the proxy model to determine whether a second inlet water temperature interval that meets the constraint conditions is present based on the minimum inlet water flow rate in the inlet water flow interval. If the first inlet water temperature interval exists and the second inlet water temperature interval does not exist, it uses a binary search method to iteratively optimize and obtain the optimal control parameters for the inlet water flow rate and inlet water temperature. The constraint conditions are expressed as follows: the battery heating time and the maximum temperature difference between battery cells are both lower than preset values. The optimization module is used to perform iterative optimization using the binary search method to obtain the optimal control parameters of the inlet water flow rate and inlet water temperature if there is an inlet water temperature range; The proxy model is trained by using the water inlet flow and water inlet temperature of the cooling system as input parameters and the battery physical quantities corresponding to the constraint conditions as output parameters.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the battery thermal management optimal control method according to any one of claims 1 to 6.

9. A readable storage medium, characterized in that: The readable storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the battery thermal management optimal control method according to any one of claims 1 to 6 is executed.

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