Battery internal temperature prediction method and device, electronic equipment, vehicle and storage medium

Abstract

The invention provides a battery internal temperature prediction method and device, electronic equipment, a vehicle and a storage medium, and the method comprises the steps: obtaining a temperature adjustment mode of a to-be-predicted battery, and obtaining a temperature prediction parameter corresponding to the temperature adjustment mode; determining an environmental factor temperature change sub-parameter, a heat dissipation error sub-parameter, a battery self-heating sub-parameter and a temperature adjustment sub-parameter based on the temperature prediction parameter; the battery internal temperature change caused by environmental factors, battery internal temperature difference, battery self-heating effect and temperature adjustment of the to-be-predicted battery by the temperature adjustment device is considered, and the temperature change rate caused by the factors is superposed to obtain the predicted battery internal temperature change rate; and then the predicted battery internal temperature is obtained in combination with the current battery average temperature, temperature prediction is performed from the perspective of a thermal management system, the influence of natural convective heat transfer of the environment temperature and different heat source transfer forms on the battery temperature is considered, and the accuracy and reliability of battery internal temperature prediction are improved.

Description

Technical Field

[0001] This application relates to the field of vehicles, and more particularly to a method, apparatus, electronic device, vehicle, and storage medium for predicting the internal temperature of a battery. Background Technology

[0002] As a core component of electric vehicles, the electrochemical performance of power batteries is highly sensitive to ambient temperature. Accurate and rapid prediction of the internal temperature of power batteries, including the highest, lowest, and average temperatures of the cells, can maximize battery performance and improve safety and durability during charging and discharging. Battery temperature estimation is fundamental to other state estimations; however, due to limitations in sensor placement and measurement accuracy, the local temperature inside the cell is difficult to estimate accurately, leading to the inability to accurately capture thermal runaway points and cell temperature differences.

[0003] In related technologies, the internal temperature of a battery is determined by estimating its state through training neural networks or using statistical data methods such as Kalman filtering. However, the internal temperature of the battery estimated by these methods still differs significantly from the actual internal temperature of the battery, resulting in poor accuracy and reliability. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, vehicle, and storage medium for predicting the internal temperature of a battery, in order to solve the technical problem in the related art that the estimated internal temperature of the battery still differs greatly from the actual internal temperature of the battery, resulting in poor accuracy and reliability.

[0005] This application provides a method for predicting the internal temperature of a battery. The method includes: acquiring a temperature adjustment mode of a battery to be predicted, and acquiring temperature prediction parameters corresponding to the temperature adjustment mode, wherein the battery to be predicted is temperature-adjusted by a temperature adjustment device; determining environmental factor temperature change sub-parameters, heat dissipation error sub-parameters, battery self-heating sub-parameters, and temperature adjustment sub-parameters based on the temperature prediction parameters, wherein the environmental factor temperature change sub-parameters characterize the rate of temperature change of the battery to be predicted due to ambient temperature, the heat dissipation error sub-parameters characterize the rate of temperature change of the battery to be predicted due to internal temperature difference, the battery self-heating sub-parameters characterize the rate of temperature change of the battery to be predicted due to battery self-heating effect, and the temperature adjustment sub-parameters characterize the rate of temperature change of the battery to be predicted due to temperature adjustment by the temperature adjustment device; superimposing the environmental factor temperature change sub-parameters, heat dissipation error sub-parameters, battery self-heating sub-parameters, and temperature adjustment sub-parameters to obtain a predicted rate of change of internal battery temperature; and determining the predicted internal battery temperature of the battery to be predicted based on the predicted rate of change of internal battery temperature and the current average battery temperature in the temperature prediction parameters.

[0006] In one embodiment of this application, the method for determining the environmental factor temperature change sub-parameter includes: determining a first temperature difference based on the current average battery temperature and the current ambient temperature; determining the environmental factor temperature change sub-parameter based on the battery's effective heat transfer area, the battery's equivalent heat transfer coefficient, the battery's initial total heat capacity, the battery's mass, and the first temperature difference; the method for determining the heat dissipation error sub-parameter includes: determining a second temperature difference based on the current highest temperature and the current lowest temperature of the battery to be predicted; determining the heat dissipation error sub-parameter based on the second temperature difference; wherein, the temperature prediction parameters include the current ambient temperature, the current average battery temperature, the battery's effective heat transfer area, the battery's equivalent heat transfer coefficient, the battery's initial total heat capacity, the battery's mass, the current highest temperature, and the current lowest temperature.

[0007] In one embodiment of this application, determining environmental factor temperature change sub-parameters based on the battery's effective heat transfer area, battery equivalent heat transfer coefficient, battery initial total heat capacity, battery mass, and the first temperature difference includes: determining the decayed battery total heat capacity using a preset fitting decay function and the battery's initial total heat capacity; determining environmental factor temperature change sub-parameters based on the battery's effective heat transfer area, battery equivalent heat transfer coefficient, decayed battery total heat capacity, battery mass, and the first temperature difference, wherein the temperature prediction parameter further includes the preset fitting decay function; determining heat dissipation error sub-parameters based on the second temperature difference includes: correcting the second temperature difference using a preset correction factor to obtain the heat dissipation error sub-parameters, wherein the temperature prediction parameter further includes the preset correction factor.

[0008] In one embodiment of this application, the method for determining the battery self-heating sub-parameter includes: determining the self-heating power based on the operating condition efficiency factor, the battery current under operating conditions, and the battery equivalent resistance; and determining the battery self-heating sub-parameter based on the self-heating power, battery mass, and initial total heat capacity of the battery. The temperature prediction parameters include the operating condition efficiency factor, the battery current under operating conditions, the battery equivalent resistance, the initial total heat capacity of the battery, and the battery mass. If the operating condition is discharge, the battery current under operating conditions is the discharge operating condition battery current, and the operating condition efficiency factor is the discharge efficiency factor. If the operating condition is charging, the battery current under operating conditions is the charging operating condition battery current, and the operating condition efficiency factor is the charging efficiency factor.

[0009] In one embodiment of this application, if the temperature adjustment mode is direct heating, determining the predicted internal temperature of the battery to be predicted based on the predicted internal temperature change rate of the battery and the current average battery temperature in the temperature prediction parameters includes: obtaining the time difference between the prediction time and the current time; determining the predicted temperature difference based on the time difference and the predicted internal temperature change rate of the battery; and determining the predicted internal temperature of the battery to be predicted at the prediction time based on the predicted temperature difference and the current average battery temperature.

[0010] In one embodiment of this application, the method for determining the temperature adjustment sub-parameter includes: adjusting the power of the battery direct heating source according to the direct heating efficiency parameter to obtain the adjusted heat source power; determining the temperature adjustment sub-parameter based on the adjusted heat source power, battery mass, and initial total heat capacity of the battery; wherein, the temperature prediction parameter includes the direct heating efficiency parameter, the battery direct heating source power, battery mass, and initial total heat capacity of the battery.

[0011] In one embodiment of this application, if the temperature adjustment mode is liquid cooling, liquid heating, air cooling, air heating, direct heating, or direct cooling, determining the predicted internal temperature of the battery to be predicted based on the predicted internal temperature change rate of the battery and the current average battery temperature in the temperature prediction parameters includes: determining the predicted heat transfer medium temperature change rate according to the temperature prediction parameters; determining the predicted heat transfer medium temperature change rate at the prediction time based on the predicted heat transfer medium temperature change rate and the current average heat transfer medium temperature; obtaining the time difference between the prediction time and the current time; determining the predicted temperature difference based on the time difference and the predicted internal temperature change rate of the battery; determining the predicted internal temperature of the battery to be predicted at the prediction time based on the predicted temperature difference and the current average battery temperature; adjusting the predicted internal temperature change rate of the battery using the predicted heat transfer medium temperature change rate and the current ambient temperature to obtain the predicted internal temperature change rate of the battery at the prediction time; and determining the predicted internal temperature of the battery at the next prediction time based on the predicted internal temperature change rate of the battery at the prediction time and the predicted internal temperature of the battery.

[0012] In one embodiment of this application, if the temperature adjustment mode is liquid cooling or liquid heating, the method for determining the temperature adjustment sub-parameter includes: determining a third temperature difference based on the current heat transfer medium temperature and the current average battery temperature; determining the heat transfer power transferred by the heat transfer medium to the battery to be predicted based on the equivalent heat transfer area of ​​the heat transfer contact surface to the battery, the heat transfer coefficient of the heat transfer medium, and the third temperature difference; and determining the temperature adjustment sub-parameter based on the heat transfer power, battery mass, and initial total heat capacity of the battery. The temperature prediction parameters further include battery mass, initial total heat capacity of the battery, and the equivalent heat transfer area of ​​the heat transfer contact surface to the battery in liquid cooling or liquid heating mode, the current heat transfer medium temperature, and the heat transfer coefficient of the heat transfer medium. The temperature adjustment device includes a heat transfer medium, and the temperature of the battery to be predicted is adjusted through the heat transfer medium.

[0013] In one embodiment of this application, determining the predicted rate of change of the heat transfer medium temperature based on the temperature prediction parameters includes: determining a fourth temperature difference based on the current heat transfer medium temperature and the current ambient temperature; determining the pipeline environment heat loss transferred by the pipeline to the environment based on the equivalent heat transfer area of ​​the pipeline to the environment, the heat transfer coefficient of the heat transfer medium, and the fourth temperature difference; adjusting the compressor input power based on the heat transfer efficiency parameters of the heat transfer medium to obtain the adjusted compressor input power; and determining the predicted rate of change of the heat transfer medium temperature based on the pipeline environment heat loss, the heat transfer power, the adjusted compressor input power, the mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium. The temperature prediction parameters further include the heat transfer efficiency parameters of the heat transfer medium, the compressor input power, the current ambient temperature, the equivalent heat transfer area of ​​the pipeline to the environment in liquid-cooled or liquid-thermal mode, the mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium.

[0014] In one embodiment of this application, if the temperature adjustment mode is air cooling or air heating, the method for determining the temperature adjustment sub-parameter includes: determining a fifth temperature difference based on the current heat transfer medium temperature and the current average battery temperature; determining the heat transfer power transferred by the heat transfer medium to the battery to be predicted based on the equivalent heat transfer area of ​​the heat transfer contact surface to the battery, the heat transfer coefficient of the heat transfer medium, and the fifth temperature difference; and determining the temperature adjustment sub-parameter based on the heat transfer power, battery mass, and initial total heat capacity of the battery. The temperature prediction parameter further includes battery mass, initial total heat capacity of the battery, and the equivalent heat transfer area of ​​the heat transfer medium to the battery in air cooling or air heating mode, the current heat transfer medium temperature, and the heat transfer coefficient of the heat transfer medium. The temperature adjustment device includes a heat transfer medium, and the temperature of the battery to be predicted is adjusted through the heat transfer medium.

[0015] In one embodiment of this application, determining the temperature change rate of the heat transfer medium based on the temperature prediction parameters includes: determining a sixth temperature difference based on the current temperature of the heat transfer medium and the current ambient temperature; determining the pipeline environment heat loss transferred by the pipeline to the environment based on the equivalent heat transfer area of ​​the pipeline to the environment, the heat transfer coefficient of the heat transfer medium, and the sixth temperature difference; adjusting the compressor input power based on the heat transfer efficiency parameters of the heat transfer medium to obtain the adjusted compressor input power; and determining the temperature change rate of the heat transfer medium based on the pipeline environment heat loss, the heat transfer power, the adjusted compressor input power, the mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium; wherein, the temperature prediction parameters further include the heat transfer efficiency parameters of the heat transfer medium, the compressor input power, the current ambient temperature, and the equivalent heat transfer area of ​​the pipeline to the environment, the mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium in air-cooled or air-heated modes.

[0016] In one embodiment of this application, if the temperature adjustment mode is direct heating, the method for determining the temperature adjustment sub-parameter includes: determining a seventh temperature difference based on the saturation temperature of the gaseous heat transfer medium and the current average battery temperature; determining an eighth temperature difference based on the current average battery temperature, the saturation temperature of the gaseous heat transfer medium, and the temperature of the heat transfer medium at the compressor outlet; and determining the effect of the heat transfer medium on the target load based on the heat transfer area of ​​the gaseous region of the heat transfer medium in the direct heating process, the heat transfer area of ​​the liquid region of the heat transfer medium in the direct heating process, the heat transfer coefficient of the gas-liquid mixture in the direct heating process, the heat transfer coefficient of the liquid in the direct heating process, the seventh temperature difference, and the eighth temperature difference. The method describes the heat release power of the battery to be predicted; and determines the temperature adjustment sub-parameters based on the heat release power, battery mass, and initial total heat capacity of the battery. The temperature prediction parameters also include battery mass, initial total heat capacity of the battery, saturation temperature of the gaseous heat transfer medium in direct heating mode, temperature of the heat transfer medium at the compressor outlet, heat transfer area of ​​the gaseous region of the heat transfer medium in the direct heating process, heat transfer area of ​​the liquid region of the heat transfer medium in the direct heating process, heat transfer coefficient of the gas-liquid mixture in the direct heating process, and heat transfer coefficient of the liquid in the direct heating process. The temperature adjustment device includes a heat transfer medium, and adjusts the temperature of the battery to be predicted through the heat transfer medium.

[0017] In one embodiment of this application, determining the predicted rate of change of the heat transfer medium temperature based on the temperature prediction parameters includes: determining a ninth temperature difference based on the heat transfer medium temperature at the compressor outlet and the current ambient temperature; determining the environmental heat loss of the heat transfer medium pipeline in the direct heating process transferred to the environment based on the equivalent heat transfer area of ​​the heat transfer medium pipeline in the direct heating process to the environment, the gas phase heat transfer coefficient of the heat transfer medium in the direct heating process, and the ninth temperature difference; adjusting the compressor input power based on the compressor mechanical efficiency to obtain the adjusted compressor input power; and determining the predicted rate of change of the heat transfer medium temperature based on the environmental heat loss of the heat transfer medium pipeline in the direct heating process, the heat release power, the adjusted compressor input power, the total mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium; wherein, the temperature prediction parameters also include the heat transfer medium temperature at the compressor outlet in the direct heating mode, the current ambient temperature, the equivalent heat transfer area of ​​the heat transfer medium pipeline in the direct heating process to the environment, the gas phase heat transfer coefficient of the heat transfer medium in the direct heating process, the compressor mechanical efficiency, the compressor input power, the total mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium.

[0018] In one embodiment of this application, if the temperature adjustment mode is direct cooling, the method for determining the temperature adjustment sub-parameter includes: determining a tenth temperature difference based on the temperature of the expanded heat transfer medium and the current average battery temperature; determining an eleventh temperature difference based on the current average battery temperature and the saturation temperature of the liquid heat transfer medium; and determining the temperature difference between the expanded heat transfer medium and the battery to be predicted based on the heat transfer area of ​​the gaseous region of the heat transfer medium during the direct cooling process, the heat transfer area of ​​the gas-liquid mixed region of the heat transfer medium during the direct cooling process, the heat transfer coefficient of the gas-liquid mixed region of the heat transfer medium during the direct cooling process, the gas phase heat transfer coefficient of the heat transfer medium during the direct cooling process, the tenth temperature difference, and the eleventh temperature difference. The temperature prediction parameters include: the heat absorption power of the battery for heat exchange; the temperature adjustment sub-parameters are determined based on the heat absorption power, battery mass, and initial total heat capacity of the battery; wherein the temperature prediction parameters also include battery mass, initial total heat capacity of the battery, heat transfer area of ​​the gaseous region of the heat transfer medium in the direct cooling process under direct cooling mode, heat transfer area of ​​the gas-liquid mixed region of the heat transfer medium in the direct cooling process, heat transfer coefficient of the gas-liquid mixed state of the heat transfer medium in the direct cooling process, heat transfer coefficient of the gas phase of the heat transfer medium in the direct cooling process, temperature of the expanded heat transfer medium, and saturation temperature of the liquid heat transfer medium; the temperature adjustment device includes a heat transfer medium, and the temperature of the battery to be predicted is adjusted through the heat transfer medium.

[0019] In one embodiment of this application, determining the predicted rate of change of the heat transfer medium temperature based on the temperature prediction parameters includes: determining a fifteenth temperature difference based on the heat transfer medium temperature at the compressor outlet and the current ambient temperature; determining the ambient heat loss of the heat transfer medium pipeline from the direct-heating process to the environment based on the equivalent heat transfer area of ​​the heat transfer medium pipeline to the environment, the gas phase heat transfer coefficient of the heat transfer medium in the direct-heating process, and the fifteenth temperature difference; determining a twelfth temperature difference based on the saturation temperature of the gaseous heat transfer medium and the current ambient temperature; and determining the predicted rate of change of the heat transfer medium temperature based on the current ambient temperature, the saturation temperature of the gaseous heat transfer medium, and the temperature of the heat transfer medium at the compressor outlet. The thirteenth temperature difference is determined; the heat transfer coefficient of the gas-liquid mixture of the heat transfer medium in the direct heating process, the heat transfer area of ​​the gaseous region of the heat transfer medium in the direct heating process, the heat transfer area of ​​the liquid region of the heat transfer medium in the direct heating process, the heat transfer coefficient of the liquid region of the heat transfer medium in the direct heating process, the twelfth and thirteenth temperature differences are used to determine the environmental heat release power of the heat transfer medium to the environment; the fourteenth temperature difference is determined based on the temperature of the condensed heat transfer medium and the current ambient temperature; the equivalent heat transfer coefficient of the expansion valve to the environment, the equivalent heat transfer area of ​​the expansion to the environment, the equivalent heat transfer coefficient of the pipeline transporting the liquid heat transfer medium in the direct cooling process to the environment, and the equivalent heat transfer area of ​​the pipeline transporting the heat transfer medium in the direct cooling process to the environment are used to determine the equivalent heat transfer coefficient of the expansion valve to the environment, the equivalent heat transfer area of ​​the expansion valve to the environment, and the equivalent heat transfer area of ​​the pipeline transporting the liquid heat transfer medium in the direct cooling process to the environment are used to determine the equivalent heat transfer coefficient of the liquid heat transfer medium in the direct cooling process to the environment. The effective heat transfer area and the fourteenth temperature difference are used to determine the heat loss transferred to the environment by the expansion valve pipeline; the compressor input power is adjusted according to the compressor mechanical efficiency to obtain the adjusted compressor input power; the predicted heat transfer medium temperature change rate is determined based on the environmental heat loss of the heat transfer medium pipeline in the direct heating process, the environmental heat release power, the heat loss transferred to the environment by the expansion valve pipeline, the heat absorption power, the adjusted compressor input power, the total mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium; wherein, the temperature prediction parameters also include the heat transfer medium temperature at the compressor outlet in the direct heating mode, the current external ambient temperature, and the equivalent heat transfer of the heat transfer medium pipeline to the environment in the direct heating process. Area, gas-phase heat transfer coefficient of heat transfer medium in direct heating process, saturation temperature of gaseous heat transfer medium, heat transfer area of ​​gaseous region of heat transfer medium in direct heating process, heat transfer area of ​​liquid region of heat transfer medium in direct heating process, heat transfer coefficient of gas-liquid mixture of heat transfer medium in direct heating process, heat transfer coefficient of liquid of heat transfer medium in direct heating process, temperature of heat transfer medium after condensation, equivalent heat transfer coefficient of expansion valve to environment, equivalent heat transfer area of ​​expansion to environment, equivalent heat transfer coefficient of pipeline of liquid heat transfer medium in direct cooling process to environment, equivalent heat transfer area of ​​pipeline of heat transfer medium in direct cooling process to environment, compressor mechanical efficiency, compressor input power, total mass flow rate of heat transfer medium and specific heat capacity of heat transfer medium.

[0020] In one embodiment of the present invention, if the operating condition is discharge and the battery internal temperature prediction method is applied to a vehicle, the method further includes: if the vehicle is in urban driving condition, determining the discharge condition battery current based on historical discharge current; if the vehicle is in high-speed driving condition, determining the discharge condition battery current based on a preset driving condition current curve.

[0021] This application embodiment also provides a battery internal temperature prediction device, the device comprising: an acquisition module, configured to acquire a temperature adjustment mode of the battery to be predicted, and acquire temperature prediction parameters corresponding to the temperature adjustment mode, wherein the battery to be predicted is temperature-adjusted by the temperature adjustment device; and a sub-parameter determination module, configured to determine environmental factor temperature change sub-parameters, heat dissipation error sub-parameters, battery self-heating sub-parameters, and temperature adjustment sub-parameters based on the temperature prediction parameters, wherein the environmental factor temperature change sub-parameters characterize the rate of temperature change of the battery to be predicted due to the ambient temperature, and the heat dissipation error sub-parameters characterize the temperature of the battery to be predicted due to the internal temperature difference of the battery. The rate of change, wherein the battery self-heating sub-parameter characterizes the rate of temperature change of the battery to be predicted due to the battery self-heating effect, and the temperature adjustment sub-parameter characterizes the rate of temperature change of the battery to be predicted due to the temperature adjustment device adjusting the temperature of the battery to be predicted; the rate of change prediction module is used to superimpose the environmental factor temperature change sub-parameter, the heat dissipation error sub-parameter, the battery self-heating sub-parameter, and the temperature adjustment sub-parameter to obtain the predicted internal temperature change rate of the battery; the temperature prediction module is used to determine the predicted internal temperature of the battery to be predicted based on the predicted internal temperature change rate of the battery and the current average battery temperature in the temperature prediction parameters.

[0022] This application embodiment also provides a vehicle, the vehicle including a battery to be predicted and a battery management system, the battery management system including the battery internal temperature prediction device described in the above embodiment, wherein the battery management system is used to perform a power-on self-test when the vehicle is powered on; if the self-test is successful, it triggers the battery internal temperature prediction device to perform a prediction of the battery internal temperature.

[0023] This application also provides an electronic device, including: a memory storing a computer program thereon; and a processor for executing the computer program in the memory to implement the steps of the method described in any of the above embodiments.

[0024] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0025] Beneficial Effects: This application proposes a method, device, electronic device, vehicle, and storage medium for predicting the internal temperature of a battery. The method obtains the temperature adjustment mode of the battery to be predicted and the corresponding temperature prediction parameters. Based on these parameters, it determines environmental factor temperature change sub-parameters, heat dissipation error sub-parameters, battery self-heating sub-parameters, and temperature adjustment sub-parameters. This takes into account the changes in the internal temperature of the battery caused by environmental factors, internal battery temperature difference, battery self-heating effect, and temperature adjustment by the temperature adjustment device. The rate of temperature change caused by these factors is superimposed to obtain the predicted internal temperature change rate of the battery, thereby obtaining the predicted internal temperature of the battery. This temperature prediction is performed from the perspective of a thermal management system, considering the influence of natural convection heat transfer on the battery's natural cooling rate and the differences brought about by different heat source transfer methods (wind heat / water heat), thus improving the accuracy and reliability of the battery internal temperature prediction. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0027] In the attached diagram:

[0028] Figure 1 A schematic flowchart of a battery internal temperature prediction method provided in an embodiment of this application;

[0029] Figure 2 This is a schematic diagram of a battery internal temperature prediction model provided in an embodiment of this application;

[0030] Figure 3 A specific schematic diagram of a battery internal temperature prediction method provided in an embodiment of this application;

[0031] Figure 4 A schematic diagram of a battery internal temperature prediction device provided in an embodiment of this application;

[0032] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0033] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0034] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0035] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0036] As a core component of electric vehicles, the electrochemical performance of power batteries is highly sensitive to ambient temperature. Accurate and rapid prediction of the internal temperature of power batteries, including the highest, lowest, and average temperatures of the cells, can maximize battery performance and improve safety and durability during charging and discharging. Battery temperature estimation is fundamental to other state estimations; however, due to limitations in sensor placement and measurement accuracy, the local temperature inside the cell is difficult to estimate accurately, leading to the inability to accurately capture thermal runaway points and cell temperature differences. To quickly estimate cell temperature, related technologies employ statistical data methods such as neural networks and Kalman filtering for state estimation.

[0037] However, the inventors discovered that the battery temperature estimation methods in related technologies have the following limitations in practical applications: 1. Lack of thermal management system modeling schemes, limiting application scenarios. Current systems, in order to quickly estimate cell temperature, typically ignore the actual thermal management system architecture, treating the battery as the research object, fixing a single thermal path heat transfer object, and considering the coolant flow rate and temperature at the battery inlet and outlet as heat boundaries. They predict cell temperature using mathematical modeling, ignoring the differences brought about by diverse heat sources and different heat transfer methods (wind-heating / water-heating) in actual applications. The lack of a modeling scheme from a thermal management system perspective, relying on experimental data to calibrate parameters, inevitably limits application scenarios. 2. Lack of dynamic identification of the natural cooling effect of the environment, leading to temperature estimation bias. Most related technologies only consider the impact of forced convection heat transfer of the coolant on cell temperature, failing to fully consider the real-time impact of natural convection heat transfer of ambient temperature on the battery's natural cooling rate. For example, in low-temperature environments (such as -5℃ to -25℃), the heat exchange rate between the battery and the environment changes significantly, but the temperature prediction model lacks dynamic identification of the natural cooling effect, resulting in biased temperature prediction results. 3. Lack of battery operating scenario identification and insufficient adaptability to operating conditions. Bernardi's heat generation rate model, based on the assumption of uniform heating, relies heavily on calculating the electrothermal coupling characteristics of the battery during operation. However, it lacks identification of battery operating scenarios and fails to establish differentiated temperature estimation methods for different battery usage conditions, such as charge / discharge, urban, and high-speed conditions. Furthermore, it lacks strategies for correcting battery polarization resistance, resulting in insufficient adaptability to operating conditions.

[0038] In view of this, a method for predicting the internal temperature of a battery is proposed. This method obtains the temperature adjustment mode of the battery to be predicted and the corresponding temperature prediction parameters. Based on these parameters, it determines environmental factor temperature change sub-parameters, heat dissipation error sub-parameters, battery self-heating sub-parameters, and temperature adjustment sub-parameters. This takes into account the changes in the internal temperature of the battery caused by environmental factors, internal battery temperature difference, battery self-heating effect, and temperature adjustment by the temperature adjustment device. The rate of temperature change caused by these factors is then superimposed to obtain the predicted internal temperature change rate of the battery, thereby obtaining the predicted internal temperature of the battery. This method, based on the perspective of a thermal management system, considers the influence of natural convection heat transfer on the battery's natural cooling rate and the differences brought about by different heat source transfer methods (wind heat / water heat), thus improving the accuracy and reliability of the battery internal temperature prediction.

[0039] Please see Figure 1 , Figure 1 This is a schematic flowchart of a battery internal temperature prediction method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps:

[0040] Step S110: Obtain the temperature adjustment mode of the battery to be predicted, and obtain the temperature prediction parameters corresponding to the temperature adjustment mode.

[0041] The battery to be predicted is temperature-adjusted using a temperature adjustment device. The battery to be predicted can be the battery of a battery-powered device such as a vehicle. For example, the battery to be predicted can be a vehicle's power battery. Temperature adjustment modes include, but are not limited to, direct heating, liquid cooling, liquid heating, air cooling, air heating, direct heating, or direct cooling. Each temperature adjustment mode has pre-set corresponding temperature prediction parameters. Once the temperature adjustment mode is determined, the corresponding parameters can be obtained in a manner known to those skilled in the art.

[0042] Step S120: Determine the environmental factor temperature change sub-parameter, heat dissipation error sub-parameter, battery self-heating sub-parameter, and temperature adjustment sub-parameter based on the temperature prediction parameters.

[0043] Among them, the environmental factor temperature change sub-parameter characterizes the temperature change rate of the battery to be predicted caused by the ambient temperature, the heat dissipation error sub-parameter characterizes the temperature change rate of the battery to be predicted caused by the internal temperature difference of the battery, the battery self-heating sub-parameter characterizes the temperature change rate of the battery to be predicted caused by the battery self-heating effect, and the temperature adjustment sub-parameter characterizes the temperature change rate of the battery to be predicted caused by the temperature adjustment device adjusting the temperature of the battery to be predicted.

[0044] It is understood that, regarding the factors of temperature change, the embodiments of this application mainly consider four factors: environmental factors, internal temperature difference of the battery, battery self-heating effect, and internal temperature change of the battery caused by the temperature adjustment device adjusting the temperature of the battery to be predicted. Of course, those skilled in the art can also add other dimensions of factors on this basis, and only need to add the rate of change caused by the new factors in the subsequent process.

[0045] Step S130: The environmental factor temperature change sub-parameter, heat dissipation error sub-parameter, battery self-heating sub-parameter, and temperature adjustment sub-parameter are superimposed to obtain the predicted battery internal temperature change rate.

[0046] Step S140: Determine the predicted internal temperature of the battery to be predicted based on the predicted internal temperature change rate of the battery and the current average temperature of the battery in the temperature prediction parameters.

[0047] As an example, based on the superposition of the above factors, the total predicted rate of change of the battery's internal temperature can be obtained in order to understand the trend of the battery's internal temperature change. The battery temperature change can be observed by constructing a battery temperature prediction curve, and the battery's internal temperature at a certain moment can be predicted.

[0048] In one embodiment, regardless of the temperature adjustment mode adopted by the battery, the environmental factor temperature change sub-parameter, heat dissipation error sub-parameter, and battery self-heating sub-parameter are all based on factors such as the battery's environment and characteristics. Therefore, when performing battery internal temperature prediction, the temperature prediction parameters corresponding to the environmental factor temperature change sub-parameter, heat dissipation error sub-parameter, and battery self-heating sub-parameter can be acquired and calculated to obtain the corresponding values. Based on the currently adopted temperature adjustment mode, the remaining temperature prediction parameters are acquired, and the corresponding calculation functions are determined and calculated to obtain the temperature adjustment sub-parameters.

[0049] Taking the battery to be predicted as a power battery as an example, the environmental factor temperature change sub-parameters are obtained by predicting the heat exchange power between the power battery and the environment in the current state. The temperature prediction parameters include the current ambient temperature, the current average battery temperature, the battery's effective heat transfer area, the battery's equivalent heat transfer coefficient, the battery's initial total heat capacity, the current highest temperature and the current lowest temperature, and the battery mass.

[0050] In one embodiment, the determination of the environmental factor temperature change sub-parameter includes: determining a first temperature difference based on the current average battery temperature and the current ambient temperature; and determining the environmental factor temperature change sub-parameter based on the battery's effective heat transfer area, the battery's equivalent heat transfer coefficient, the battery's initial total heat capacity, the battery's mass, and the first temperature difference.

[0051] Following the above embodiments, the environmental factor temperature change sub-parameters are determined based on the battery's effective heat transfer area, battery equivalent heat transfer coefficient, battery initial total heat capacity, battery mass, and a first temperature difference. This includes: determining the battery's total heat capacity after attenuation using a preset fitting attenuation function and the battery's initial total heat capacity; and determining the environmental factor temperature change sub-parameters based on the battery's effective heat transfer area, battery equivalent heat transfer coefficient, battery total heat capacity after attenuation, battery mass, and the first temperature difference. The temperature prediction parameters also include the preset fitting attenuation function. The preset fitting attenuation function will have corresponding values ​​as time changes, and its value is greater than 0 and less than or equal to 1.

[0052] In one embodiment, the constant portion of the following formula can also be pre-calculated and stored as a constant locally in the vehicle or locally in the execution object of the battery internal temperature prediction method, so that it can be called when needed.

[0053] Considering the uneven heat exchange process between the power battery and the environment, which leads to a large temperature difference inside the battery, a heat dissipation error sub-parameter needs to be introduced to compensate for this temperature difference. As an example, the heat dissipation error sub-parameter is determined by: determining a second temperature difference based on the current highest and lowest temperatures of the battery to be predicted; and determining the heat dissipation error sub-parameter based on the second temperature difference.

[0054] Following the above embodiments, determining the heat dissipation error sub-parameter based on the second temperature difference includes: correcting the second temperature difference using a preset correction factor to obtain the heat dissipation error sub-parameter, and the temperature prediction parameter further includes the preset correction factor. The preset correction factor includes a first correction factor and a second correction factor; the first correction factor is used for linear correction of the second temperature difference, and the second correction factor is used for nonlinear correction of the second temperature difference.

[0055] As an example, taking a power battery as the subject of the prediction, the dominant factor in the natural cooling / heating process of a power battery is ambient temperature. Its basic heat transfer equation is:

[0056]

[0057] Among them, T avg T represents the current average battery temperature. env The ambient temperature is given, h is the battery's equivalent heat transfer coefficient, A is the effective heat dissipation area of ​​the battery pack, and C is the current ambient temperature. bat Let t be the initial total heat capacity of the battery, and t be time. To predict the rate of temperature change inside the battery, m bat For battery quality.

[0058] The average battery temperature can be calculated by averaging the temperature values ​​collected by multiple temperature sensors in the battery, or it can be obtained by other methods known to those skilled in the art.

[0059] Considering that the initial total heat capacity of the battery changes as its lifespan decreases, a preset fitting decay function f(t) should be added, i.e.:

[0060]

[0061] Among them, T avg T represents the current average battery temperature. env The ambient temperature is given, h is the battery's equivalent heat transfer coefficient, A is the effective heat dissipation area of ​​the battery pack, and C is the current ambient temperature. bat Let be the initial total heat capacity of the battery, t be time, and f(t) be the preset fitted decay function. To predict the rate of temperature change inside the battery, m bat For battery quality.

[0062] Under different environmental conditions, uneven heat exchange between the power battery and the environment leads to significant temperature differences within the battery, with the difference between the highest and lowest temperatures potentially exceeding 8°C. Therefore, nonlinear correction factors ε and k are needed to address heat dissipation errors caused by battery non-uniformity. The main heat transfer equation for the natural heating / cooling module is:

[0063]

[0064] Among them, T avg T represents the current average battery temperature. env The ambient temperature is given, h is the battery's equivalent heat transfer coefficient, A is the effective heat dissipation area of ​​the battery pack, and C is the current ambient temperature. bat Let T be the initial total heat capacity of the battery, t be time, and f(t) be the preset fitted decay function. max The current highest temperature, T min The current lowest temperature, ε and k are preset correction factors. To predict the rate of temperature change inside the battery, m bat For battery quality.

[0065] During battery use, the self-heating effect cannot be ignored. The equivalent resistance method is used to represent the irreversible process inside the battery as a resistor, and the heating power is calculated by combining the battery current under different modes, thus obtaining the battery self-heating sub-parameters. In one embodiment, the determination of the battery self-heating sub-parameters includes: determining the self-heating power based on the operating condition efficiency factor, the battery current under operating conditions, and the battery equivalent resistance; and determining the battery self-heating sub-parameters based on the self-heating power, battery mass, and initial total heat capacity of the battery. The temperature prediction parameters include the operating condition efficiency factor, the battery current under operating conditions, the battery equivalent resistance, the initial total heat capacity of the battery, and the battery mass. If the operating condition is discharge, the battery current under operating conditions is the discharge current, and the operating condition efficiency factor is the discharge efficiency factor; if the operating condition is charging, the battery current under operating conditions is the charging current, and the operating condition efficiency factor is the charging efficiency factor. The operating condition efficiency factor can be set according to the needs of those skilled in the art.

[0066] Following the above embodiments, if the operating condition is discharge and the battery internal temperature prediction method is applied to a vehicle, the method further includes: if the vehicle's driving condition is urban, determining the discharge operating condition battery current based on historical discharge current; if the vehicle's driving condition is high-speed, determining the discharge operating condition battery current based on a preset driving operating condition current curve. As an example, the historical discharge current can be based on the vehicle's current journey, or historical journeys including the current journey, or the statistical average of historical discharge currents under urban conditions during historical journeys. The preset driving operating condition current curve can be, for example, the current curve for Chinese light-duty vehicles; the specific solution method can be determined through interpolation, etc., which will not be elaborated here.

[0067] Due to significant differences in operating conditions, different modes are used to estimate the self-heating power Q. shTo ensure prediction accuracy, the equivalent resistance method is used, treating the irreversible process inside the battery as a resistor, and then calculating the heating power by combining the battery current under different modes. During charging, the charging current is constrained by the battery's SOC (State of Charge) and temperature, and the battery's internal resistance also changes with temperature and SOC. Therefore, an operating condition efficiency factor θ needs to be introduced for correction. That is:

[0068] I chrg =f(SOC,T) Formula (4),

[0069] R cell =g(SOC,T) Formula (5),

[0070] Q sh =θ·I chrg 2 ·R cell Formula (6),

[0071] Among them, I chrg The current is the battery current under charging conditions, T is the current average internal temperature of the battery, SOC is the state of charge of the battery to be predicted, and R is the current under charging conditions. celk Q is the battery's equivalent resistance. sh θ represents the self-heating power, and θ is the charging efficiency factor.

[0072] Under discharge conditions, data statistical processing can be performed by distinguishing between urban and high-speed operating conditions. Urban operating conditions involve low-frequency, high-amplitude pulse currents, and the average current I can be calculated using historical data. urban This serves as the battery current under discharge conditions; under high-speed conditions, it involves continuous medium-to-high rate discharge with strong current stability, and current curve interpolation based on the driving conditions of Chinese light-duty vehicles is used. Simultaneously, an efficiency factor μ needs to be introduced for correction, i.e.:

[0073] Q sh =μ·I dchrg 2 ·R cell Formula (7),

[0074] Among them, Q sh I is the self-heating power, μ is the discharge efficiency factor, and I is the self-heating power. dchrg R is the battery current under discharge conditions. cell This is the equivalent resistance of the battery.

[0075] Active heating methods for power batteries include direct heating (heating film heating) and indirect heating (liquid heat, direct heat), while active cooling methods include direct cooling (air cooling) and indirect cooling (liquid cooling, direct cooling).

[0076] In one embodiment, if the temperature adjustment mode is direct heating, determining the predicted internal temperature of the battery to be predicted based on the predicted internal temperature change rate of the battery includes: determining the predicted internal temperature of the battery to be predicted based on the predicted internal temperature change rate of the battery and the current average battery temperature, wherein the current average battery temperature is obtained based on temperature prediction parameters; wherein the temperature prediction parameters include the current average battery temperature.

[0077] As an example, if the temperature adjustment mode is direct heating, the predicted internal temperature of the battery to be predicted is determined based on the predicted rate of change of the internal temperature of the battery and the current average temperature of the battery in the temperature prediction parameters. This includes: obtaining the time difference between the prediction time and the current time; determining the predicted temperature difference based on the time difference and the predicted rate of change of the internal temperature of the battery; and determining the predicted internal temperature of the battery to be predicted at the prediction time based on the predicted temperature difference and the current average temperature of the battery.

[0078] Following the above embodiments, the method for determining the temperature adjustment sub-parameter includes: adjusting the power of the battery direct heating source according to the direct heating efficiency parameter to obtain the adjusted heat source power; determining the temperature adjustment sub-parameter based on the adjusted heat source power, battery mass, and initial total heat capacity of the battery; wherein, the temperature prediction parameters include the direct heating efficiency parameter, the power of the battery direct heating source, the battery mass, and the initial total heat capacity of the battery.

[0079] The following is an example of the derivation process for predicting the heat exchange power between the power battery and the environment in the current state:

[0080] Direct heating of a battery involves placing a heat source in direct contact with the battery cell. Heat generated by the heat source is directly transferred to the battery via heat conduction, without any additional medium. Therefore, this process mainly consists of direct heating of the battery, heat generation within the battery itself, and heat exchange between the battery and its surroundings (natural heating / cooling). The thermodynamic balance equation for this heat transfer process can be expressed as:

[0081]

[0082] Among them, P dir Q is the power of the direct heating source for the battery. h For the battery's natural heat transfer power, m bat For battery quality, C bat This represents the initial total heat capacity of the battery. To predict the rate of change of internal temperature in the battery, the natural heat transfer power of the battery is obtained by superimposing environmental factors, heat dissipation error factors, and battery self-heating factors.

[0083] Although the thermal efficiency of direct heating devices inside the battery is usually high, it is not 100%, so the direct heating efficiency parameter η is introduced.

[0084] The battery temperature change prediction function under direct heating can be expressed as:

[0085]

[0086] Among the above formulas, the formula above is the battery temperature change prediction function under charging conditions, and the formula below is the battery temperature change prediction function under charging conditions. T avg T represents the current average battery temperature. env The ambient temperature is given, h is the battery's equivalent heat transfer coefficient, A is the effective heat dissipation area of ​​the battery pack, and C is the current ambient temperature. bat Let T be the initial total heat capacity of the battery, t be time, and f(t) be the preset fitted decay function. max The current highest temperature, T min The current lowest temperature, ε and k are preset correction factors, I chrg Where θ is the battery current under charging conditions, μ is the charging efficiency factor, and I is the discharging efficiency factor. dchrg R is the battery current under discharge conditions. cell P is the equivalent resistance of the battery. dir For the direct heating source power of the battery, m bat Where η is the battery mass and η is the direct heating efficiency parameter. To predict the rate of change of the battery's internal temperature, for the function above, it represents the predicted rate of change of the battery's internal temperature under charging conditions, and for the function below, it represents the predicted rate of change of the battery's internal temperature under discharging conditions.

[0087] The current highest and lowest temperatures can be obtained by measuring the maximum and minimum values ​​collected by multiple temperature sensors within the battery, or they can be determined by other means known to those skilled in the art.

[0088] In one embodiment, if the temperature adjustment mode is liquid cooling, liquid heating, air cooling, air heating, direct heating, or direct cooling, determining the predicted internal temperature of the battery to be predicted based on the predicted internal temperature change rate of the battery and the current average battery temperature in the temperature prediction parameters includes: determining the predicted heat transfer medium temperature change rate according to the temperature prediction parameters; determining the predicted heat transfer medium temperature change rate at the prediction time based on the predicted heat transfer medium temperature change rate and the current average heat transfer medium temperature; obtaining the time difference between the prediction time and the current time; determining the predicted temperature difference based on the time difference and the predicted internal temperature change rate of the battery (determined based on the parameters at the current time); determining the predicted internal temperature of the battery to be predicted at the prediction time based on the predicted temperature difference and the current average battery temperature; adjusting the predicted internal temperature change rate of the battery (determined based on the parameters at the current time) using the predicted heat transfer medium temperature change rate to obtain the predicted internal temperature change rate of the battery at the prediction time; and determining the predicted internal temperature of the battery at the next prediction time based on the predicted internal temperature change rate of the battery at the prediction time and the predicted internal temperature of the battery.

[0089] In the above embodiments, the temperature of the heat transfer medium changes relatively significantly over time. Therefore, the predicted temperature of the heat transfer medium at future moments is obtained by calculating and predicting the rate of change of the heat transfer medium temperature. The influence of environmental factors on the battery temperature can be offset by a preset fitting attenuation function and a preset correction factor. This makes the predicted internal battery temperature obtained by this method more accurate.

[0090] In one embodiment, if the temperature adjustment mode is liquid cooling or liquid heating, the determination of the temperature adjustment sub-parameters includes: determining a third temperature difference based on the current heat transfer medium temperature and the current average battery temperature; determining the heat transfer power transferred by the heat transfer medium to the battery to be predicted based on the equivalent heat transfer area of ​​the heat transfer contact surface to the battery, the heat transfer coefficient of the heat transfer medium, and the third temperature difference; and determining the temperature adjustment sub-parameters based on the heat transfer power, battery mass, and initial total heat capacity of the battery. The temperature prediction parameters also include battery mass, initial total heat capacity of the battery, and the following parameters under liquid cooling or liquid heating mode: equivalent heat transfer area of ​​the heat transfer contact surface to the battery, current heat transfer medium temperature, and heat transfer coefficient of the heat transfer medium. The temperature adjustment device includes a heat transfer medium, and the temperature of the battery to be predicted is adjusted through the heat transfer medium. The heat transfer coefficient of the heat transfer medium can be determined in a manner known to those skilled in the art. In application, the value can be pre-calculated and stored for acquisition by this method, or it can be determined by obtaining the Reynolds number, Prandtl number, characteristic length of the flow heat transfer process, and thermal conductivity of the heat transfer medium. The Reynolds number and Prandtl number can be determined in a manner known to those skilled in the art.

[0091] Following the above embodiments, determining the predicted rate of change of the heat transfer medium temperature based on temperature prediction parameters includes: determining a fourth temperature difference based on the current heat transfer medium temperature and the current ambient temperature; determining the pipeline-environment heat loss transferred from the pipeline to the environment based on the equivalent heat transfer area of ​​the pipeline to the environment, the heat transfer coefficient of the heat transfer medium, and the fourth temperature difference; adjusting the compressor input power based on the heat transfer efficiency parameters of the heat transfer medium to obtain the adjusted compressor input power; and determining the predicted rate of change of the heat transfer medium temperature based on the pipeline-environment heat loss, heat transfer power, adjusted compressor input power, heat transfer medium mass flow rate, and heat transfer medium specific heat capacity. The temperature prediction parameters also include the heat transfer efficiency parameters of the heat transfer medium, the compressor input power, the current ambient temperature, and the following parameters in liquid-cooled or liquid-thermal mode: the equivalent heat transfer area of ​​the pipeline to the environment, the heat transfer medium mass flow rate, and the heat transfer medium specific heat capacity.

[0092] Indirect thermal management of batteries involves transferring heat by heating / cooling the heat transfer medium (liquid or air) surrounding the battery. Its heat transfer mechanism is more complex, involving multiple heat exchange processes. The derivation process of indirect heating / cooling is as follows:

[0093] Liquid heat / cooling technology involves a liquid heat transfer medium, whose temperature is altered by an auxiliary heat / cold source, flowing along a predetermined piping system under the action of a circulating pump. Through close contact between the pipes and the battery, heat is transferred between the liquid medium and the battery via thermal conduction, thereby achieving precise temperature control of the battery. In this heat transfer process, the temperature change of the heat transfer medium is influenced by the power of the heat / cold source, the material properties of the heat transfer medium, and the system's heat loss. Its thermodynamic balance equation can be expressed as:

[0094]

[0095] Where, m med,l C is the mass flow rate of the heat transfer medium. med,l T is the specific heat capacity of the heat transfer medium. med,l P is the current temperature of the heat transfer medium. med,l η represents the input power of the heat source / cold source (compressor input power). med,l P represents the efficiency of the heat source / cold source (the heat transfer efficiency parameter of the heat transfer medium). loss,med.l This represents the power loss due to heat transfer.

[0096] Based on the current temperature adjustment mode, if it is liquid-cooled, the input power of the liquid compressor can be selected according to the input power of the cold source; otherwise, it can be selected according to the input power of the heat source. Other parameters are similar, and other modes are also similar, so they will not be elaborated further.

[0097] Simultaneously, the heat loss power of the heat transfer medium is determined by the heat loss Q from the pipeline environment. pipe,med,l and the heat Q transferred to the battery batThe relationship between the heat transfer coefficient h at the contact surface between the heat transfer medium and the battery and the fluid conditions and flow velocity v can be expressed by the Dittus-Boelter formula and the multiphase heat transfer correction parameter g(x,Pr). l F) Establish the relation:

[0098] Q pipe,med,l =h(v med,l )·A pipe,med,l ·(T med,l -T env ) formula (11),

[0099] Q bat =h(v med,l )·A med,l ·(T med,l -T avg ) formula (12),

[0100]

[0101] Among them, Q pipe,med,l h(v) represents the heat loss from the pipeline to the surrounding environment. med,l T represents the heat transfer coefficient of the heat transfer medium. med,l T represents the current temperature of the heat transfer medium. env T represents the current ambient temperature. avg Q represents the current average battery temperature. bat A represents the heat transfer power transferred by the heat transfer medium to the battery to be predicted. pipe,med,l A represents the equivalent heat transfer area of ​​the pipe to the environment (equivalent heat transfer area of ​​the pipe to the environment). med,l Re is the equivalent heat transfer area of ​​the heat transfer contact surface to the battery (the equivalent heat transfer area of ​​the heat transfer medium contact surface to the battery), Re is the Reynolds number, Pr is the Prandtl number, and L is the equivalent heat transfer area of ​​the heat transfer contact surface to the battery. l k is the characteristic length of the flow heat transfer process. l Where is the thermal conductivity of the heat transfer medium, x is the dryness of the heat transfer medium, and F is the two-phase friction coefficient. As an example, when the heat transfer medium is heated, n = 0.4, and when it is cooled, n = 0.3.

[0102] In liquid cooling or liquid heating modes, the heat transfer coefficient of the heat transfer medium can be corrected by multiple heat transfer correction parameters, which are determined based on the dryness of the heat transfer medium, the two-phase friction coefficient, and Prandtl number.

[0103] After integrating the above formulas, an example method for determining the predicted rate of change of heat transfer medium temperature is as follows:

[0104]

[0105] in, To predict the rate of temperature change of the heat transfer medium, m med,l C is the mass flow rate of the heat transfer medium. med,l P is the specific heat capacity of the heat transfer medium. med,l η represents the input power of the heat source / cold source (compressor input power). med,l The efficiency of the heat source / cold source (heat transfer efficiency parameter of the heat transfer medium), h(v) med,l T represents the heat transfer coefficient of the heat transfer medium. med,l T represents the current temperature of the heat transfer medium. env T represents the current ambient temperature. avg Given the current average battery temperature, A pipe,med,l A represents the equivalent heat transfer area of ​​the pipe to the environment (equivalent heat transfer area of ​​the pipe to the environment). med,l This refers to the equivalent heat transfer area of ​​the heat transfer contact surface to the battery (the equivalent heat transfer area of ​​the heat transfer medium contact surface to the battery).

[0106] For heat transfer between the heat transfer contact surface and the battery, the heat transfer power input to the battery by the heat transfer medium is considered, along with the battery's self-heating and heat loss to the environment. Its thermodynamic equilibrium equation is:

[0107]

[0108] Where, m bat For battery quality, C bat This represents the initial total heat capacity of the battery. To predict the rate of temperature change inside the battery, Q bat Q is the heat transfer power transferred by the heat transfer medium to the battery to be predicted. h This refers to the battery's natural heat transfer power.

[0109] The battery's natural heat transfer power is obtained by combining environmental factors, heat dissipation error factors, and battery self-heating factors.

[0110] Combining with the previous Q bat With Q sh The model yields the following battery temperature change prediction function:

[0111]

[0112] in, To predict the rate of temperature change of the heat transfer medium, m med,l C is the mass flow rate of the heat transfer medium. med,l P is the specific heat capacity of the heat transfer medium. med,l η represents the input power of the heat source / cold source (compressor input power). med,l The efficiency of the heat source / cold source (heat transfer efficiency parameter of the heat transfer medium), h(v) med,l T represents the heat transfer coefficient of the heat transfer medium. med,lT represents the current temperature of the heat transfer medium. env T represents the current ambient temperature. avg Given the current average battery temperature, A pipe,med,l A represents the equivalent heat transfer area of ​​the pipe to the environment (equivalent heat transfer area of ​​the pipe to the environment). med,l This refers to the equivalent heat transfer area of ​​the heat transfer contact surface to the battery (the equivalent heat transfer area of ​​the heat transfer medium contact surface to the battery). To predict the rate of change of the battery's internal temperature, for the function , it represents the predicted rate of change of the battery's internal temperature under charging conditions; for the function , it represents the predicted rate of change of the battery's internal temperature under discharging conditions, where h is the battery's equivalent heat transfer coefficient, A is the effective heat dissipation area of ​​the battery pack, and C... bat Let T be the initial total heat capacity of the battery, t be time, and f(t) be the preset fitted decay function. max The current highest temperature, T min The current lowest temperature, ε and k are preset correction factors, I chrg Where θ is the battery current under charging conditions, μ is the charging efficiency factor, and I is the discharging efficiency factor. dchrg R is the battery current under discharge conditions. cell m is the equivalent resistance of the battery. bat For battery quality.

[0113] It should be noted that the temperature rise of the heat transfer medium has a limited range, which is jointly limited by the thermal management logic, material characteristics, and heating pipeline. When analyzing the thermal management system, when the temperature of the heat transfer medium reaches the preset target temperature or the maximum / minimum temperature, the system will adjust the medium flow rate and the output power of the heat source / cold source accordingly, so that the power equals the circuit heat loss and the battery exchange power, to ensure that the battery temperature change rate is zero, thereby maintaining the battery temperature in a stable state.

[0114] In one embodiment, if the temperature adjustment mode is air cooling or air heating, the determination of the temperature adjustment sub-parameters includes: determining a fifth temperature difference based on the current heat transfer medium temperature and the current average battery temperature; determining the heat transfer power transferred by the heat transfer medium to the battery to be predicted based on the equivalent heat transfer area of ​​the heat transfer contact surface to the battery, the heat transfer coefficient of the heat transfer medium, and the fifth temperature difference; and determining the temperature adjustment sub-parameters based on the heat transfer power, battery mass, and initial total heat capacity of the battery. The temperature prediction parameters also include battery mass, initial total heat capacity of the battery, and the equivalent heat transfer area of ​​the heat transfer contact surface to the battery, the current heat transfer medium temperature, and the heat transfer coefficient of the heat transfer medium under air cooling or air heating modes. The temperature adjustment device includes a heat transfer medium, which is used to adjust the temperature of the battery to be predicted.

[0115] Following the above embodiments, determining the predicted rate of change of the heat transfer medium temperature based on temperature prediction parameters includes: determining a sixth temperature difference based on the current heat transfer medium temperature and the current ambient temperature; determining the pipeline-environment heat loss transferred from the pipeline to the environment based on the equivalent heat transfer area of ​​the pipeline to the environment, the heat transfer coefficient of the heat transfer medium, and the sixth temperature difference; adjusting the compressor input power based on the heat transfer efficiency parameters of the heat transfer medium to obtain the adjusted compressor input power; and determining the predicted rate of change of the heat transfer medium temperature based on the pipeline-environment heat loss, heat transfer power, adjusted compressor input power, heat transfer medium mass flow rate, and heat transfer medium specific heat capacity. The temperature prediction parameters also include the heat transfer efficiency parameters of the heat transfer medium, the compressor input power, the current ambient temperature, and the equivalent heat transfer area of ​​the pipeline to the environment, the heat transfer medium mass flow rate, and the heat transfer medium specific heat capacity under air-cooled or air-heated modes.

[0116] For air-heated / air-cooled battery technology, a heated / room-temperature gaseous heat transfer medium is transported to the battery along a specific piping system, directly contacting the battery surface and transferring heat through convection and conduction. The temperature of the gas is affected by the output power of the heat source / blower, the material properties of the heat transfer medium, and the system's heat loss. Its thermodynamic equilibrium state is described by the corresponding thermodynamic equations.

[0117]

[0118] Q pipe,med,g =h(v med,g )·A pipe,med,g ·(T med,g -T env ) formula (18),

[0119] Q bat =h(v med,g )·A med2 ·(T med,g -T avg ) formula (19),

[0120] Where, m med,g C is the mass flow rate of the heat transfer medium. med,g T is the specific heat capacity of the heat transfer medium. med,g P represents the current temperature of the heat transfer medium. In air-cooled conditions, the temperature of the heat transfer medium is equal to the ambient temperature. loss,med,g P is the heat loss power of the heat transfer medium. med,g η represents the input power of the heat source / blower (compressor input power). med,g Q represents the efficiency of the heat source / blower (the heat transfer efficiency parameter of the heat transfer medium). pipe,med,g h(v) represents the heat loss from the pipeline to the environment. med,g ) represents the heat transfer coefficient of the heat transfer medium, v med,g T is the flow rate of the heat transfer medium.env T represents the current ambient temperature. avg Q represents the current average battery temperature. bat A represents the heat transfer power transferred by the heat transfer medium to the battery to be predicted. pipe,med,g A represents the equivalent heat transfer area of ​​the pipe to the environment (equivalent heat transfer area of ​​the pipe to the environment). med2 This refers to the equivalent heat transfer area of ​​the heat transfer contact surface to the battery (the equivalent heat transfer area of ​​the heat transfer medium contact surface to the battery). To predict the rate of temperature change of the heat transfer medium.

[0121] After integrating the above formulas, an example method for determining the predicted rate of change of heat transfer medium temperature is as follows:

[0122]

[0123] in, To predict the rate of temperature change of the heat transfer medium, η med,g P represents the efficiency of the heat source / blower (the heat transfer efficiency parameter of the heat transfer medium). med,g The input power of the heat source / blower (compressor input power), h(v) med,g A is the heat transfer coefficient of the heat transfer medium. pipe,med,g A represents the equivalent heat transfer area of ​​the pipe to the environment (equivalent heat transfer area of ​​the pipe to the environment). med2 T is the equivalent heat transfer area of ​​the heat transfer contact surface of the battery. env T represents the current ambient temperature. avg T represents the current average battery temperature. med,d The current temperature of the heat transfer medium is given. During air cooling, the temperature of the heat transfer medium is equal to the ambient temperature. (m) med,g C is the mass flow rate of the heat transfer medium. med,g is the specific heat capacity of the heat transfer medium.

[0124] Based on the heat transfer model of the battery mentioned above, the battery temperature change prediction function is obtained as follows:

[0125]

[0126] in, To predict the rate of temperature change of the heat transfer medium, η med,g P represents the efficiency of the heat source / blower (the heat transfer efficiency parameter of the heat transfer medium). med,g The input power of the heat source / blower (compressor input power), h(v) med,g A is the heat transfer coefficient of the heat transfer medium. pipe,med,g A represents the equivalent heat transfer area of ​​the pipe to the environment (equivalent heat transfer area of ​​the pipe to the environment). med2 T is the equivalent heat transfer area of ​​the heat transfer contact surface of the battery.env T represents the current ambient temperature. avg T represents the current average battery temperature. med,g T represents the current temperature of the heat transfer medium during air cooling. med,g Equal to the ambient temperature, m med,g C is the mass flow rate of the heat transfer medium. med,g Specific heat capacity of the heat transfer medium To predict the rate of change of the battery's internal temperature, for the function , it represents the predicted rate of change of the battery's internal temperature under charging conditions; for the function , it represents the predicted rate of change of the battery's internal temperature under discharging conditions, where h is the battery's equivalent heat transfer coefficient, A is the effective heat dissipation area of ​​the battery pack, and C... bat Let T be the initial total heat capacity of the battery, t be time, and f(t) be the preset fitted decay function. max The current highest temperature, T min The current lowest temperature, ε and k are preset correction factors, I chrg Where θ is the battery current under charging conditions, μ is the charging efficiency factor, and I is the discharging efficiency factor. dchrg R is the battery current under discharge conditions. cell m is the equivalent resistance of the battery. bat For battery quality.

[0127] It should be noted that the temperature rise of the heat transfer medium has a limited range, which is jointly limited by the thermal management logic, material characteristics, and heating pipeline. When analyzing the thermal management system, when the temperature of the heat transfer medium reaches the preset target temperature or the maximum / minimum temperature, the system will adjust the medium flow rate and the output power of the heat source / cold source accordingly, so that the power equals the circuit heat loss and the battery exchange power, to ensure that the battery temperature change rate is zero, thereby maintaining the battery temperature in a stable state.

[0128] In one embodiment, if the temperature adjustment mode is direct heating, the method for determining the temperature adjustment sub-parameters includes: determining a seventh temperature difference based on the saturation temperature of the gaseous heat transfer medium and the current average battery temperature; determining an eighth temperature difference based on the current average battery temperature, the saturation temperature of the gaseous heat transfer medium, and the temperature of the heat transfer medium at the compressor outlet; and determining the heat transfer medium based on the heat transfer area of ​​the gaseous region of the heat transfer medium in the direct heating process, the heat transfer area of ​​the liquid region of the heat transfer medium in the direct heating process, the heat transfer coefficient of the gas-liquid mixture in the direct heating process, the heat transfer coefficient of the liquid in the direct heating process, the seventh temperature difference, and the eighth temperature difference. The system aims to predict the heat release power of the battery. Temperature adjustment sub-parameters are determined based on the heat release power, battery mass, and initial total heat capacity of the battery. These temperature prediction parameters include battery mass, initial total heat capacity, saturation temperature of the gaseous heat transfer medium in direct heating mode, temperature of the heat transfer medium at the compressor outlet, heat transfer area of ​​the gaseous region of the heat transfer medium in the direct heating process, heat transfer area of ​​the liquid region of the heat transfer medium in the direct heating process, heat transfer coefficient of the gas-liquid mixture in the direct heating process, and heat transfer coefficient of the liquid heat transfer medium in the direct heating process. The temperature adjustment device includes a heat transfer medium, which is used to adjust the temperature of the battery to be predicted.

[0129] Following the above embodiments, determining the predicted rate of change of the heat transfer medium temperature based on the temperature prediction parameters includes: determining the ninth temperature difference based on the heat transfer medium temperature at the compressor outlet and the current ambient temperature; determining the environmental heat loss of the heat transfer medium pipeline in the direct heating process transferred to the environment based on the equivalent heat transfer area of ​​the heat transfer medium pipeline in the direct heating process to the environment, the gas phase heat transfer coefficient of the heat transfer medium in the direct heating process, and the ninth temperature difference; adjusting the compressor input power based on the compressor mechanical efficiency to obtain the adjusted compressor input power; and determining the predicted rate of change of the heat transfer medium temperature based on the environmental heat loss of the heat transfer medium pipeline in the direct heating process, the heat release power, the adjusted compressor input power, the total mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium. The temperature prediction parameters also include the heat transfer medium temperature at the compressor outlet in the direct heating mode, the current ambient temperature, the equivalent heat transfer area of ​​the heat transfer medium pipeline in the direct heating process to the environment, the gas phase heat transfer coefficient of the heat transfer medium in the direct heating process, the compressor mechanical efficiency, the compressor input power, the total mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium.

[0130] Direct heating and direct cooling technologies for batteries exist within the same system, but the flow path of the heat transfer medium is altered by controlling different solenoid valves. In direct heating, the compressor compresses the low-temperature, low-pressure heat transfer medium into a high-temperature, high-pressure gas, which then enters the battery to release heat. In direct cooling, the heat transfer medium is compressed by the compressor, exchanges heat with the environment in the condenser, and then flows through an expansion valve for throttling and pressure reduction, becoming a low-temperature, low-pressure gas-liquid mixture that enters the battery to absorb heat.

[0131] By adjusting the compressor speed to change the flow rate of the heat transfer medium, rapid response and stable control of the battery temperature are achieved. A sensor is placed at the compressor outlet, condenser outlet, and power battery coolant outlet to collect temperature and pressure data. The derivation process of the direct heating method is as follows:

[0132] The compressor compresses the low-temperature, low-pressure heat transfer medium, thereby outputting a high-temperature, high-pressure heat transfer medium. Its output power is P. comp .

[0133]

[0134] Where, m cool η is the total mass flow rate of the heat transfer medium. m For compressor mechanical efficiency, To predict the rate of temperature change of the heat transfer medium, C cool P is the specific heat capacity of the heat transfer medium. comp P is the input power of the compressor. loss , cool This refers to the power loss of the heat transfer medium.

[0135] The compressed heat transfer medium is transported directionally to the condenser through pipelines. The heat loss power of the pipelines through heat exchange with the environment is Q. pipe1 :

[0136] Q pipe1 =h(v cool,g )·A pipe1 ·(T cool,comp -T emg ) formula (23),

[0137] Among them, A pipe1 T represents the equivalent heat transfer area of ​​the pipeline to the environment. cool,comp h(v) represents the temperature of the heat transfer medium at the compressor outlet. cool,g T represents the gas-phase heat transfer coefficient of the heat transfer medium in a direct heating process. env Q represents the current ambient temperature. pipe1 This refers to the heat loss in the pipeline environment transmitted to the surroundings.

[0138] The heat transfer medium enters the battery directly, releasing heat to the battery; the heat release power is Q. bat1 :

[0139]

[0140]

[0141] Among them, Q bat1 The heat transfer medium is the heat dissipation power of the cell to be predicted, h(v) g,sup The heat transfer coefficient of the gas-liquid mixture in the direct heating process, Ag,sup T represents the heat transfer area of ​​the gaseous region of the heat transfer medium in a direct heating process. cool,comp T represents the temperature of the heat transfer medium at the compressor outlet. cool,g,sat T is the saturation temperature of the gaseous heat transfer medium. avg h(v) represents the current average battery temperature. g,l1 A is the heat transfer coefficient of the gas-liquid mixture of the heat transfer medium. g,l1 The heat transfer area of ​​the liquid region of the heat transfer medium in a direct heating process, Re is the Reynolds number, Pr is the Prandtl number, and L is the heat transfer surface area. l Let n be the characteristic length of the flow heat transfer process, k be the thermal conductivity of the heat transfer medium, x be the dryness of the heat transfer medium, and F be the two-phase friction coefficient. As an example, n = 0.4 when the heat transfer medium is heated and n = 0.3 when it is cooled.

[0142] Because the heat exchange power in the heat transfer medium process is determined by Q pipe1 and Q bat1 Composition, after integrating the above formula, is:

[0143]

[0144] in, To predict the rate of temperature change of the heat transfer medium, h(v) g,sup A is the heat transfer coefficient of the gas-liquid mixture in the direct heating process. g,sup T represents the heat transfer area of ​​the gaseous region of the heat transfer medium in a direct heating process. cool,comp T represents the temperature of the heat transfer medium at the compressor outlet. cool,g,sat T is the saturation temperature of the gaseous heat transfer medium. avg h(v) represents the current average battery temperature. g,l1 A is the liquid heat transfer coefficient of the heat transfer medium in a direct heating process. g,l1 h(v) represents the heat transfer area of ​​the liquid region of the heat transfer medium in a direct heating process. l1 ) represents the liquid heat transfer coefficient of the heat transfer medium in a direct heating process, m cool η is the total mass flow rate of the heat transfer medium. m For the compressor's mechanical efficiency, C cool P is the specific heat capacity of the heat transfer medium. comp For the compressor input power, A pipe1 h(v) represents the equivalent heat transfer area of ​​the heat transfer medium pipeline to the environment in a direct heating process. cool,g T represents the gas-phase heat transfer coefficient of the heat transfer medium in a direct heating process. env This refers to the current ambient temperature.

[0145] Considering the battery's self-heating effect and the heat exchange power with the environment, the battery temperature change prediction function is obtained as follows:

[0146]

[0147] in, To predict the rate of temperature change of the heat transfer medium, h(v) g,sup A is the heat transfer coefficient of the gas-liquid mixture in the direct heating process. g,sup T represents the heat transfer area of ​​the gaseous region of the heat transfer medium in a direct heating process. cool,comp T represents the temperature of the heat transfer medium at the compressor outlet. cool,g,sat T is the saturation temperature of the gaseous heat transfer medium. avg h(v) represents the current average battery temperature. g,l1 A is the liquid heat transfer coefficient of the heat transfer medium in a direct heating process. g,l1 h(v) represents the heat transfer area of ​​the liquid region of the heat transfer medium in a direct heating process. l1 ) represents the liquid heat transfer coefficient of the heat transfer medium in a direct heating process, m cool η is the total mass flow rate of the heat transfer medium. m For the compressor's mechanical efficiency, C cool P is the specific heat capacity of the heat transfer medium. comp For the compressor input power, A pipe1 h(v) represents the equivalent heat transfer area of ​​the heat transfer medium pipeline to the environment in a direct heating process. cool,g T represents the gas-phase heat transfer coefficient of the heat transfer medium in a direct heating process. env The current ambient temperature, To predict the rate of change of battery internal temperature, a function is used to represent the predicted rate of change of battery internal temperature under charging conditions, and a function is used to represent the predicted rate of change of battery internal temperature under discharging conditions. Here, h is the battery's equivalent heat transfer coefficient, A is the effective heat dissipation area of ​​the battery pack, t is time, f(t) is a preset fitting decay function, and T... max The current highest temperature, T min The current lowest temperature, ε and k are preset correction factors, I chrg Where θ is the battery current under charging conditions, μ is the charging efficiency factor, and I is the discharging efficiency factor. dchrg R is the battery current under discharge conditions. cell m is the equivalent resistance of the battery. bat For battery quality, C bat This represents the initial total heat capacity of the battery.

[0148] In one embodiment, if the temperature adjustment mode is direct cooling, the method for determining the temperature adjustment sub-parameters includes: determining the tenth temperature difference based on the temperature of the expanded heat transfer medium and the current average battery temperature; determining the eleventh temperature difference based on the current average battery temperature and the saturation temperature of the liquid heat transfer medium; and determining the temperature difference between the expanded heat transfer medium and the battery to be predicted based on the heat transfer area of ​​the gaseous region of the heat transfer medium during the direct cooling process, the heat transfer area of ​​the gas-liquid mixed region of the heat transfer medium during the direct cooling process, the heat transfer coefficient of the gas-liquid mixed region of the heat transfer medium during the direct cooling process, the heat transfer coefficient of the gas phase of the heat transfer medium during the direct cooling process, the tenth temperature difference, and the eleventh temperature difference. The heat exchange absorbance power; the temperature adjustment sub-parameters are determined based on the heat absorbance power, battery mass, and initial total heat capacity of the battery; among which, the temperature prediction parameters also include battery mass, initial total heat capacity of the battery, and the following parameters in the direct cooling mode: heat transfer area of ​​the gaseous region of the heat transfer medium in the direct cooling process, heat transfer area of ​​the gas-liquid mixed region of the heat transfer medium in the direct cooling process, gas phase heat transfer coefficient of the heat transfer medium in the direct cooling process, gas-liquid mixed heat transfer coefficient of the heat transfer medium in the direct cooling process, temperature of the expanded heat transfer medium, and saturation temperature of the liquid heat transfer medium. The temperature adjustment device includes a heat transfer medium, and the temperature of the battery to be predicted is adjusted through the heat transfer medium.

[0149] Following the above embodiments, determining the predicted rate of change of the heat transfer medium temperature based on temperature prediction parameters includes: determining the fifteenth temperature difference based on the heat transfer medium temperature at the compressor outlet and the current ambient temperature; determining the heat loss of the heat transfer medium pipeline to the environment based on the equivalent heat transfer area of ​​the direct-heating process heat transfer medium pipeline to the environment, the gas-phase heat transfer coefficient of the direct-heating process heat transfer medium, and the fifteenth temperature difference; determining the twelfth temperature difference based on the saturation temperature of the gaseous heat transfer medium and the current ambient temperature; and determining the thirteenth temperature difference based on the current ambient temperature, the saturation temperature of the gaseous heat transfer medium, and the heat transfer medium temperature at the compressor outlet. Temperature difference; the heat transfer power of the heat transfer medium to the environment is determined based on the liquid heat transfer coefficient of the heat transfer medium in the direct heating process, the heat transfer area of ​​the gaseous region of the heat transfer medium in the direct heating process, the heat transfer area of ​​the liquid region of the heat transfer medium in the direct heating process, the heat transfer coefficient of the gas-liquid mixture of the heat transfer medium in the direct heating process, the twelfth temperature difference, and the thirteenth temperature difference; the fourteenth temperature difference is determined based on the temperature of the condensed heat transfer medium and the current ambient temperature; the equivalent heat transfer coefficient of the expansion valve to the environment, the equivalent heat transfer area of ​​the expansion to the environment, the equivalent heat transfer coefficient of the pipeline through which the liquid heat transfer medium is transported in the direct cooling process to the environment, and the equivalent heat transfer surface of the pipeline through which the heat transfer medium is transported in the direct cooling process to the environment. The heat loss transferred to the environment by the expansion valve pipeline is determined by the temperature difference between the product and the fourteenth temperature. The compressor input power is adjusted based on the compressor mechanical efficiency to obtain the adjusted compressor input power. The predicted rate of change of the heat transfer medium temperature is determined based on the environmental heat loss of the heat transfer medium pipeline in the direct heating process, the environmental heat release power, the heat loss transferred to the environment by the expansion valve pipeline, the heat absorption power, the adjusted compressor input power, the total mass flow rate of the heat transfer medium, and the specific heat capacity of the heat transfer medium. The temperature prediction parameters also include the following parameters in the direct heating mode: the temperature of the heat transfer medium at the compressor outlet, the current ambient temperature, and the equivalent heat transfer area of ​​the heat transfer medium pipeline to the environment in the direct heating process. The following parameters are considered in the direct heating process: gas phase heat transfer coefficient of the heat transfer medium, saturation temperature of the gaseous heat transfer medium, heat transfer area of ​​the gaseous region of the heat transfer medium in the direct heating process, heat transfer area of ​​the liquid region of the heat transfer medium in the direct heating process, liquid heat transfer coefficient of the heat transfer medium in the direct heating process, heat transfer coefficient of the gas-liquid mixture of the heat transfer medium in the direct heating process, temperature of the heat transfer medium after condensation, equivalent heat transfer coefficient of the expansion valve to the environment, equivalent heat transfer area of ​​the expansion valve to the environment, equivalent heat transfer coefficient of the pipeline for the liquid heat transfer medium in the direct cooling process to the environment, equivalent heat transfer area of ​​the pipeline for the heat transfer medium in the direct cooling process to the environment, compressor mechanical efficiency, compressor input power, total mass flow rate of the heat transfer medium, and specific heat capacity of the heat transfer medium.

[0150] As an example, the derivation process of the direct cooling method is as follows:

[0151] After being compressed, the heat transfer medium releases heat to the environment in the condenser, forming a flow path towards the expansion valve. The heat transfer power Q during this path is [not specified]. pipe2

[0152] Q pipe2 =h env,l ·A pipe2 ·(T cool,haet -T env ) formula (28),

[0153] Among them, h env,l A represents the equivalent heat transfer coefficient of the pipeline for transporting the liquid heat transfer medium in a direct cooling process to the environment. pipe2 T represents the equivalent heat transfer area of ​​the heat transfer medium pipeline in the direct cooling process to the environment. cool,haet T is the temperature of the heat transfer medium after expansion. env Q represents the current ambient temperature. pipe2 This refers to the direct cooling process of the heat transfer medium in the pipeline, which transfers heat to the environment.

[0154] The expansion valve in a direct-heating system acts as a throttling and pressure-reducing device. Treating this process as adiabatic, a semi-empirical modified model H(P) is established. plate ,(T plate -T env ),C v A v The heat transfer coefficient is corrected, and the heat transfer power Q within the expansion valve is increased. eev for:

[0155]

[0156] Among them, Q eev h is the heat loss transferred to the environment by the expansion valve pipeline. eev,l A represents the equivalent heat transfer coefficient of the expansion valve to the environment. eev To expand the equivalent heat transfer area to the environment, T cool,haet T is the temperature of the heat transfer medium after expansion. env P represents the current ambient temperature. haet ΔT is the condenser outlet pressure. haet C represents the subcooling at the condenser outlet. v A is the flow coefficient of the expansion valve. v This refers to the flow area of ​​the expansion valve orifice.

[0157] The expanded heat transfer medium exchanges heat with the battery, and the heat absorption power Q bat2 :

[0158] Q bat2 =h(v g,l2 )·A g,l2 (T cool,l,sat -T avg )+h(v g2 )·A g2 (T cool,eev -Tavg ) Formula (31)

[0159] Among them, Q bat2 h)v represents the heat absorption power of the expanded heat transfer medium exchanging heat with the battery to be predicted. g,l2 A is the heat transfer coefficient of the gas-liquid mixture in the direct cooling process. g,l2 T represents the heat transfer area of ​​the gas-liquid mixed region of the heat transfer medium in the direct cooling process. cool,l,sat T is the saturation temperature of the liquid heat transfer medium. avg h(v) represents the current average battery temperature. g2 A is the gas-phase heat transfer coefficient of the heat transfer medium in the direct cooling process. g2 T represents the heat transfer area of ​​the gaseous region of the heat transfer medium in the direct cooling process. cool,eev The temperature of the heat transfer medium after expansion.

[0160]

[0161] Where h(v) g,l2 Let n be the heat transfer coefficient of the gas-liquid mixture in the direct cooling process, x be the dryness fraction of the refrigerant, F be the two-phase friction coefficient, and n = 0.4 when the heat transfer medium is heated and n = 0.3 when cooled. Re is the Reynolds number, pr is the Prandtl number, and L is the refrigerant coefficient. l k is the characteristic length of the flow heat transfer process. l The thermal conductivity of the liquid heat transfer medium.

[0162] Considering the battery's self-heating effect and the heat exchange power with the environment, the battery temperature change prediction function is obtained as follows:

[0163]

[0164] in, To predict the rate of temperature change of the heat transfer medium, η m For the compressor's mechanical efficiency, P comp h(v) is the input power of the compressor. cool,g T represents the gas-phase heat transfer coefficient of the heat transfer medium in a direct heating process. cool,comp T represents the temperature of the heat transfer medium at the compressor outlet. env T represents the current ambient temperature. cool,eev A represents the temperature of the heat transfer medium after expansion. pipe1 T represents the equivalent heat transfer area of ​​the heat transfer medium pipeline to the environment in a direct heating process. cool,g,sat A is the saturation temperature of the gaseous heat transfer medium. g,sup h(v) represents the heat transfer area of ​​the gaseous region of the heat transfer medium in a direct heating process. g,sup h(v) represents the heat transfer coefficient of the gas-liquid mixture in a direct heating process. l1h(v) is the liquid heat transfer coefficient of the heat transfer medium in the direct heating process. g,l1 h(v) is the liquid heat transfer coefficient of the heat transfer medium in the direct heating process. g2 A is the gas-phase heat transfer coefficient of the heat transfer medium in the direct cooling process. g,l1 h is the heat transfer area of ​​the liquid region of the heat transfer medium in a direct heating process. eev,l A represents the equivalent heat transfer coefficient of the expansion valve to the environment. eev To expand the equivalent heat transfer area to the environment, T cool,haet h represents the temperature of the heat transfer medium after condensation. env,l A represents the equivalent heat transfer coefficient of the pipeline for transporting the liquid heat transfer medium in a direct cooling process to the environment. pipe2 h(v) represents the equivalent heat transfer area of ​​the heat transfer medium pipeline to the environment in the direct cooling process. g,l2 A is the heat transfer coefficient of the gas-liquid mixture in the direct cooling process. g,l2 T represents the heat transfer area of ​​the gas-liquid mixed region of the heat transfer medium in the direct cooling process. cool,l,sat T is the saturation temperature of the liquid heat transfer medium. avg h(v) represents the current average battery temperature. l2 A is the liquid heat transfer coefficient of the heat transfer medium in the direct cooling process. g2 The heat transfer area of ​​the gaseous region of the heat transfer medium in the direct cooling process, in m cool η is the total mass flow rate of the heat transfer medium. m For the compressor's mechanical efficiency, C cool Specific heat capacity of the heat transfer medium To predict the rate of change of battery internal temperature, a function is used to represent the predicted rate of change of battery internal temperature under charging conditions, and a function is used to represent the predicted rate of change of battery internal temperature under discharging conditions. Here, h is the battery's equivalent heat transfer coefficient, A is the effective heat dissipation area of ​​the battery pack, t is time, f(t) is a preset fitting decay function, and T... max The current highest temperature, T min The current lowest temperature, ε and k are preset correction factors, I chrg Where θ is the battery current under charging conditions, μ is the charging efficiency factor, and I is the discharging efficiency factor. dchrg R is the battery current under discharge conditions. cell m is the equivalent resistance of the battery. bat For battery quality, C bat This represents the initial total heat capacity of the battery.

[0165] This model allows users to input parameters such as the current ambient temperature, the battery's lowest temperature (current lowest temperature), the battery's highest temperature (current highest temperature), the battery's average temperature (current average battery temperature), the PTC (Positive Temperature Coefficient) heater's power, and the battery's operating conditions to obtain the battery's real-time temperature change rate, predict the battery's temperature change curve, and achieve refined thermal management of the battery by changing parameters such as the PTC's heat generation power and the coolant flow rate.

[0166] The battery internal temperature prediction method provided in the above embodiments obtains the temperature adjustment mode of the battery to be predicted and the temperature prediction parameters corresponding to the temperature adjustment mode. Based on the temperature prediction parameters, it determines environmental factor temperature change sub-parameters, heat dissipation error sub-parameters, battery self-heating sub-parameters, and temperature adjustment sub-parameters. This method considers the changes in battery internal temperature caused by environmental factors, battery internal temperature difference, battery self-heating effect, and temperature adjustment device adjusting the temperature of the battery to be predicted. It also superimposes the temperature change rate caused by these factors to obtain the predicted battery internal temperature change rate, and thus obtains the predicted battery internal temperature. This method performs temperature prediction from the perspective of a thermal management system, considering the influence of natural convection heat transfer of ambient temperature on the battery's natural cooling rate, as well as the differences brought about by different heat source transfer forms (wind heat / water heat), thereby improving the accuracy and reliability of battery internal temperature prediction.

[0167] Optionally, this method also establishes a power battery temperature prediction model, simulating the battery's natural heating / cooling and active heating / cooling processes. By inputting the ambient temperature, heat source heating power, cooling power, and coolant flow rate, the maximum, minimum, and average cell temperatures can be predicted. This method is applicable to current common thermal management architectures and can be adapted to different battery heating methods. Strategy modifications are made for different battery operating conditions, considering the impact of battery charging / discharging scenarios and low-speed / high-speed driving scenarios on the battery's self-heating effect. Furthermore, the impact of high and low temperature driving / parking on the battery pack's natural convection heat transfer is also considered, which helps improve the accuracy of the battery temperature prediction model results and enhances the model's universality for multiple battery application scenarios. This makes the predicted battery internal temperature more accurate and reliable.

[0168] Taking the application of this method to vehicles as an example, in order to adapt to different thermal management combinations in the vehicle, the various heating and cooling methods are decomposed into different modules, and combined for different vehicle models to form corresponding temperature models. See [link / reference]. Figure 2 , Figure 2This is a schematic diagram of a battery internal temperature prediction model provided in one embodiment of this application. In an exemplary specific embodiment, the power battery thermal management prediction model consists of two parts: natural heating / cooling and active heating / cooling. Active heating / cooling are two independent sub-modules. Active heating is divided into two heating methods: direct heating and indirect heating, and into four heating methods: PTC heating, heating film heating, direct heating, and liquid heating. Active cooling is divided into two cooling methods: direct cooling and indirect cooling, and into three cooling methods: air cooling, direct cooling, and liquid cooling. In a vehicle, generally one or two heating methods and one or two cooling methods, together with the natural heating / cooling part, form an independent battery thermal management mode, and the battery internal temperature prediction model is designed accordingly.

[0169] Please see Figure 3 , Figure 3 A specific schematic diagram of a battery internal temperature prediction method provided in an embodiment of this application is shown below. Figure 3 As shown, taking the application of this method to a vehicle as an example, the specific usage process is as follows:

[0170] In step S310, the battery thermal management system completes the power-on self-test to ensure that the startup is complete.

[0171] The battery thermal management system connects to the system upon power-on and first completes a power-on self-test to ensure the system is fault-free, initial conditions are normal, and hardware circuitry is functioning correctly. The next step is performed after power-on is complete.

[0172] It's understandable that the battery thermal management system needs to perform a self-check when the vehicle is powered on.

[0173] Step S320: Input environmental parameters and confirm the environmental self-heating / sub-cooling rate.

[0174] Input the environmental parameters of the vehicle into the system to complete the configuration of the natural heating / natural cooling model.

[0175] The environmental self-heating / sub-cooling rate is also known as the environmental factor temperature change sub-parameter, heat dissipation error sub-parameter, and battery self-heating sub-parameter.

[0176] For example, preset correction factors ε and k can be set or selected based on the vehicle's environment, as well as the configuration of the attenuation empirical function based on the vehicle's environment. The vehicle's environment includes, but is not limited to, the vehicle's location, season, weather, whether it is indoors or outdoors, and also includes determining the input environmental parameters and confirming relevant data on the environment's self-heating / self-cooling rate.

[0177] Step S330: Based on the vehicle's thermal management hardware configuration, confirm the active heating and active cooling methods of the thermal management system.

[0178] In a specific embodiment, as an example, the active heating and active cooling methods of the thermal management system are liquid cooling and liquid heating.

[0179] Step S340: The determined active heating and active cooling method estimation models are integrated with the self-heating / self-cooling model to form a battery temperature prediction system.

[0180] The prediction model obtained through system integration is shown in formula (16), and will not be elaborated here.

[0181] It is understandable that by selecting an appropriate method to calculate the temperature adjustment sub-parameter through the above approach, and then superimposing the temperature adjustment sub-parameter with the environmental factor temperature change sub-parameter, the heat dissipation error sub-parameter, and the battery self-heating sub-parameter, the predicted internal temperature of the battery can be obtained.

[0182] Step S350: Integrate the temperature prediction system with the vehicle behavior.

[0183] Since temperature prediction is inextricably linked to battery self-heating, and battery self-heating behavior differs significantly between discharge and charging conditions, it is necessary to integrate the temperature prediction system with overall vehicle behavior. For example, this could involve selecting an appropriate operating current.

[0184] By integrating a dual-mode thermal management mechanism of natural temperature variation and active regulation, the system can accurately predict the extreme and average cell temperatures based on ambient temperature, heat source power, and cooling parameters. It possesses strong compatibility with industry-standard thermal management architectures and flexible adaptability to multiple heating modes. Compared to related technologies, the method provided in this application innovatively introduces a multi-condition dynamic correction strategy. This strategy comprehensively considers the differences in battery self-heating effects caused by charging and discharging conditions, high and low speed driving, and the impact of ambient temperature on the natural heat exchange of the battery pack during driving / parking. This effectively improves the accuracy of extreme temperature prediction and the model's generalization ability, enhancing the technical applicability of thermal management strategies for all scenarios in new energy vehicles.

[0185] In one embodiment, a battery internal temperature prediction device is provided, which is used to perform the battery internal temperature prediction method provided in any of the above embodiments. See also... Figure 4 , Figure 4 A schematic diagram of a battery internal temperature prediction device provided in an embodiment of this application is shown below. Figure 4As shown, the battery internal temperature prediction device 400 includes an acquisition module 410, used to acquire the temperature adjustment mode of the battery to be predicted, and to acquire the temperature prediction parameters corresponding to the temperature adjustment mode. The battery to be predicted is temperature-adjusted by the temperature adjustment device. A sub-parameter determination module 420 is used to determine environmental factor temperature change sub-parameters, heat dissipation error sub-parameters, battery self-heating sub-parameters, and temperature adjustment sub-parameters based on the temperature prediction parameters. The environmental factor temperature change sub-parameter represents the rate of temperature change of the battery to be predicted due to the ambient temperature, and the heat dissipation error sub-parameter represents the rate of temperature change of the battery to be predicted due to the internal temperature difference of the battery. The battery self-heating sub-parameter characterizes the rate of temperature change of the battery to be predicted due to the battery self-heating effect, and the temperature adjustment sub-parameter characterizes the rate of temperature change of the battery to be predicted due to the temperature adjustment device adjusting the temperature of the battery to be predicted; the rate of change prediction module 430 is used to superimpose the environmental factor temperature change sub-parameter, the heat dissipation error sub-parameter, the battery self-heating sub-parameter, and the temperature adjustment sub-parameter to obtain the predicted rate of temperature change of the battery's internal temperature; the temperature prediction module 440 is used to determine the predicted internal temperature of the battery to be predicted based on the current predicted rate of temperature change of the battery's internal temperature and the current average temperature of the battery in the current temperature prediction parameters.

[0186] Specific limitations regarding the battery internal temperature prediction device can be found in the limitations of the battery internal temperature prediction method described above, and will not be repeated here. Each module in the aforementioned battery internal temperature prediction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the electronic device, or stored in software in the memory of the electronic device, so that the processor can call and execute the corresponding operations of each module.

[0187] In this embodiment, the battery internal temperature prediction device is essentially equipped with multiple modules to execute the battery internal temperature prediction method in any of the above embodiments. The specific functions and technical effects can be referred to the above embodiments, and will not be repeated here.

[0188] In one embodiment, a vehicle is provided, the vehicle including a battery to be predicted and a battery management system, the battery management system including the battery internal temperature prediction device provided in the above embodiment, wherein: the battery management system is used to perform a power-on self-test when the vehicle is powered on; if the self-test is successful, it triggers the battery internal temperature prediction device to perform a prediction of the battery internal temperature.

[0189] For specific limitations regarding the vehicle, please refer to the limitations on the battery internal temperature prediction method mentioned above, which will not be repeated here. The various modules in the vehicle described above can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware within or independently of the processor in the electronic device, or stored in software within the memory of the electronic device, so that the processor can call and execute the corresponding operations of each module.

[0190] In this embodiment, the vehicle is essentially equipped with multiple modules to execute the vehicle-side measurement method in the battery internal temperature prediction method of any of the above embodiments. The specific functions and technical effects can be referred to the above embodiments, and will not be repeated here.

[0191] See Figure 5 , Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown below. Figure 5 As shown, this embodiment of the invention also provides an electronic device 500, including a processor 501, a memory 502, and a communication bus 503; the communication bus 503 is used to connect the processor 501 and the memory 502; the processor 501 is used to execute a computer program stored in the memory 502 to implement the method described in any of the above embodiments.

[0192] This invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being used to cause a computer to perform the method provided in any of the above embodiments.

[0193] This application also provides a non-volatile readable storage medium storing one or more modules (programs). When these modules are applied to a device, they enable the device to execute the instructions containing the steps provided in this application.

[0194] This application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0195] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof. The aforementioned computer-readable medium can be included in the aforementioned electronic device; or it can exist independently and not assembled into the electronic device.

[0196] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0197] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0198] It should be understood that the terms "first," "second," etc., used in this application are used to distinguish similar objects and do not necessarily indicate a specific order or sequence. The technical features to which these terms are used can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0199] It should be understood that although the flowcharts provided in the embodiments of this application indicate the various steps with arrows, the order indicated by the arrows does not necessarily limit the implementation order of these steps. Those skilled in the art can perform these steps in other orders according to different implementation scenarios and requirements.

[0200] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for predicting the internal temperature of a battery, characterized in that, The method comprises: obtaining a temperature adjustment mode of a battery to be predicted, and obtaining a temperature prediction parameter corresponding to the temperature adjustment mode, the battery to be predicted being temperature-adjusted by a temperature adjustment device; determining an environmental factor temperature change sub-parameter, a heat dissipation error sub-parameter, a battery self-heating sub-parameter and a temperature adjustment sub-parameter according to the temperature prediction parameter, the environmental factor temperature change sub-parameter representing a temperature change rate of the battery to be predicted caused by an environmental temperature, the heat dissipation error sub-parameter representing a temperature change rate of the battery to be predicted caused by an internal temperature difference of the battery, the battery self-heating sub-parameter representing a temperature change rate of the battery to be predicted caused by a battery self-heating effect, and the temperature adjustment sub-parameter representing a temperature change rate of the battery to be predicted caused by temperature adjustment of the battery to be predicted by the temperature adjustment device; superimposing the environmental factor temperature change sub-parameter, the heat dissipation error sub-parameter, the battery self-heating sub-parameter and the temperature adjustment sub-parameter to obtain a predicted internal temperature change rate of the battery; determining a predicted internal temperature of the battery to be predicted based on the predicted internal temperature change rate of the battery and a current average temperature of the battery in the temperature prediction parameter.

2. The battery internal temperature prediction method of claim 1, wherein: the determination of the environmental factor temperature change sub-parameter comprises: determining a first temperature difference value according to a current average temperature of the battery and a current external environmental temperature; and determining the environmental factor temperature change sub-parameter based on a battery effective heat transfer area, a battery equivalent heat transfer coefficient, a battery initial total heat capacity, a battery mass and the first temperature difference value; the determination of the heat dissipation error sub-parameter comprises: determining a second temperature difference value according to a current maximum temperature and a current minimum temperature of the battery to be predicted; and determining the heat dissipation error sub-parameter based on the second temperature difference value; wherein the temperature prediction parameter comprises the current external environmental temperature, the current average temperature of the battery, the battery effective heat transfer area, the battery equivalent heat transfer coefficient, the battery initial total heat capacity, the battery mass, the current maximum temperature and the current minimum temperature.

3. The battery internal temperature prediction method of claim 2, wherein: determining the environmental factor temperature change sub-parameter based on the battery effective heat transfer area, the battery equivalent heat transfer coefficient, the battery initial total heat capacity, the battery mass and the first temperature difference value comprises: determining an attenuated battery total heat capacity by a preset fitting attenuation function and the battery initial total heat capacity, and determining the environmental factor temperature change sub-parameter based on the battery effective heat transfer area, the battery equivalent heat transfer coefficient, the attenuated battery total heat capacity, the battery mass and the first temperature difference value, the temperature prediction parameter further comprising the preset fitting attenuation function; determining the heat dissipation error sub-parameter based on the second temperature difference value comprises: correcting the second temperature difference value by a preset correction factor to obtain the heat dissipation error sub-parameter, the temperature prediction parameter further comprising the preset correction factor.

4. The battery internal temperature prediction method according to claim 1, wherein the determination of the battery self-heating sub-parameter comprises: determining a self-heating power according to a working condition efficiency factor, a working condition battery current and a battery equivalent resistance; and determining the battery self-heating sub-parameter based on the self-heating power, the battery mass and the initial total heat capacity of the battery; wherein the temperature prediction parameter comprises the operating condition efficiency factor, the operating condition battery current, the battery equivalent resistance, the initial total heat capacity of the battery and the battery mass; if the operating condition is discharging, the operating condition battery current is the discharging operating condition battery current, and the operating condition efficiency factor is the discharging efficiency factor; if the operating condition is charging, the operating condition battery current is the charging operating condition battery current, and the operating condition efficiency factor is the charging efficiency factor.

5. The battery internal temperature prediction method according to any one of claims 1 to 4, characterized by, if the temperature adjustment mode is direct heating, determining the predicted battery internal temperature of the battery to be predicted based on the predicted battery internal temperature change rate and the current battery average temperature in the temperature prediction parameter, comprising: obtaining a time difference value between the prediction time and the current time; determining a predicted temperature difference value according to the time difference value and the predicted battery internal temperature change rate; determining the predicted battery internal temperature of the battery to be predicted at the prediction time according to the predicted temperature difference value and the current battery average temperature.

6. The battery internal temperature prediction method according to claim 5, wherein the determination mode of the temperature adjustment sub-parameter comprises: adjusting the battery direct heating heat source power according to the direct heating efficiency parameter to obtain an adjusted heat source power; determining the temperature adjustment sub-parameter based on the adjusted heat source power, the battery mass and the initial total heat capacity of the battery; wherein the temperature prediction parameter comprises the direct heating efficiency parameter, the battery direct heating heat source power, the battery mass and the initial total heat capacity of the battery.

7. The battery internal temperature prediction method according to any one of claims 1 to 4, characterized by, if the temperature adjustment mode is liquid cooling, liquid heating, air cooling, air heating, direct heating or direct cooling, determining the predicted battery internal temperature of the battery to be predicted based on the predicted battery internal temperature change rate and the current battery average temperature in the temperature prediction parameter, comprising: determining a predicted heat transfer medium temperature change rate according to the temperature prediction parameter; determining a predicted heat transfer medium temperature change rate at the prediction time based on the predicted heat transfer medium temperature change rate and the current heat transfer medium average temperature; obtaining a time difference value between the prediction time and the current time; determining a predicted temperature difference value according to the time difference value and the predicted battery internal temperature change rate; determining the predicted battery internal temperature of the battery to be predicted at the prediction time according to the predicted temperature difference value and the current battery average temperature; adjusting the predicted battery internal temperature change rate by the predicted heat transfer medium temperature change rate and the current external environment temperature to obtain the predicted battery internal temperature change rate at the prediction time; determining the predicted battery internal temperature at the next prediction time based on the predicted battery internal temperature change rate at the prediction time and the predicted battery internal temperature.

8. The battery internal temperature prediction method according to claim 7, wherein if the temperature adjustment mode is liquid cooling or liquid heating, the determination mode of the temperature adjustment sub-parameter comprises: determining a third temperature difference value according to the current heat transfer medium temperature and the current battery average temperature; determining the heat transfer power of the heat transfer medium transferred to the battery to be predicted according to the equivalent heat transfer area of the heat transfer contact surface to the battery, the heat transfer medium heat transfer coefficient and the third temperature difference value; determining the temperature adjustment sub-parameter based on the heat transfer power, the battery mass and the initial total heat capacity of the battery; The temperature prediction parameters further include battery mass, initial total heat capacity of the battery, and equivalent heat transfer area of the heat transfer contact surface to the battery, current heat transfer medium temperature, and heat transfer medium heat transfer coefficient in the liquid cooling or liquid heating mode, and the temperature adjustment device includes a heat transfer medium, and the temperature adjustment is performed on the battery to be predicted by the heat transfer medium.

9. The battery internal temperature prediction method according to claim 8, wherein The determination of the predicted heat transfer medium temperature change rate according to the temperature prediction parameters includes: determining a fourth temperature difference value according to the current heat transfer medium temperature and the current external environment temperature; determining a pipeline environment heat loss of the pipeline to the environment according to the equivalent heat transfer area of the pipeline to the environment, the heat transfer medium heat transfer coefficient, and the fourth temperature difference value; adjusting the compressor input power according to the heat transfer efficiency parameter of the heat transfer medium to obtain an adjusted compressor input power; determining the predicted heat transfer medium temperature change rate according to the pipeline environment heat loss, the heat transfer power, the adjusted compressor input power, the heat transfer medium mass flow rate, and the heat transfer medium specific heat capacity; The temperature prediction parameters further include heat transfer efficiency parameter of the heat medium, compressor input power, current external environment temperature, equivalent heat transfer area of the pipeline to the environment in the liquid cooling or liquid heating mode, heat transfer medium mass flow rate, and heat transfer medium specific heat capacity.

10. The battery internal temperature prediction method of claim 7, wherein, If the temperature adjustment mode is air cooling or air heating, the determination of the temperature adjustment sub-parameters includes: determining a fifth temperature difference value according to the current heat transfer medium temperature and the current battery average temperature; determining a heat transfer power of the heat transfer medium to the battery to be predicted according to the equivalent heat transfer area of the heat transfer contact surface to the battery, the heat transfer medium heat transfer coefficient, and the fifth temperature difference value; determining the temperature adjustment sub-parameters based on the heat transfer power, battery mass, and initial total heat capacity of the battery; The temperature prediction parameters further include battery mass, initial total heat capacity of the battery, and equivalent heat transfer area of the heat transfer contact surface to the battery, current heat transfer medium temperature, and heat transfer medium heat transfer coefficient in the air cooling or air heating mode, and the temperature adjustment device includes a heat transfer medium, and the temperature adjustment is performed on the battery to be predicted by the heat transfer medium.

11. The battery internal temperature prediction method according to claim 10, wherein The determination of the heat transfer medium temperature change rate according to the temperature prediction parameters includes: determining a sixth temperature difference value according to the current heat transfer medium temperature and the current external environment temperature; determining a pipeline environment heat loss of the pipeline to the environment according to the equivalent heat transfer area of the pipeline to the environment, the heat transfer medium heat transfer coefficient, and the sixth temperature difference value; adjusting the compressor input power according to the heat transfer efficiency parameter of the heat transfer medium to obtain an adjusted compressor input power; determining the predicted heat transfer medium temperature change rate according to the pipeline environment heat loss, the heat transfer power, the adjusted compressor input power, the heat transfer medium mass flow rate, and the heat transfer medium specific heat capacity; The temperature prediction parameters further include heat transfer efficiency parameter of the heat medium, compressor input power, current external environment temperature, equivalent heat transfer area of the pipeline to the environment in the liquid cooling or liquid heating mode, heat transfer medium mass flow rate, and heat transfer medium specific heat capacity.

12. The battery internal temperature prediction method of claim 7, wherein, If the temperature adjustment mode is direct heating, the determination of the temperature adjustment sub-parameters includes: determining a seventh temperature difference value according to the saturation temperature of the gaseous heat transfer medium and the current battery average temperature; determining an eighth temperature difference value according to the current average battery temperature, the saturation temperature of the gaseous heat transfer medium, and the heat transfer medium temperature at the compressor outlet; determining a heat release power of the heat transfer medium to the battery to be predicted according to the heat transfer area of the gaseous region of the direct heating process heat transfer medium, the heat transfer area of the liquid region of the direct heating process heat transfer medium, the heat transfer coefficient of the gaseous-liquid mixed state of the direct heating process heat transfer medium, the heat transfer coefficient of the liquid state of the direct heating process heat transfer medium, the seventh temperature difference value, and the eighth temperature difference value; determining the temperature adjustment sub-parameter based on the heat release power, the battery mass, and the initial total heat capacity of the battery; wherein the temperature prediction parameter further comprises the battery mass, the initial total heat capacity of the battery, the saturation temperature of the gaseous heat transfer medium in the direct heating mode, the heat transfer medium temperature at the compressor outlet, the heat transfer area of the gaseous region of the direct heating process heat transfer medium, the heat transfer area of the liquid region of the direct heating process heat transfer medium, the heat transfer coefficient of the gaseous-liquid mixed state of the direct heating process heat transfer medium, and the heat transfer coefficient of the liquid state of the direct heating process heat transfer medium, and the temperature adjustment device comprises a heat transfer medium, and the temperature adjustment of the battery to be predicted is performed by the heat transfer medium.

13. The battery internal temperature prediction method of claim 12, wherein, determining the predicted heat transfer medium temperature change rate according to the temperature prediction parameter comprises: determining a ninth temperature difference value according to the heat transfer medium temperature at the compressor outlet and the current external environment temperature; determining an environmental heat loss of the direct heating process heat transfer medium pipeline to the environment according to the equivalent heat transfer area of the direct heating process heat transfer medium pipeline to the environment, the gaseous phase heat transfer coefficient of the direct heating process heat transfer medium, and the ninth temperature difference value; adjusting the compressor input power according to the mechanical efficiency of the compressor to obtain an adjusted compressor input power; determining the predicted heat transfer medium temperature change rate according to the environmental heat loss of the direct heating process heat transfer medium pipeline, the heat release power, the adjusted compressor input power, the total mass flow of the heat transfer medium, and the specific heat capacity of the heat transfer medium; wherein the temperature prediction parameter further comprises the heat transfer medium temperature at the compressor outlet in the direct heating mode, the current external environment temperature, the equivalent heat transfer area of the direct heating process heat transfer medium pipeline to the environment, the gaseous phase heat transfer coefficient of the direct heating process heat transfer medium, the mechanical efficiency of the compressor, the compressor input power, the total mass flow of the heat transfer medium, and the specific heat capacity of the heat transfer medium.

14. The battery internal temperature prediction method of claim 7, wherein, if the temperature adjustment mode is direct cooling, the determination of the temperature adjustment sub-parameter comprises: determining a tenth temperature difference value according to the temperature of the expanded heat transfer medium and the current average battery temperature; determining an eleventh temperature difference value according to the current average battery temperature and the saturation temperature of the liquid heat transfer medium; determining a heat absorption power of the expanded heat transfer medium and the battery to be predicted according to the heat transfer area of the gaseous region of the direct cooling process heat transfer medium, the heat transfer area of the gaseous-liquid mixed state region of the direct cooling process heat transfer medium, the heat transfer coefficient of the gaseous-liquid mixed state of the direct cooling process heat transfer medium, the gaseous phase heat transfer coefficient of the direct cooling process heat transfer medium, the tenth temperature difference value, and the eleventh temperature difference value; determining the temperature adjustment sub-parameter based on the heat absorption power, the battery mass, and the initial total heat capacity of the battery; The temperature prediction parameters further include battery mass, battery initial total heat capacity, heat exchange area of the gaseous region of the direct cooling process heat transfer medium in the direct cooling mode, heat exchange area of the gaseous-liquid mixed region of the direct cooling process heat transfer medium, heat exchange coefficient of the gaseous-liquid mixed state of the direct cooling process heat transfer medium, heat exchange coefficient of the gaseous phase of the direct cooling process heat transfer medium, temperature of the expanded heat transfer medium, and saturation temperature of the liquid heat transfer medium, and the temperature adjustment device includes a heat transfer medium, and the temperature adjustment device adjusts the temperature of the battery to be predicted through the heat transfer medium.

15. The battery internal temperature prediction method of claim 14, wherein, The determination of the predicted heat transfer medium temperature change rate according to the temperature prediction parameters includes: determining a fifteenth temperature difference value according to the heat transfer medium temperature at the compressor outlet and the current external environment temperature; determining a direct heating process heat transfer medium pipeline environment heat loss of the heat transfer medium pipeline to the environment according to the equivalent heat transfer area of the direct heating process heat transfer medium pipeline to the environment, the heat exchange coefficient of the gaseous phase of the direct heating process heat transfer medium, and the fifteenth temperature difference value; determining a twelfth temperature difference value according to the saturation temperature of the gaseous heat transfer medium and the current external environment temperature; determining a thirteenth temperature difference value according to the current external environment temperature, the saturation temperature of the gaseous heat transfer medium, and the heat transfer medium temperature at the compressor outlet; determining an environment heat release power of the heat transfer medium to the environment according to the heat exchange coefficient of the liquid state of the direct heating process heat transfer medium, the heat exchange area of the gaseous region of the direct heating process heat transfer medium, the heat exchange area of the liquid region of the direct heating process heat transfer medium, the heat exchange coefficient of the gaseous-liquid mixed state of the direct heating process heat transfer medium, the twelfth temperature difference value, and the thirteenth temperature difference value; determining a fourteenth temperature difference value according to the temperature of the condensed heat transfer medium and the current external environment temperature; determining a heat loss of the expansion valve pipeline to the environment according to the equivalent heat transfer coefficient of the expansion valve to the environment, the equivalent heat transfer area of the expansion to the environment, the equivalent heat transfer coefficient of the direct cooling process liquid heat transfer medium to the pipeline to the environment, the equivalent heat transfer area of the direct cooling process heat transfer medium pipeline to the environment, and the fourteenth temperature difference value; adjusting the compressor input power according to the mechanical efficiency of the compressor to obtain an adjusted compressor input power; determining the predicted heat transfer medium temperature change rate according to the direct heating process heat transfer medium pipeline environment heat loss, the environment heat release power, the heat loss of the expansion valve pipeline to the environment, the heat absorption power, the adjusted compressor input power, the total mass flow of the heat transfer medium, and the specific heat capacity of the heat transfer medium. The temperature prediction parameters further include the heat transfer medium temperature at the compressor outlet in the direct heating mode, the current external environment temperature, the equivalent heat transfer area of the direct heating process heat transfer medium pipeline to the environment, the gaseous heat transfer medium gas phase heat transfer coefficient, the saturation temperature of the gaseous heat transfer medium, the heat transfer area of the gaseous region of the direct heating process heat transfer medium, the heat transfer area of the liquid region of the direct heating process heat transfer medium, the gaseous-liquid mixed state heat transfer coefficient of the direct heating process heat transfer medium, the liquid state heat transfer coefficient of the direct heating process heat transfer medium, the temperature of the condensed heat transfer medium, the equivalent heat transfer coefficient of the expansion valve to the environment, the equivalent heat transfer area of the expansion to the environment, the equivalent heat transfer coefficient of the direct cooling process liquid heat transfer medium transmission pipeline to the environment, the equivalent heat transfer area of the direct cooling process heat transfer medium pipeline to the environment, the mechanical efficiency of the compressor, the input power of the compressor, the total mass flow of the heat transfer medium, and the specific heat capacity of the heat transfer medium.

16. The battery internal temperature prediction method of claim 4, wherein, If the use condition is discharging, and the battery internal temperature prediction method is applied to a vehicle, the method further comprises: If the driving state of the vehicle is an urban condition, determining the discharging condition battery current according to historical discharging current; If the driving state of the vehicle is a high-speed condition, determining the discharging condition battery current based on a preset driving condition current curve.

17. A battery internal temperature prediction device characterized by comprising: The device comprises: An acquisition module configured to acquire a temperature adjustment mode of a battery to be predicted, and acquire temperature prediction parameters corresponding to the temperature adjustment mode, wherein the battery to be predicted is subjected to temperature adjustment by a temperature adjustment device; A sub-parameter determination module configured to determine, according to the temperature prediction parameters, an environmental factor temperature change sub-parameter, a heat dissipation error sub-parameter, a battery self-heating sub-parameter, and a temperature adjustment sub-parameter, wherein the environmental factor temperature change sub-parameter represents a temperature change rate of the battery to be predicted caused by an environmental temperature, the heat dissipation error sub-parameter represents a temperature change rate of the battery to be predicted caused by a battery internal temperature difference, the battery self-heating sub-parameter represents a temperature change rate of the battery to be predicted caused by a battery self-heating effect, and the temperature adjustment sub-parameter represents a temperature change rate of the battery to be predicted caused by temperature adjustment of the battery to be predicted by the temperature adjustment device; A change rate prediction module configured to superimpose the environmental factor temperature change sub-parameter, the heat dissipation error sub-parameter, the battery self-heating sub-parameter, and the temperature adjustment sub-parameter to obtain a predicted battery internal temperature change rate; A temperature prediction module configured to determine a predicted battery internal temperature of the battery to be predicted based on the predicted battery internal temperature change rate and a current battery average temperature in the temperature prediction parameters.

18. A vehicle characterized by comprising: The vehicle comprises a battery to be predicted and a battery management system, and the battery management system comprises the battery internal temperature prediction device of claim 17, wherein the battery management system is configured to perform a power-on self-test when the vehicle is powered on, and trigger the battery internal temperature prediction device to perform prediction of the predicted battery internal temperature if the self-test is successful.

19. An electronic device, comprising: The memory has a computer program stored thereon; The processor is configured to execute the computer program in the memory to implement the steps of the method of any one of claims 1 to 16. ​ 20. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, which is executed by a processor, implements the steps of the method according to any one of claims 1 to 16.

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