Thermal management control methods, devices, electronic equipment, and storage media for fast charging of electric vehicle batteries

Abstract

This application provides a thermal management control method, apparatus, electronic device, and storage medium for fast charging of electric vehicle batteries. The thermal management control method includes: acquiring the initial charging temperature of the battery, the initial state of charge (SOC) of the battery, and a user-inputted fast charging mode, wherein the fast charging mode includes a fastest charging mode, a lowest charging energy consumption mode, and a best overall charging performance mode; determining a combination of thermal management parameters based on the initial charging temperature, the initial SOC of the battery, and the user-inputted fast charging mode; and controlling the charging process of the battery based on the determined thermal management parameter combination. This application solves the technical problem of failing to meet diverse user charging needs and failing to achieve the expected charging effect.

Description

Technical Field

[0001] This application relates to the field of power battery thermal management technology, and more specifically, to a thermal management control method, device, electronic device, and storage medium for fast charging of electric vehicle batteries. Background Technology

[0002] As a core component of electric vehicles, the power battery pack can only ensure its charging and discharging performance, safety, and lifespan by operating within a suitable temperature range. High battery operating temperatures accelerate battery aging and reduce cycle life. Excessive heat can also cause violent chemical reactions within the battery, leading to expansion, leakage, and smoke; in severe cases, it can even induce thermal runaway, resulting in battery fire and explosion, affecting battery lifespan and safety. Conversely, low battery operating temperatures increase internal resistance and reduce usable capacity, leading to a decrease in low-temperature driving range. Furthermore, low temperatures reduce battery activity, limiting charging and discharging power. In addition, charging the battery at low temperatures can easily cause lithium plating, leading to irreversible lifespan degradation, and may even cause internal short circuits resulting in thermal runaway, severely impacting lifespan and safety. Therefore, effective thermal management control methods are needed to maintain the battery temperature within a suitable range, thus avoiding the adverse effects of excessively high or low temperatures on its charging and discharging performance, safety, and lifespan.

[0003] Currently, thermal management control methods for fast-charging batteries primarily employ PTC heaters and battery coolers to heat and cool the battery respectively. Specifically, when the battery temperature falls below a certain lower heating threshold, the PTC heater is activated to heat the battery; when the battery temperature rises above the upper heating threshold, the PTC heater is deactivated to stop heating. Conversely, when the battery temperature rises above a certain upper cooling threshold, the battery cooler is activated to cool the battery; when the battery temperature falls below the lower cooling threshold, the battery cooler is deactivated to stop cooling. This method aims to maintain the battery temperature within a suitable range and improve charging efficiency. However, this thermal management control method has the following drawbacks:

[0004] On the one hand, current thermal management control methods for fast charging do not differentiate between fast charging modes, resulting in a relatively simple charging mode that cannot achieve different charging effects, including the fastest charging speed, the lowest charging energy consumption, or the best overall charging performance, thus failing to meet the diverse charging needs of users.

[0005] On the other hand, most current thermal management control methods use fixed thermal management parameters to control the fast charging process. However, when the starting temperature and starting state of charge of the battery are different, the corresponding fast charging rate and battery internal resistance are also different, resulting in a large difference in the self-generated heat of the battery. This leads to different heating or cooling requirements for the battery. If fixed thermal management parameters are still used for different starting temperatures and starting state of charge of the battery, the actual heating or cooling requirements of the battery will not be met, resulting in longer charging time or greater charging energy consumption, and the expected charging effect cannot be achieved. Summary of the Invention

[0006] The purpose of this application is to provide a thermal management control method, device, electronic device, and storage medium for fast charging of electric vehicle batteries, in order to solve the technical problem of failing to meet the diverse charging needs of users and failing to achieve the expected charging effect.

[0007] In a first aspect, the present invention provides a thermal management control method for fast charging of an electric vehicle battery, the method comprising:

[0008] The system acquires the starting temperature of battery charging, the starting SOC of the battery charging, and the fast charging mode input by the user, wherein the fast charging mode includes the fastest charging mode, the lowest charging energy consumption mode, and the best overall charging performance mode.

[0009] The combination of thermal management parameters is determined based on the starting temperature of battery charging, the starting state of battery charging, and the fast charging mode input by the user.

[0010] The charging process of the battery is controlled based on the combination of constant thermal management parameters.

[0011] The method of the first aspect of this application can obtain the battery charging start temperature, the battery charging start SOC, and the user-input fast charging mode. The fast charging mode includes a fastest charging speed mode, a lowest charging energy consumption mode, and a best overall charging performance mode. Based on the battery charging start temperature, the battery charging start SOC, and the user-input fast charging mode, a combination of thermal management parameters can be determined, and the battery charging process can be controlled based on this combination of thermal management parameters. Compared with the prior art, this method allows for a charging process that meets different user charging needs by using the user-input fastest charging speed mode, lowest charging energy consumption mode, best overall charging performance mode, and thermal management parameter combination. Furthermore, the method can set a thermal management parameter combination based on the battery charging start temperature and the battery charging start SOC, enabling the charging process based on this combination to be implemented according to these parameters. This allows for the use of corresponding thermal management parameter combinations for different battery charging start temperatures and start SOCs, thereby achieving the desired charging effect.

[0012] In an optional implementation, the method further includes:

[0013] A one-dimensional battery charging simulation analysis model is constructed. The input of the one-dimensional battery charging simulation analysis model is the parameters under the target operating condition. The parameters under the target operating condition include the initial temperature, initial SOC, heating threshold, cooling threshold, target heating water temperature, target cooling water temperature, target heating flow rate, and target cooling flow rate. The output of the one-dimensional battery charging simulation analysis model is the charging energy consumption and charging time.

[0014] Fast charging simulation experiments were conducted on the one-dimensional charging simulation analysis model of the battery and optimized to generate thermal management parameter combination tables corresponding to the fastest charging speed mode, the lowest charging energy consumption mode, and the best overall charging performance mode.

[0015] And, the determination of the thermal management parameter combination based on the battery charging start temperature, the battery charging start SOC, and the user-input fast charging mode includes:

[0016] When the user inputs the fastest charging mode, the thermal management parameter combination table corresponding to the fastest charging mode is queried based on the battery charging start temperature and the battery charging start SOC to obtain the thermal management parameter combination.

[0017] When the fast charging mode input by the user is the lowest charging energy consumption mode, the thermal management parameter combination table corresponding to the lowest charging energy consumption mode is queried based on the starting temperature of the battery charging and the starting SOC of the battery charging to obtain the thermal management parameter combination.

[0018] When the fast charging mode input by the user is the optimal mode for overall charging performance, the thermal management parameter combination table corresponding to the optimal mode for overall charging performance is queried based on the starting temperature of battery charging and the starting SOC of battery charging to obtain the thermal management parameter combination.

[0019] In an optional implementation, after constructing the one-dimensional battery charging simulation analysis model and before conducting fast-charging simulation experiments and optimizing the one-dimensional battery charging simulation analysis model, the method further includes:

[0020] Determine whether the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions. If the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions, then perform a fast charging simulation test and optimize the one-dimensional battery charging simulation analysis model. If the accuracy of the one-dimensional battery charging simulation analysis model does not meet the preset conditions, then calibrate and correct the one-dimensional battery charging simulation analysis model.

[0021] In an optional implementation, the step of performing fast-charging simulation experiments and optimizing the one-dimensional charging simulation analysis model of the battery to generate a thermal management parameter combination table corresponding to the fastest charging speed mode, a thermal management parameter combination table corresponding to the lowest charging energy consumption mode, and a thermal management parameter combination table corresponding to the best overall charging performance mode includes:

[0022] When the one-dimensional battery charging simulation analysis model generates the thermal management parameter combination table corresponding to the fastest charging mode, the optimization algorithm of the one-dimensional battery charging simulation analysis model is a genetic algorithm, the optimization objective is charging time, and the optimization constraints include the maximum battery temperature difference and the maximum battery temperature, wherein the maximum battery temperature is less than or equal to 50°C, and the maximum battery temperature difference is less than or equal to 10°C.

[0023] In an optional implementation, the step of performing fast-charging simulation experiments and optimizing the one-dimensional charging simulation analysis model of the battery to generate a thermal management parameter combination table corresponding to the fastest charging speed mode, a thermal management parameter combination table corresponding to the lowest charging energy consumption mode, and a thermal management parameter combination table corresponding to the best overall charging performance mode includes:

[0024] When the one-dimensional battery charging simulation analysis model generates the thermal management parameter combination table corresponding to the lowest charging energy consumption mode, the optimization algorithm of the one-dimensional battery charging simulation analysis model is a genetic algorithm, the optimization objective is charging energy consumption, and the optimization constraints include the maximum battery temperature difference, the maximum battery temperature, and the charging time, wherein the maximum battery temperature is less than or equal to 50°C, the maximum battery temperature difference is less than or equal to 10°C, and the charging time is less than or equal to 200 min.

[0025] In an optional implementation, the step of performing fast-charging simulation experiments and optimizing the one-dimensional charging simulation analysis model of the battery to generate a thermal management parameter combination table corresponding to the fastest charging speed mode, a thermal management parameter combination table corresponding to the lowest charging energy consumption mode, and a thermal management parameter combination table corresponding to the best overall charging performance mode includes:

[0026] When the one-dimensional battery charging simulation analysis model generates the thermal management parameter combination table corresponding to the optimal mode for comprehensive charging performance, the optimization algorithm of the one-dimensional battery charging simulation analysis model is the second-generation non-dominated sorting genetic algorithm. The optimization objectives are charging time and charging energy consumption, and the optimization constraints include the maximum battery temperature difference and the maximum battery temperature, wherein the maximum battery temperature is less than or equal to 50°C and the maximum battery temperature difference is less than or equal to 10°C.

[0027] In an optional implementation, determining whether the accuracy of the one-dimensional battery charging simulation analysis model meets preset conditions includes:

[0028] Obtain the simulation values ​​of the one-dimensional battery charging simulation analysis model for the simulation conditions, and obtain the test values ​​for the test conditions;

[0029] The test value is compared with the simulation value. If the error between the simulation value and the test value is less than or equal to a preset error threshold, the accuracy of the one-dimensional battery charging simulation analysis model is determined to meet the preset condition. If the error between the simulation value and the test value is greater than the preset error threshold, the accuracy of the one-dimensional battery charging simulation analysis model is determined to not meet the preset condition.

[0030] In a second aspect, the present invention provides a thermal management control device for fast charging of electric vehicle batteries, the device comprising:

[0031] The acquisition module is used to acquire the starting temperature of battery charging, the starting SOC of the battery charging, and the fast charging mode input by the user, wherein the fast charging mode includes the fastest charging mode, the lowest charging energy consumption mode, and the best overall charging performance mode.

[0032] The determination module is used to determine a combination of thermal management parameters based on the starting temperature of battery charging, the starting state of battery charging, and the fast charging mode input by the user.

[0033] The control module is used to control the charging process of the battery based on the combination of constant thermal management parameters.

[0034] Thirdly, the present invention provides an electronic device, comprising:

[0035] Processor; and

[0036] The memory is configured to store machine-readable instructions that, when executed by the processor, perform the thermal management control method for fast charging of electric vehicle batteries as described in any of the foregoing embodiments.

[0037] Fourthly, the present invention provides a storage medium storing a computer program, the computer program being executed by a processor as described in any of the foregoing embodiments, the thermal management control method for fast charging of electric vehicle batteries. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic flowchart of a thermal management control method for fast charging of an electric vehicle battery disclosed in an embodiment of this application;

[0040] Figure 2 This is a schematic diagram of the architecture of a one-dimensional battery charging simulation analysis model disclosed in an embodiment of this application;

[0041] Figure 3 This is a schematic diagram of the structure of a thermal management control device for fast charging of an electric vehicle battery disclosed in an embodiment of this application;

[0042] Figure 4 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. Detailed Implementation

[0043] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0044] Example 1

[0045] Please see Figure 1 , Figure 1This is a flowchart illustrating a thermal management control method for fast charging of an electric vehicle battery disclosed in an embodiment of this application. Figure 1 As shown, the method in this application embodiment includes the following steps:

[0046] 101. Obtain the starting temperature of battery charging, the starting SOC of battery charging, and the fast charging mode input by the user. The fast charging mode includes the fastest charging mode, the lowest charging energy consumption mode, and the best overall charging performance mode.

[0047] 102. Determine the combination of thermal management parameters based on the battery charging start temperature, the battery charging start SOC, and the fast charging mode input by the user;

[0048] 103. Controlling the battery charging process based on a combination of constant thermal management parameters.

[0049] The method in this application embodiment can obtain the battery charging start temperature, the battery charging start SOC, and the user-input fast charging mode. The fast charging mode includes the fastest charging speed mode, the lowest charging energy consumption mode, and the optimal overall charging performance mode. Based on the battery charging start temperature, the battery charging start SOC, and the user-input fast charging mode, a combination of thermal management parameters can be determined, and the battery charging process can be controlled based on this combination of thermal management parameters. Compared with existing technologies, this method allows for a charging process that meets different user charging needs by using the user-input fastest charging speed mode, lowest charging energy consumption mode, optimal overall charging performance mode, and thermal management parameter combination. Furthermore, by setting the thermal management parameter combination based on the battery charging start temperature and the battery charging start SOC, the charging process based on this combination can be implemented according to the battery charging start temperature and the battery charging start SOC. This allows for the use of corresponding thermal management parameter combinations for different battery charging start temperatures and battery charging start SOCs, thereby achieving the desired charging effect.

[0050] In the embodiments of this application, users can select any one of the following modes: the fastest charging speed mode, the lowest charging energy consumption mode, and the best overall charging performance mode, thereby fulfilling the user's desired charging needs. For example, if a user selects the fastest charging speed mode, it can meet the user's need to complete charging as quickly as possible.

[0051] In this embodiment of the application, the starting temperature of battery charging refers to the temperature when the battery just enters the charging state, and the starting SOC of battery charging refers to the SOC when the battery just enters the charging state, where SOC refers to State of Charge (battery state of charge).

[0052] In the embodiments of this application, the thermal management parameter combination refers to a combination of multiple thermal management parameters used to control the battery charging process.

[0053] In this application embodiment, as an optional implementation, the method of this application embodiment further includes:

[0054] A one-dimensional battery charging simulation analysis model is constructed. The input of the one-dimensional battery charging simulation analysis model is the parameters under the target operating condition, which include the initial temperature, initial SOC, heating threshold, cooling threshold, target heating water temperature, target cooling water temperature, target heating flow rate, and target cooling flow rate. The output of the one-dimensional battery charging simulation analysis model is the charging energy consumption and charging time.

[0055] Fast charging simulation experiments were conducted on the one-dimensional battery charging simulation analysis model and optimized to generate thermal management parameter combination tables for the fastest charging mode, the lowest charging energy consumption mode, and the best overall charging performance mode.

[0056] In addition, the combination of thermal management parameters is determined based on the battery charging start temperature, the battery charging start SOC, and the fast charging mode input by the user, including the following sub-steps:

[0057] When the user inputs the fastest charging mode, the thermal management parameter combination table corresponding to the fastest charging mode is queried based on the battery charging start temperature and the battery charging start SOC to obtain the thermal management parameter combination.

[0058] When the user inputs the lowest charging energy consumption mode, the thermal management parameter combination table corresponding to the lowest charging energy consumption mode is queried based on the battery charging start temperature and the battery charging start SOC to obtain the thermal management parameter combination.

[0059] When the user inputs the best overall charging performance mode for fast charging, the thermal management parameter combination table corresponding to the best overall charging performance mode is queried based on the battery charging start temperature and the battery charging start SOC to obtain the thermal management parameter combination.

[0060] In this embodiment of the application, conducting fast charging simulation tests and optimizing the one-dimensional charging simulation analysis model of the battery refers to conducting fast charging simulation tests using the one-dimensional charging simulation analysis model of the battery, and optimizing the results of the fast charging simulation tests so that the results of the fast charging simulation tests are close to the optimization target.

[0061] In this embodiment of the application, as an optional implementation, after constructing the one-dimensional battery charging simulation analysis model and before conducting fast charging simulation experiments and optimizing the one-dimensional battery charging simulation analysis model, the method of this embodiment of the application further includes the following steps:

[0062] Determine whether the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions. If the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions, then perform a fast charging simulation test and optimize the one-dimensional battery charging simulation analysis model. If the accuracy of the one-dimensional battery charging simulation analysis model does not meet the preset conditions, then calibrate and correct the one-dimensional battery charging simulation analysis model.

[0063] In an optional implementation, a fast-charging simulation experiment is conducted on the one-dimensional battery charging simulation analysis model and optimized to generate a thermal management parameter combination table corresponding to the fastest charging speed mode, a thermal management parameter combination table corresponding to the lowest charging energy consumption mode, and a thermal management parameter combination table corresponding to the mode with the best overall charging performance. This includes the following sub-steps:

[0064] When the one-dimensional battery charging simulation analysis model generates the thermal management parameter combination table corresponding to the fastest charging mode, the optimization algorithm of the one-dimensional battery charging simulation analysis model is a genetic algorithm, the optimization objective is charging time, and the optimization constraints include the maximum battery temperature difference and the maximum battery temperature, wherein the maximum battery temperature is less than or equal to 50℃ and the maximum battery temperature difference is less than or equal to 10℃.

[0065] In an optional implementation, a fast-charging simulation experiment is conducted on the one-dimensional battery charging simulation analysis model and optimized to generate a thermal management parameter combination table corresponding to the fastest charging speed mode, a thermal management parameter combination table corresponding to the lowest charging energy consumption mode, and a thermal management parameter combination table corresponding to the mode with the best overall charging performance. This includes the following sub-steps:

[0066] When the one-dimensional battery charging simulation analysis model generates a thermal management parameter combination table corresponding to the mode with the lowest charging energy consumption, the optimization algorithm of the one-dimensional battery charging simulation analysis model is a genetic algorithm, the optimization objective is charging energy consumption, and the optimization constraints include the maximum battery temperature difference, the maximum battery temperature, and the charging time. Among them, the maximum battery temperature is less than or equal to 50℃, the maximum battery temperature difference is less than or equal to 10℃, and the charging time is less than or equal to 200min.

[0067] In this embodiment of the application, as an optional implementation, a fast-charging simulation experiment is conducted on the one-dimensional battery charging simulation analysis model and optimized to generate a thermal management parameter combination table corresponding to the fastest charging speed mode, a thermal management parameter combination table corresponding to the lowest charging energy consumption mode, and a thermal management parameter combination table corresponding to the best overall charging performance mode. This includes the following sub-steps:

[0068] When the one-dimensional battery charging simulation analysis model generates the thermal management parameter combination table corresponding to the optimal mode pair for comprehensive charging performance, the optimization algorithm of the one-dimensional battery charging simulation analysis model is the second-generation non-dominated sorting genetic algorithm. The optimization objectives are charging time and charging energy consumption, and the optimization constraints include the maximum temperature difference of the battery and the highest temperature of the battery. Among them, the highest temperature of the battery is less than or equal to 50℃, and the maximum temperature difference of the battery is less than or equal to 10℃.

[0069] In this embodiment of the application, as an optional implementation method, determining whether the accuracy of the one-dimensional charging simulation analysis model of the battery meets the preset conditions includes the following sub-steps:

[0070] Obtain the simulation values ​​of the one-dimensional battery charging simulation analysis model for the simulation conditions, and obtain the test values ​​for the test conditions;

[0071] The test values ​​are compared with the simulation values. If the error between the simulation values ​​and the test values ​​is less than or equal to the preset error threshold, the accuracy of the one-dimensional battery charging simulation analysis model is determined to meet the preset conditions. If the error between the simulation values ​​and the test values ​​is greater than the preset error threshold, the accuracy of the one-dimensional battery charging simulation analysis model is determined to not meet the preset conditions.

[0072] As an example of an embodiment of this application, a thermal management control method for fast charging of an electric vehicle battery includes:

[0073] Establish a one-dimensional battery charging simulation analysis model;

[0074] Determine whether the accuracy of the one-dimensional battery charging simulation analysis model meets the requirements;

[0075] The one-dimensional battery charging simulation analysis model was calibrated and corrected.

[0076] Fast charging simulation experiments were conducted on the one-dimensional battery charging simulation analysis model and optimized, generating thermal management parameter combination tables for three different fast charging modes.

[0077] When fast charging begins, the user first specifies the fast charging mode;

[0078] Obtain the starting temperature and starting state of charge (SOC) of the battery;

[0079] Based on the user-specified fast charging mode, as well as the battery's initial temperature and initial SOC, the corresponding thermal management parameter combination is retrieved from the corresponding thermal management parameter combination table.

[0080] The charging process is controlled by this combination of thermal management parameters to achieve the charging effect in the corresponding fast charging mode.

[0081] Furthermore, in the step of establishing a one-dimensional battery charging simulation analysis model, the one-dimensional battery charging simulation analysis model includes a battery equivalent circuit model, a battery temperature control system model, and a control strategy model. The schematic diagram of the established one-dimensional battery charging simulation analysis model is shown below. Figure 2 As shown, Figure 2 This is a schematic diagram of the architecture of a one-dimensional battery charging simulation analysis model disclosed in this application. Specifically, the battery equivalent circuit model is mainly used to calculate and output information such as the battery's SOC (State of Charge) and the battery's heat generation based on input information such as the battery's own electrical parameters, load power, fast charging current, and battery temperature. Among them, the battery temperature mainly includes the battery's highest temperature, lowest temperature, and average temperature; the battery's own electrical parameters mainly include the battery's capacity, charging / discharging internal resistance DCR, open circuit voltage OCV, and the number of series and parallel connections of the battery; the load power mainly includes the heating power of the PTC heater, the power consumption of the compressor, the power of the water pump, and the power consumption of other low-pressure accessories, etc. The power consumption of the compressor is mainly used to provide cooling for the battery cooler, and then the battery cooler cools the battery.

[0082] Specifically, the battery temperature control system model is mainly used to calculate and output information such as battery temperature, battery inlet water temperature, battery inlet flow rate, and water pump power based on input information such as water pump speed, PTC heater heating power, battery cooler cooling power, battery's own heat generation, battery structural and physical property parameters, and the performance parameters of each temperature control component. Among these, battery temperature mainly includes the battery's highest, lowest, and average temperatures; battery structural and physical property parameters mainly include battery size, weight, density, specific heat capacity, and thermal conductivity; the performance parameters of each temperature control component mainly include the flow resistance characteristics of each temperature control component, the efficiency characteristics of the water pump, the heat transfer performance parameters of the battery cooler, and the heating performance parameters of the PTC heater, etc. Each temperature control component mainly includes pipes, valves, battery cold plates, water pumps, battery coolers, and PTC heaters, etc.

[0083] Specifically, the control strategy model is mainly used to calculate and output information such as water pump speed based on input information such as heating target flow rate or cooling target flow rate and battery inlet flow rate; and to calculate and output information such as heating power of PTC heater based on input information such as battery temperature, battery heating threshold, heating target water temperature, battery inlet water temperature and heating power limit; and to calculate and output information such as cooling power of battery cooler and power consumption of compressor based on input information such as battery temperature, battery cooling threshold, cooling target water temperature, battery inlet water temperature, cooling power limit and compressor performance parameters; and to calculate and output information such as fast charging current based on input information such as battery temperature, battery SOC and DC charging strategy including DC charging MAP and charging current limit. Among them, battery temperature mainly includes the battery's maximum temperature, minimum temperature, and average temperature; battery heating threshold refers to the time when the battery's minimum temperature is less than or equal to the heating threshold, the PTC heater is turned on to heat the battery until the minimum temperature is greater than the heating threshold, and then heating is stopped; battery cooling threshold refers to the time when the battery's maximum temperature is greater than or equal to the cooling threshold, the battery cooler is turned on to cool the battery until the maximum temperature is less than the cooling threshold, and then cooling is stopped; compressor performance parameters mainly include the compressor's energy efficiency ratio (COP), etc.; DC charging MAP is a two-dimensional lookup table that includes battery temperature and battery SOC, that is, based on the current battery temperature and SOC, the fast charging rate can be found, and then the fast charging current can be obtained.

[0084] In the step of determining whether the accuracy of the one-dimensional battery charging simulation analysis model meets the requirements, parameters such as the battery charging start temperature, start SOC, heating threshold, cooling threshold, heating target water temperature, cooling target water temperature, heating target flow rate, and cooling target flow rate are selected as simulation input parameters. The value range of each simulation input parameter is determined. Then, several simulation conditions are established based on the simulation input parameters and their value ranges. These simulation conditions should cover charging scenarios such as low temperature, normal temperature, and high temperature, and should also cover charging scenarios such as low SOC, medium SOC, and high SOC. Next, the values ​​of the simulation input parameters corresponding to these simulation conditions are input into the one-dimensional battery charging simulation analysis model to conduct fast charging simulation tests, and the charging time and charging energy consumption of each simulation condition are obtained. Simultaneously, several test conditions are established based on these simulation conditions. Each test condition corresponds one-to-one with the simulation conditions and has the same input parameters. Then, bench tests or whole-vehicle tests are performed on these test conditions, and the charging time and charging energy consumption of each test condition are obtained. Finally, the charging time of each simulation condition is compared with the charging time of the corresponding test condition, and the charging energy consumption of each simulation condition is compared with the charging energy consumption of the corresponding test condition. This yields the error between the simulated and tested values ​​of the charging time and the error between the simulated and tested values ​​of the charging energy consumption for each condition. If the charging time error for each operating condition is less than the preset charging time error threshold, and the charging energy consumption error for each operating condition is less than the preset charging energy consumption error threshold, then the accuracy of the one-dimensional charging simulation analysis model of the battery is deemed to meet the requirements, and the step "conducting a fast charging simulation test and optimizing the one-dimensional charging simulation analysis model of the battery, and generating thermal management parameter combination tables for three different fast charging modes" can be continued. Otherwise, the accuracy of the one-dimensional charging simulation analysis model of the battery is deemed not to meet the requirements, and the step "calibrating and correcting the one-dimensional charging simulation analysis model of the battery" needs to be executed. Charging energy consumption mainly includes the total energy consumption of the PTC heater, compressor, water pump, and other low-pressure accessories during the charging process. Charging time is the time elapsed from the current initial SOC to reaching 100% SOC.

[0085] Furthermore, in the calibration and correction of the one-dimensional battery charging simulation analysis model, after determining that the accuracy of the established one-dimensional battery charging simulation analysis model does not meet the requirements, the simulation model needs to be calibrated and corrected based on the test results of the test conditions. Then, the step of "determining whether the accuracy of the one-dimensional battery charging simulation analysis model meets the requirements" is repeated to judge the accuracy of the simulation model until the accuracy of the simulation model meets the requirements.

[0086] Furthermore, after determining that the accuracy of the established one-dimensional battery charging simulation analysis model meets the requirements, fast charging simulation experiments and optimizations can be conducted based on this simulation model to generate thermal management parameter combination tables for different fast charging modes. First, the fast charging modes are divided into three types: fastest charging speed, lowest charging energy consumption, and best overall charging performance. Each fast charging mode corresponds to a thermal management parameter combination table, namely, the thermal management parameter combination table for the fastest charging speed mode, the thermal management parameter combination table for the lowest charging energy consumption mode, and the thermal management parameter combination table for the best overall charging performance mode. Each thermal management parameter combination table is a two-dimensional lookup table including the battery's initial temperature and initial SOC. That is, the corresponding thermal management parameter combination can be retrieved from the thermal management parameter combination table based on the battery's initial temperature and initial SOC. Then, the charging process is controlled by this thermal management parameter combination, which mainly includes parameters such as battery heating threshold, cooling threshold, target heating water temperature, target cooling water temperature, target heating flow rate, and target cooling flow rate. When selecting the fastest charging mode, the corresponding thermal management parameter combination is first retrieved from the thermal management parameter combination table for the fastest charging mode based on the battery's initial temperature and initial SOC. This thermal management parameter combination then controls the charging process to minimize charging time. Similarly, when selecting the lowest energy consumption charging mode, the corresponding thermal management parameter combination is retrieved from the thermal management parameter combination table for the lowest energy consumption charging mode based on the battery's initial temperature and initial SOC. This thermal management parameter combination then controls the charging process to minimize charging energy consumption. Finally, when selecting the optimal overall charging performance mode, the corresponding thermal management parameter combination is retrieved from the thermal management parameter combination table for the optimal overall charging performance mode based on the battery's initial temperature and initial SOC. This thermal management parameter combination then controls the charging process to minimize both charging time and charging energy consumption. The thermal management parameter combination tables for the three different fast charging modes can be obtained by optimizing the established one-dimensional battery charging simulation analysis model using an optimization algorithm. The specific method is as follows:

[0087] First, thermal management parameters such as battery heating threshold Th, cooling threshold Tc, target heating water temperature Wh, target cooling water temperature Wc, target heating flow rate Qh, and target cooling flow rate Qc are selected as design variables.

[0088]

[0089] Table 1

[0090] Then, the thermal management parameter combination table is divided as shown in Table 1. Table 1 is a thermal management parameter combination table. The x-axis (column) of this two-dimensional table is set as the battery's initial SOC, and the y-axis (row) is set as the battery's initial temperature. It is then divided into n initial SOC points SOC1, SOC2, ..., SOCn and m initial temperature points T1, T2, ..., Tm. Optionally, the battery's initial temperature ranges from -20 to 50 °C, the initial SOC ranges from 0% to 95%, m is 15, and n is 20. The value of the intersection of the i-th starting temperature point Ti and the j-th starting SOC point SOCj in the thermal management parameter combination table is the thermal management parameter combination Pij corresponding to the starting temperature point Ti and the starting SOC point SOCj, where Pij = {Thij, Tcij, Whij, Wcij, Qhij, Qcij}, where Thij, Tcij, Whij, Wcij, Qhij, and Qcij are the battery heating threshold, cooling threshold, heating target water temperature, cooling target water temperature, heating target flow rate, and cooling target flow rate corresponding to the starting temperature point Ti and the starting SOC point SOCj in the combination table, respectively. They together constitute the thermal management parameter combination Pij at the intersection point, where i = 1, 2, ..., m; j = 1, 2, ..., n. Therefore, for each fast charging mode, based on the established one-dimensional battery charging simulation analysis model, and using optimization algorithms to optimize each thermal management parameter combination Pij one by one, all optimized thermal management parameter combinations Pij are finally summarized in Table 1 to generate the corresponding thermal management parameter combination table for the fast charging mode. The specific generation method is as follows:

[0091] Fastest charging mode: Charging time t is selected as the optimization objective, and the battery's highest temperature Tmax and maximum temperature difference ΔTmax are selected as constraints. Then, a genetic algorithm (GA) is used to perform single-objective optimization on the one-dimensional charging simulation analysis model of the battery. For any combination of thermal management parameters Pij in Table 1, the corresponding optimization problem can be described as follows:

[0092] mint=t(Thij,Tcij,Whij,Wcij,Qhij,Qcij),

[0093] stThmin≤Thij≤Thmax

[0094] Tcmin≤Tcij≤Tcmax,

[0095] Whmin≤Whij≤Whmax

[0096] Wcmin≤Wcij≤Wcmax,

[0097] Qhmin≤Qhij≤Qhmax,

[0098] Qcmin≤Qcij≤Qcmax,

[0099] Tmax≤TUL,

[0100] ΔTmax ≤ ΔTUL.

[0101] Where i is any value from 1, 2, ..., m, and j is any value from 1, 2, ..., n.

[0102] Wherein, Thmax and Thmin are the upper and lower limits of the battery heating threshold range, respectively. The lower limit of the heating threshold cannot be set too small to ensure that the battery can enter the heating phase in time, and the upper limit of the heating threshold cannot be set too large to avoid resource waste caused by overheating. Optionally, 10℃≤Thij≤30℃; Tcmax and Tcmin are the upper and lower limits of the battery cooling threshold range, respectively. The lower limit of the cooling threshold cannot be set too small to avoid resource waste caused by premature cooling, and the upper limit of the cooling threshold cannot be set too large to ensure that the battery can enter the cooling phase in time. Optionally, 20℃≤Tcij≤45℃; Whmax and Whmin are respectively The upper and lower limits of the target heating water temperature range are defined. The lower limit of the target heating water temperature should not be set too low to ensure sufficient heating capacity and prevent the battery from heating too slowly, which would affect the charging speed. Simultaneously, the upper limit of the target heating water temperature should not be set too high to avoid excessive temperature differences in the battery, which could affect its lifespan. Optionally, 35℃≤Whij≤50℃; Wcmax and Wcmin are the upper and lower limits of the target cooling water temperature range, respectively. The lower limit of the target cooling water temperature should not be set too low to avoid excessive temperature differences in the battery, which would affect its lifespan. Simultaneously, the upper limit of the target cooling water temperature should not be set too high to ensure sufficient cooling capacity and prevent the battery from cooling too slowly, which would affect the charging speed. Optionally, 15℃≤Wcij≤25℃; Qhmax and Qhmin are the upper and lower limits of the target heating flow rate, respectively. The lower limit of the target heating flow rate should not be set too low to ensure sufficient heating capacity and prevent the battery from heating too slowly, which would affect the charging speed. At the same time, the upper limit of the target heating flow rate should not be set too high to avoid excessive temperature difference in the battery and affect its service life. Optionally, 5L / min≤Qhij≤20L / min; Qcmax and Qcmin are the upper and lower limits of the target cooling flow rate, respectively. The lower limit of the target cooling flow rate should not be set too low to ensure sufficient cooling capacity and prevent the battery from cooling too slowly, which would affect the charging speed. At the same time, the upper limit of the target cooling flow rate should not be set too high, which would affect the charging speed. The upper limit of the charging speed cannot be set too high to avoid excessive cooling temperature differences in the battery, which could affect its lifespan. Optionally, 5L / min ≤ Qcij ≤ 20L / min; TUL is the upper limit of the battery's maximum temperature. By constraining the battery's maximum temperature to not exceed this upper limit TUL, it is ensured that the battery will not slow down its charging speed due to excessive temperature, and safety issues such as thermal runaway caused by overheating are also avoided. Optionally, Tmax ≤ 50℃; ΔTUL is the upper limit of the battery's maximum temperature difference. By constraining the battery's maximum temperature difference to not exceed this upper limit ΔTUL, it is ensured that battery consistency will not deteriorate due to excessive temperature differences, thus extending battery lifespan. Optionally, ΔTmax ≤ 10℃.

[0103] Using the above optimization method, each thermal management parameter combination Pij in Table 1 is optimized one by one. Then, all the optimized thermal management parameter combinations Pij are summarized in Table 1 to generate the thermal management parameter combination table for the fastest charging mode.

[0104] Minimum charging energy consumption mode: Charging energy consumption E is selected as the optimization objective. In addition to the maximum battery temperature Tmax and the maximum battery temperature difference ΔTmax as constraints, charging time t is added as an additional constraint. Then, a genetic algorithm (GA) is used to perform single-objective optimization on the one-dimensional charging simulation analysis model of the battery. For any combination of thermal management parameters Pij in Table 1, the corresponding optimization problem can be described as follows:

[0105] minE=E(Thij,Tcij,Whij,Wcij,Qhij,Qcij),

[0106] stThmin≤Thij≤Thmax

[0107] Tcmin≤Tcij≤Tcmax,

[0108] Whmin≤Whij≤Whmax

[0109] Wcmin≤Wcij≤Wcmax,

[0110] Qhmin≤Qhij≤Qhmax,

[0111] Qcmin≤Qcij≤Qcmax,

[0112] Tmax≤TUL,

[0113] ΔTmax≤ΔTUL,

[0114] t≤tUL.

[0115] Where i is any value from 1, 2, ..., m, and j is any value from 1, 2, ..., n.

[0116] The selectable value ranges for each of the above parameters can be referenced from the settings in the fastest charging mode. In addition, tUL is the upper limit of the charging time. By constraining the charging time to not exceed this upper limit tUL, the situation of sacrificing charging time greatly to reduce charging energy consumption is avoided, ensuring that the charging time is maintained within an acceptable range. Optionally, t≤200min.

[0117] Using the above optimization method, each thermal management parameter combination Pij in Table 1 is optimized one by one. Then, all the optimized thermal management parameter combinations Pij are summarized in Table 1 to generate the thermal management parameter combination table under the lowest charging energy consumption mode.

[0118] The optimal charging performance model is determined by selecting charging time t and charging energy consumption E as optimization objectives, and the maximum battery temperature Tmax and maximum battery temperature difference ΔTmax as constraints. Then, the second-generation non-dominated sorting genetic algorithm NSGA-II is used to perform multi-objective optimization on the one-dimensional charging simulation analysis model of the battery. For any combination of thermal management parameters Pij in Table 1, the corresponding optimization problem can be described as follows:

[0119] mint=t(Thij,Tcij,Whij,Wcij,Qhij,Qcij),

[0120] minE=E(Thij,Tcij,Whij,Wcij,Qhij,Qcij),

[0121] stThmin≤Thij≤Thmax

[0122] Tcmin≤Tcij≤Tcmax,

[0123] Whmin≤Whij≤Whmax

[0124] Wcmin≤Wcij≤Wcmax,

[0125] Qhmin≤Qhij≤Qhmax,

[0126] Qcmin≤Qcij≤Qcmax,

[0127] Tmax≤TUL,

[0128] ΔTmax ≤ ΔTUL.

[0129] Where i is any value from 1, 2, ..., m, and j is any value from 1, 2, ..., n.

[0130] The selectable value ranges for each of the above parameters can be referenced from the settings in the fastest charging speed mode.

[0131] Using the above optimization method, multi-objective optimization is performed on each thermal management parameter combination Pij in Table 1, and a corresponding Pareto solution set is obtained. However, since there is a certain contradiction between the two optimization objectives, there is no solution that allows each optimization objective to reach its optimal value at the same time. Therefore, a trade-off must be made between the two optimization objectives. Finally, according to the actual needs, a satisfactory optimal solution can be selected from the Pareto solution set as the corresponding thermal management parameter combination Pij. Then, all the optimized thermal management parameter combinations Pij are summarized in Table 1 to generate the thermal management parameter combination table under the best overall charging performance mode.

[0132] Furthermore, in the step of user specifying the fast charging mode when starting fast charging, after the DC fast charging gun is successfully connected and before starting current fast charging, the user can specify the desired fast charging mode from the central control screen as needed, including three fast charging modes: fastest charging speed, lowest charging energy consumption, and best overall charging performance.

[0133] Furthermore, in the step of obtaining the starting temperature and starting SOC of battery charging;

[0134] After the DC fast charging gun is successfully connected, before starting fast charging, the starting temperature and initial remaining charge (SOC) of the battery are obtained. The starting temperature of battery charging includes the battery's initial maximum temperature and initial minimum temperature.

[0135] Furthermore, in the step of retrieving the corresponding thermal management parameter combination from the corresponding thermal management parameter combination table based on the user-specified fast charging mode and the battery's initial charging temperature and initial SOC, the vehicle's backend first retrieves the thermal management parameter combination table for the corresponding fast charging mode, and then, based on the obtained battery initial temperature and initial SOC, looks up the corresponding thermal management parameter combination at that initial temperature and initial SOC. During the lookup, if no initial temperature and initial SOC point exactly match the current battery initial temperature and initial SOC in the combination table, the lookup is performed according to the nearest principle. That is, the thermal management parameter combination closest to the current initial temperature and initial SOC is found in the thermal management parameter combination table and used as the corresponding thermal management parameter combination. For example, if the current battery initial temperature is between temperature points T1 and T2 and closer to T2, and the current initial SOC is between SOC points SOC1 and SOC2 and closer to SOC1, then the thermal management parameter combination obtained by looking up the table according to the nearest principle is P21. The thermal management parameter combination mainly includes parameters such as battery heating threshold, cooling threshold, target heating water temperature, target cooling water temperature, target heating flow rate, and target cooling flow rate.

[0136] Furthermore, in the step of controlling the charging process with this combination of thermal management parameters to achieve the charging effect of the corresponding fast charging mode, the thermal management parameter combination obtained from the table based on the user-specified fast charging mode, battery initial temperature, and initial SOC controls the entire charging process to achieve the charging effect of the corresponding fast charging mode. The charging effects corresponding to the fastest charging speed, lowest charging energy consumption, and best overall charging performance are the shortest charging time, lowest charging energy consumption, and the minimum combined charging time and energy consumption, respectively.

[0137] Example 2

[0138] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a thermal management control device for fast charging of an electric vehicle battery disclosed in an embodiment of this application, as shown below. Figure 3 As shown, the apparatus in this embodiment includes the following functional modules:

[0139] The acquisition module 201 is used to acquire the starting temperature of battery charging, the starting SOC of battery charging, and the fast charging mode input by the user. The fast charging mode includes the fastest charging mode, the lowest charging energy consumption mode, and the best overall charging performance mode.

[0140] The determination module 202 is used to determine the combination of thermal management parameters based on the starting temperature of battery charging, the starting SOC of battery charging, and the fast charging mode input by the user.

[0141] The control module 203 is used to control the battery charging process based on a combination of constant thermal management parameters.

[0142] Example 3

[0143] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application, such as... Figure 4 As shown, the electronic device in this application embodiment includes:

[0144] Processor 301; and,

[0145] The memory 302 is configured to store machine-readable instructions that, when executed by the processor 301, perform the thermal management control method for fast charging of electric vehicle batteries as described in any of the foregoing embodiments.

[0146] Example 4

[0147] This application provides a storage medium storing a computer program, which is executed by a processor as a thermal management control method for fast charging of an electric vehicle battery as described in any of the foregoing embodiments.

[0148] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0149] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional modules in the various embodiments of this 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.

[0151] It should be noted that if a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0152] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0153] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A thermal management control method for fast charging of electric vehicle batteries, characterized in that, The method includes: The system acquires the starting temperature of battery charging, the starting SOC of the battery charging, and the fast charging mode input by the user, wherein the fast charging mode includes the fastest charging mode, the lowest charging energy consumption mode, and the best overall charging performance mode. The combination of thermal management parameters is determined based on the starting temperature of battery charging, the starting state of battery charging, and the fast charging mode input by the user. The charging process of the battery is controlled based on the combination of constant thermal management parameters; Furthermore, the method further includes: A one-dimensional battery charging simulation analysis model is constructed. The input of the one-dimensional battery charging simulation analysis model is the parameters under the target operating condition. The parameters under the target operating condition include the initial temperature, initial SOC, heating threshold, cooling threshold, target heating water temperature, target cooling water temperature, target heating flow rate, and target cooling flow rate. The output of the one-dimensional battery charging simulation analysis model is the charging energy consumption and charging time. Fast charging simulation experiments were conducted on the one-dimensional charging simulation analysis model of the battery and optimized to generate thermal management parameter combination tables corresponding to the fastest charging speed mode, the lowest charging energy consumption mode, and the best overall charging performance mode. And, the determination of the thermal management parameter combination based on the battery charging start temperature, the battery charging start SOC, and the user-input fast charging mode includes: When the user inputs the fastest charging mode, the thermal management parameter combination table corresponding to the fastest charging mode is queried based on the battery charging start temperature and the battery charging start SOC to obtain the thermal management parameter combination. When the fast charging mode input by the user is the lowest charging energy consumption mode, the thermal management parameter combination table corresponding to the lowest charging energy consumption mode is queried based on the starting temperature of the battery charging and the starting SOC of the battery charging to obtain the thermal management parameter combination. When the fast charging mode input by the user is the optimal mode for overall charging performance, the thermal management parameter combination table corresponding to the optimal mode for overall charging performance is queried based on the starting temperature of battery charging and the starting SOC of battery charging to obtain the thermal management parameter combination. And, after constructing the one-dimensional battery charging simulation analysis model, and before conducting fast-charging simulation experiments and optimizing the one-dimensional battery charging simulation analysis model, the method further includes: Determine whether the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions. If the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions, then perform the fast charging simulation test and optimization on the one-dimensional battery charging simulation analysis model. If the accuracy of the one-dimensional battery charging simulation analysis model does not meet the preset conditions, then calibrate and correct the one-dimensional battery charging simulation analysis model. And, the determination of whether the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions includes: Obtain the simulation values ​​of the one-dimensional battery charging simulation analysis model for the simulation conditions, and obtain the test values ​​for the test conditions; The test value is compared with the simulation value. If the error between the simulation value and the test value is less than or equal to a preset error threshold, the accuracy of the one-dimensional battery charging simulation analysis model is determined to meet the preset condition. If the error between the simulation value and the test value is greater than the preset error threshold, the accuracy of the one-dimensional battery charging simulation analysis model is determined to not meet the preset condition.

2. The method as described in claim 1, characterized in that, The process of performing fast-charging simulation experiments and optimizing the one-dimensional charging simulation analysis model of the battery to generate thermal management parameter combination tables for the fastest charging speed mode, the lowest charging energy consumption mode, and the optimal overall charging performance mode includes: When the one-dimensional battery charging simulation analysis model generates the thermal management parameter combination table corresponding to the fastest charging mode, the optimization algorithm of the one-dimensional battery charging simulation analysis model is a genetic algorithm, the optimization objective is charging time, and the optimization constraints include the maximum battery temperature difference and the maximum battery temperature, wherein the maximum battery temperature is less than or equal to 50°C, and the maximum battery temperature difference is less than or equal to 10°C.

3. The method as described in claim 1, characterized in that, The process of performing fast-charging simulation experiments and optimizing the one-dimensional charging simulation analysis model of the battery to generate thermal management parameter combination tables for the fastest charging speed mode, the lowest charging energy consumption mode, and the optimal overall charging performance mode includes: When the one-dimensional battery charging simulation analysis model generates the thermal management parameter combination table corresponding to the lowest charging energy consumption mode, the optimization algorithm of the one-dimensional battery charging simulation analysis model is a genetic algorithm, the optimization objective is charging energy consumption, and the optimization constraints include the maximum battery temperature difference, the maximum battery temperature, and the charging time, wherein the maximum battery temperature is less than or equal to 50°C, the maximum battery temperature difference is less than or equal to 10°C, and the charging time is less than or equal to 200 min.

4. The method as described in claim 1, characterized in that, The process of performing fast-charging simulation experiments and optimizing the one-dimensional charging simulation analysis model of the battery to generate thermal management parameter combination tables for the fastest charging speed mode, the lowest charging energy consumption mode, and the optimal overall charging performance mode includes: When the one-dimensional battery charging simulation analysis model generates the thermal management parameter combination table corresponding to the optimal mode for comprehensive charging performance, the optimization algorithm of the one-dimensional battery charging simulation analysis model is the second-generation non-dominated sorting genetic algorithm. The optimization objectives are charging time and charging energy consumption, and the optimization constraints include the maximum battery temperature difference and the maximum battery temperature, wherein the maximum battery temperature is less than or equal to 50°C and the maximum battery temperature difference is less than or equal to 10°C.

5. A thermal management control device for fast charging of electric vehicle batteries, characterized in that, The device includes: The acquisition module is used to acquire the starting temperature of battery charging, the starting SOC of the battery charging, and the fast charging mode input by the user, wherein the fast charging mode includes the fastest charging mode, the lowest charging energy consumption mode, and the best overall charging performance mode. The determination module is used to determine a combination of thermal management parameters based on the starting temperature of battery charging, the starting state of battery charging, and the fast charging mode input by the user. A control module is used to control the charging process of the battery based on the combination of constant thermal management parameters; Furthermore, the device is also used for: A one-dimensional battery charging simulation analysis model is constructed. The input of the one-dimensional battery charging simulation analysis model is the parameters under the target operating condition. The parameters under the target operating condition include the initial temperature, initial SOC, heating threshold, cooling threshold, target heating water temperature, target cooling water temperature, target heating flow rate, and target cooling flow rate. The output of the one-dimensional battery charging simulation analysis model is the charging energy consumption and charging time. Fast charging simulation experiments were conducted on the one-dimensional charging simulation analysis model of the battery and optimized to generate thermal management parameter combination tables corresponding to the fastest charging speed mode, the lowest charging energy consumption mode, and the best overall charging performance mode. And, the determination of the thermal management parameter combination based on the battery charging start temperature, the battery charging start SOC, and the user-input fast charging mode includes: When the user inputs the fastest charging mode, the thermal management parameter combination table corresponding to the fastest charging mode is queried based on the battery charging start temperature and the battery charging start SOC to obtain the thermal management parameter combination. When the fast charging mode input by the user is the lowest charging energy consumption mode, the thermal management parameter combination table corresponding to the lowest charging energy consumption mode is queried based on the starting temperature of the battery charging and the starting SOC of the battery charging to obtain the thermal management parameter combination. When the fast charging mode input by the user is the optimal mode for overall charging performance, the thermal management parameter combination table corresponding to the optimal mode for overall charging performance is queried based on the starting temperature of battery charging and the starting SOC of battery charging to obtain the thermal management parameter combination. And, after constructing the one-dimensional battery charging simulation analysis model, and before conducting fast-charging simulation experiments and optimizing the one-dimensional battery charging simulation analysis model, the device is further configured to: Determine whether the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions. If the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions, then perform the fast charging simulation test and optimization on the one-dimensional battery charging simulation analysis model. If the accuracy of the one-dimensional battery charging simulation analysis model does not meet the preset conditions, then calibrate and correct the one-dimensional battery charging simulation analysis model. And, the determination of whether the accuracy of the one-dimensional battery charging simulation analysis model meets the preset conditions includes: Obtain the simulation values ​​of the one-dimensional battery charging simulation analysis model for the simulation conditions, and obtain the test values ​​for the test conditions; The test value is compared with the simulation value. If the error between the simulation value and the test value is less than or equal to a preset error threshold, the accuracy of the one-dimensional battery charging simulation analysis model is determined to meet the preset condition. If the error between the simulation value and the test value is greater than the preset error threshold, the accuracy of the one-dimensional battery charging simulation analysis model is determined to not meet the preset condition.

6. An electronic device, characterized in that, include: processor; as well as A memory configured to store machine-readable instructions that, when executed by the processor, perform the thermal management control method for fast charging of an electric vehicle battery as described in any one of claims 1-4.

7. A storage medium, characterized in that, The storage medium stores a computer program, which is executed by a processor as described in any one of claims 1-4, the thermal management control method for fast charging of electric vehicle batteries.

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