Power battery temperature prediction method and device, vehicle, and storage medium

Abstract

The application relates to the technical field of battery thermal state estimation, in particular to a power battery temperature prediction method and device, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring current battery parameters of a power battery; obtaining a battery model of the power battery through simulation according to the current battery parameters, and identifying a relationship coefficient related to temperature in model parameters of the battery model; and predicting the temperature of the power battery at a predicted time according to the relationship coefficient and the current battery parameters. Therefore, the problems that the existing technology does not consider that the working conditions faced by the battery management system algorithm are more complex, and that part of thermal parameters cannot be simply calculated when the battery is integrated into a battery pack are solved.

Description

Technical Field

[0001] This invention relates to the field of battery thermal state estimation technology, specifically to a method, device, vehicle, and storage medium for predicting the temperature of power batteries. Background Technology

[0002] The power battery management system monitors and controls various aspects of the power battery's condition. Battery performance degrades and the risk of safety issues increases under high and low temperature conditions, making battery thermal management a crucial component of the battery management system.

[0003] A method for identifying the lumped thermal parameters of lithium-ion batteries has been proposed in related technologies. This method involves applying a specific sinusoidal alternating current to the battery in an experimental environment and estimating the lumped thermal parameters of the battery based on changes in battery temperature and ambient temperature using a search iterative algorithm. However, the operating conditions faced by algorithms applied to battery management systems are more complex and variable, and some thermal parameters cannot be easily calculated when batteries are integrated into a battery pack.

[0004] To enable parameter identification to be applied to battery management systems, some calibrable parameters need to be calibrated offline, and identification should only be performed on uncertain thermal parameters. However, the computing power of an onboard BMS (Battery Management System) is limited, making algorithm simplification essential. Summary of the Invention

[0005] One objective of this invention is to provide a method for predicting the temperature of a power battery, in order to solve the problem that the existing technology does not take into account the more complex operating conditions faced by the battery management system algorithm and that some thermal parameters cannot be easily calculated when the battery is integrated into a battery pack; a second objective is to provide a device for predicting the temperature of a power battery; a third objective is to provide a vehicle; and a fourth objective is to provide a computer-readable storage medium.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for predicting the temperature of a power battery includes the following steps: obtaining the current battery parameters of the power battery; performing simulation based on the current battery parameters to obtain a battery model of the power battery, and identifying temperature-related coefficients in the model parameters of the battery model; and predicting the temperature of the power battery at an estimated time based on the coefficients and the current battery parameters.

[0007] Based on the above technical means, the embodiments of this application can combine parameter identification and simulation estimation. The battery model of the power battery is obtained by simulation based on the battery parameters of the power battery. The relationship coefficient between the model parameters and temperature is identified. The temperature of the power battery at the estimated time is predicted based on the relationship coefficient and the current battery parameters. The identification results are used for state estimation in real time, which ensures the accuracy of the estimation results.

[0008] Furthermore, the step of predicting the temperature of the power battery at the estimated time based on the relationship coefficient and the current battery parameters includes: obtaining the charging strategy table of the power battery; predicting the internal resistance and current value of the power battery at the estimated time based on the charging strategy table; and calculating the temperature of the power battery at the estimated time based on the current battery parameters, the internal resistance, the current value, and the relationship coefficient.

[0009] Based on the above technical means, the embodiments of this application can predict the internal resistance and current values ​​of the battery at the estimated time according to the charging strategy table, and further calculate the temperature of the power battery at the estimated time based on the current battery parameters, internal resistance, current value and relationship coefficient.

[0010] Furthermore, identifying the temperature-related coefficients in the model parameters of the battery model includes: solving the battery model using the recursive least squares method to obtain the temperature-related coefficients.

[0011] Based on the above technical means, the embodiments of this application can use the recursive least squares method, which has a smaller computational load, to solve the battery model and obtain the temperature-related coefficients, thereby reducing the computational load of the model.

[0012] Furthermore, the battery model is as follows: , in, Here, C represents the temperature of a single battery cell, C represents the specific heat capacity of a single battery cell, and m represents the mass of a single battery cell. This represents the average voltage of a single battery cell. The OCV (Open circuit voltage) is calculated based on SOC (State of Charge, battery state of charge). This is the current current value of the battery. Let A be the inlet water temperature, and A be the relationship coefficient.

[0013] Furthermore, the current battery parameters include one or more of the following: cell temperature, cell voltage, state of charge (SOC), inlet temperature, and current.

[0014] Furthermore, the step of simulating the battery model of the power battery based on the current battery parameters includes: calculating the heat generated by the battery itself based on the cell voltage, SOC and the battery current; establishing a differential equation for battery temperature change based on the heat generated by the cell itself, the cell temperature and the battery inlet temperature; and simplifying the differential equation for battery temperature change based on the heat dissipation of the power battery to obtain the battery model of the power battery.

[0015] Based on the above technical means, the embodiments of this application can establish a differential equation for battery temperature change based on the heat generated by the battery cell itself, the temperature of the individual battery cell and the temperature of the battery inlet. In order to improve the convergence of parameter identification, the influence of ambient temperature is not considered in the identification formula, and the equation is simplified to obtain the battery model of the power battery.

[0016] Furthermore, the step of calculating the heat generated by the battery itself based on the cell voltage, SOC, and battery current includes: obtaining the relationship curve between the SOC and open-circuit voltage (OCV) of the power battery; determining the OCV corresponding to the SOC based on the relationship curve; calculating the average cell voltage of the power battery based on the cell voltage; calculating the potential of the power battery based on the average cell voltage and the OCV; and calculating the heat generated by the battery itself based on the potential of the power battery and the battery current.

[0017] Based on the above technical means, the embodiments of this application use the potential of the power battery and the battery current to calculate the heat generated by the battery itself, taking into account the inaccuracy of the battery's internal resistance, thereby improving the accuracy of heat calculation.

[0018] A power battery temperature prediction device includes: an acquisition module for acquiring current battery parameters of the power battery; an identification module for simulating the power battery based on the current battery parameters to obtain a battery model of the power battery, and identifying temperature-related coefficients in the model parameters of the battery model; and a prediction module for predicting the temperature of the power battery at an estimated time based on the coefficients and the current battery parameters.

[0019] Furthermore, the prediction module is further configured to: obtain the charging strategy table of the power battery; predict the internal resistance and current value of the power battery at the estimated time based on the charging strategy table; and calculate the temperature of the power battery at the estimated time based on the current battery parameters, the internal resistance value, the current value, and the relationship coefficient.

[0020] Furthermore, the identification module is further used to: solve the battery model using the recursive least squares method to obtain the temperature-related coefficients.

[0021] Furthermore, the battery model is as follows: , in, Here, C represents the temperature of a single battery cell, C represents the specific heat capacity of a single battery cell, and m represents the mass of a single battery cell. This represents the average voltage of a single battery cell. The OCV is calculated based on SOC. This is the current current value of the battery. Let A be the inlet water temperature, and A be the relationship coefficient.

[0022] Furthermore, the current battery parameters include one or more of the following: cell temperature, cell voltage, state of charge (SOC), inlet temperature, and current.

[0023] Furthermore, the identification module is further used to: calculate the heat generated by the battery itself based on the cell voltage, SOC and the battery current; establish a differential equation for battery temperature change based on the heat generated by the cell itself, the cell temperature and the battery inlet temperature, and simplify the differential equation for battery temperature change based on the heat dissipation of the power battery to obtain the battery model of the power battery.

[0024] Furthermore, the step of calculating the heat generated by the battery itself based on the cell voltage, SOC, and battery current includes: obtaining the relationship curve between the SOC and open-circuit voltage (OCV) of the power battery; determining the OCV corresponding to the SOC based on the relationship curve; calculating the average cell voltage of the power battery based on the cell voltage; calculating the potential of the power battery based on the average cell voltage and the OCV; and calculating the heat generated by the battery itself based on the potential of the power battery and the battery current.

[0025] A vehicle includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the power battery temperature prediction method as described in the above embodiments.

[0026] A computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the power battery temperature prediction method as described in the above embodiments.

[0027] The beneficial effects of this invention are: (1) The embodiments of this application can combine parameter identification and simulation estimation. The battery model of the power battery is obtained by simulation based on the battery parameters of the power battery. The relationship coefficient between the model parameters and temperature is identified. The temperature of the power battery at the estimated time is predicted based on the relationship coefficient and the current battery parameters. The identification results are used for state estimation in real time, which ensures the accuracy of the estimation results.

[0028] (2) According to the embodiments of this application, the internal resistance and current values ​​of the battery at the estimated time can be predicted based on the charging strategy table, and the temperature of the power battery at the estimated time can be calculated further based on the current battery parameters, internal resistance, current value and relationship coefficient.

[0029] (3) The embodiments of this application can use the recursive least squares method with less computation to solve the battery model and obtain the temperature-related coefficients, thereby reducing the computational load of the model.

[0030] (4) In this embodiment, the battery temperature change differential equation can be established based on the heat generated by the cell itself, the temperature of the cell and the temperature of the battery inlet. In order to improve the convergence of parameter identification, the influence of ambient temperature is not considered in the identification formula. The equation is simplified to obtain the battery model of the power battery.

[0031] (5) The embodiments of this application use the potential of the power battery and the battery current to calculate the heat generated by the battery itself, taking into account the problem that the internal resistance of the battery may be inaccurate.

[0032] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0033] Figure 1 A flowchart of a power battery temperature prediction method provided in an embodiment of this application; Figure 2 A flowchart illustrating a power battery temperature prediction method provided in one embodiment of this application; Figure 3 A schematic diagram illustrating the changes in model coefficients corresponding to the lowest temperature provided in this application embodiment; Figure 4 A schematic diagram illustrating the changes in model coefficients corresponding to the highest temperature provided in this application embodiment; Figure 5 A schematic diagram illustrating the estimation effect of the minimum temperature provided in the embodiments of this application; Figure 6 A schematic diagram illustrating the estimation effect of the highest temperature provided in the embodiments of this application; Figure 7 A schematic diagram of the power battery temperature prediction device provided in the embodiments of this application; Figure 8 This is a schematic diagram of the vehicle structure provided in an embodiment of this application. Detailed Implementation

[0034] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0035] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0036] Specifically, Figure 1 This is a flowchart illustrating a method for predicting the temperature of a power battery, as provided in an embodiment of this application.

[0037] It should be noted that the following embodiments use a power battery with a water-cooled and water-heated system as an example.

[0038] like Figure 1 As shown, the method for predicting the temperature of a power battery includes the following steps: In step S101, the current battery parameters of the power battery are obtained.

[0039] The current battery parameters include one or more of the following: cell temperature, cell voltage, state of charge (SOC), inlet temperature, and current.

[0040] It should be noted that the current battery parameters of the power battery need to be selected from a certain range of data. The selected data range should keep the battery thermal management status "heating" or "cooling" unchanged, and be used for subsequent processing steps.

[0041] In step S102, a battery model of the power battery is obtained by simulation based on the current battery parameters, and the temperature-related coefficients in the model parameters of the battery model are identified.

[0042] It is understood that the embodiments of this application can simulate the power battery model based on the current battery parameters, and identify the temperature-related coefficients in the model parameters so as to predict the power battery temperature in the future.

[0043] It should be noted that the simulated battery model of the power battery models the changes in the battery's highest and lowest temperatures during the charging process. Furthermore, the battery model equates the battery pack to two individual cells, representing the cells at the highest and lowest temperatures, respectively. In addition, the cell parameters of the battery model can be obtained through offline calibration methods using HPPC and OCV experiments.

[0044] In this embodiment of the application, the battery model of the power battery is obtained by simulation based on the current battery parameters, including: calculating the heat generated by the battery itself based on the cell voltage, SOC and battery current; establishing the differential equation of battery temperature change based on the heat generated by the cell itself, the cell temperature and the battery inlet temperature, and simplifying the differential equation of battery temperature change based on the heat dissipation of the power battery to obtain the battery model of the power battery.

[0045] Specifically, the process of calculating the battery model of the power battery in this embodiment of the application is as follows: The battery pack is equivalent to two individual cells at the highest and lowest temperatures. The external environment of the cells is simplified into two parts: the heating / cooling water circuit and the external environment. Based on the formula for thermal conductivity... In the formula, T represents temperature, and x represents the direction of heat flow. Let x be the thermal conductivity coefficient in the x-direction. Let x be the heat flux density in the x-direction. Let x be the temperature gradient in the x-direction. The differential equation for the battery temperature change can be obtained as follows: , In the formula, T cell The cell temperature is represented by 'c', the specific heat capacity of the cell is represented by 'm', the mass of the cell is represented by 'Q', and the heat generated by the battery itself is represented by 'k'. x1 T represents the equivalent thermal conductivity between the battery and the hydrothermal / water-cooled circuit. w T represents the inlet water temperature. e The external ambient temperature of the battery is represented by l1, the material thickness between the battery and the hydrothermal / water-cooling circuit is represented by S1, and the contact area between the battery and the hydrothermal / water-cooling circuit is represented by k. x2 L1 represents the equivalent thermal conductivity between the battery and the external environment, L2 represents the material thickness between the battery and the external environment, and S2 represents the contact area between the battery and the external environment.

[0046] Actual measurements show that the heat generated by the battery itself and the heat exchanged through the water-thermal circuit are far greater than the heat dissipated between the battery and the external environment. To ensure convergence of parameter identification, the influence of ambient temperature is not considered in the identification formula, and A = Then the differential equation for battery temperature change can be simplified to the following form: .

[0047] In this embodiment of the application, the calculation of the heat generated by the battery itself based on the cell voltage, SOC, and battery current includes: obtaining the relationship curve between the SOC and the open circuit voltage OCV of the power battery; determining the OCV corresponding to the SOC based on the relationship curve; calculating the average cell voltage of the power battery based on the cell voltage; calculating the potential of the power battery based on the average cell voltage and OCV; and calculating the heat generated by the battery itself based on the potential of the power battery and the battery current.

[0048] It is understood that, in the embodiments of this application, the OCV can be calculated based on the relationship curve between the SOC and open-circuit voltage (OCV) of the power battery, the average single-cell voltage of the power battery can be calculated based on the single-cell voltage, the potential of the power battery can be calculated based on the average single-cell voltage and OCV, and finally the heat generated by the battery itself can be calculated based on the potential of the power battery and the battery current. The calculation formula is as follows: , in, This represents the average voltage of a single battery cell. The OCV is calculated based on SOC. This represents the current current collected by the battery.

[0049] It should be noted that the battery itself generates heat. This only considers the heat generated by the battery current across its internal resistance. However, considering the potential inaccuracy of the battery's internal resistance, the parameter identification uses the method of multiplying the overpotential by the current. .

[0050] In this embodiment of the application, identifying the temperature-related coefficients in the model parameters of the battery model includes: solving the battery model using the recursive least squares method to obtain the temperature-related coefficients.

[0051] The battery model is as follows: , in, Here, C represents the temperature of a single battery cell, C represents the specific heat capacity of a single battery cell, and m represents the mass of a single battery cell. This represents the average voltage of a single battery cell. The OCV is calculated based on SOC. This is the current current value of the battery. Let A be the inlet water temperature, and A be the relationship coefficient.

[0052] The observed values ​​are: The parameter is A, and the coefficient is ( The parameter A can be solved using the recursive least squares method.

[0053] It is understood that the embodiments of this application can use the recursive least squares method to solve the battery model and obtain the temperature-related coefficients. In step S103, the temperature of the power battery at the estimated time is predicted based on the relationship coefficient and the current battery parameters.

[0054] Furthermore, the temperature of the power battery at the estimated time is predicted based on the relationship coefficient and the current battery parameters, including: obtaining the charging strategy table of the power battery; predicting the internal resistance and current value of the power battery at the estimated time based on the charging strategy table; and calculating the temperature of the power battery at the estimated time based on the current battery parameters, internal resistance, current value, and relationship coefficient.

[0055] It is understood that, according to the embodiments of this application, the internal resistance and current values ​​of the power battery at the estimated time can be predicted based on the charging strategy table, and the temperature of the power battery at the estimated time can be calculated based on the current battery parameters, internal resistance, current value and relationship coefficient.

[0056] It should be noted that the current value within the estimated time period can also be predicted based on the current operating condition of the battery. In addition, the time period for predicting the future temperature is the same as the time period under the same thermal management state.

[0057] The specific formula for calculating the temperature of the power battery at the estimated time is as follows: , in, This represents the battery's internal resistance at the estimated time. The estimated current value at the predicted time is estimated by looking up a table based on the battery charging strategy.

[0058] The following will combine Figure 2 The method for predicting the temperature of a power battery according to embodiments of this application is further described, including: Step 1: Offline calibration of battery-related parameters: cell internal resistance, cell specific heat capacity, and cell SOC-OCV curve.

[0059] Step 2: Obtain battery status parameters: highest temperature, lowest temperature, current, highest single-cell voltage, lowest single-cell voltage, and battery state of charge.

[0060] Step 3: Calculate the average voltage, and obtain the OCV from the table based on the SOC; calculate the overpotential by subtracting the OCV from the average cell voltage; multiply the absolute value of the overpotential by the current current to obtain the current battery heat generation power.

[0061] Step 4: Calculate the highest and lowest temperatures separately, and use an online identification algorithm to calculate the relationship coefficient between the temperature difference and temperature change.

[0062] Step 5: Using the current water temperature or thermal management target water temperature and charging map, predict the highest and lowest temperature states respectively.

[0063] In practical applications, a series of low-temperature or high-temperature charging experiments can be conducted on the power battery first, with water heating or cooling cycles activated. Parameter identification and simulation are then performed using data collected by the BMS. The temperature accuracy of the BMS can be set to 1℃, so the actual temperature in the data varies in steps. For example, when identifying parameters online during a high-temperature cooling process, the change in model coefficients corresponding to the lowest temperature is shown below. Figure 3 As shown, the changes in model coefficients corresponding to the highest temperature are as follows: Figure 4 As shown; for example, when predicting temperature during low-temperature heating, the estimation effect of the minimum temperature during low-temperature heating is as follows: Figure 5 As shown, the estimation effect of the maximum temperature during the low-temperature heating process is as follows: Figure 6 As shown. Among them, Figure 5 and Figure 6 The middle circle represents the estimated time point. The online identification uses data before the circle for identification. After 1 / 4 of the charging process has passed, a good estimate of the temperature change during the entire charging process can be obtained.

[0064] The power battery temperature prediction method proposed in this application combines parameter identification and simulation estimation. It obtains a battery model of the power battery through simulation based on the battery parameters, identifies the temperature-related coefficients of the model parameters, and predicts the temperature of the power battery at the estimated time based on these coefficients and the current battery parameters. The identification results are used in real-time for state estimation, ensuring the accuracy of the estimation results. Furthermore, it uses a recursive least squares method with relatively low computational cost to solve the battery model and obtain the temperature-related coefficients, reducing the computational load of the model and enabling the algorithm to be used in an in-vehicle BMS.

[0065] Next, the power battery temperature prediction device according to the embodiments of this application is described with reference to the accompanying drawings.

[0066] Figure 7 This is a block diagram of a power battery temperature prediction device according to an embodiment of this application.

[0067] like Figure 7 As shown, the power battery temperature prediction device 10 includes: an acquisition module 100, an identification module 200, and a prediction module 300.

[0068] The acquisition module 100 is used to acquire the current battery parameters of the power battery; the identification module 200 is used to simulate the power battery model based on the current battery parameters to identify the temperature-related relationship coefficients in the model parameters of the battery model; and the prediction module 300 is used to predict the temperature of the power battery at the estimated time based on the relationship coefficients and the current battery parameters.

[0069] In this embodiment, the prediction module 300 is further configured to: obtain a charging strategy table for the power battery; predict the internal resistance and current values ​​of the power battery at an estimated time based on the charging strategy table; and calculate the temperature of the power battery at the estimated time based on the current battery parameters, internal resistance, current values, and relationship coefficients.

[0070] In this embodiment of the application, the identification module 200 is further used to: solve the battery model using the recursive least squares method to obtain the temperature-related coefficients.

[0071] In this embodiment of the application, the battery model is as follows: , in, Here, C represents the temperature of a single battery cell, C represents the specific heat capacity of a single battery cell, and m represents the mass of a single battery cell. This represents the average voltage of a single battery cell. The OCV is calculated based on SOC. This is the current current value of the battery. Let A be the inlet water temperature, and A be the relationship coefficient.

[0072] In this embodiment of the application, the current battery parameters include one or more of the following: cell temperature, cell voltage, state of charge (SOC), inlet temperature, and current.

[0073] In this embodiment, the identification module 200 is further configured to: calculate the heat generated by the battery itself based on the cell voltage, SOC and battery current; establish a differential equation for battery temperature change based on the heat generated by the cell itself, the cell temperature and the battery inlet temperature, and simplify the differential equation for battery temperature change based on the heat dissipation of the power battery to obtain the battery model of the power battery.

[0074] In this embodiment of the application, the calculation of the heat generated by the battery itself based on the cell voltage, SOC, and battery current includes: obtaining the relationship curve between the SOC and the open circuit voltage OCV of the power battery; determining the OCV corresponding to the SOC based on the relationship curve; calculating the average cell voltage of the power battery based on the cell voltage; calculating the potential of the power battery based on the average cell voltage and OCV; and calculating the heat generated by the battery itself based on the potential of the power battery and the battery current.

[0075] It should be noted that the foregoing explanation of the embodiment of the power battery temperature prediction method also applies to the power battery temperature prediction device of this embodiment, and will not be repeated here.

[0076] The power battery temperature prediction device proposed in this application combines parameter identification and simulation estimation. It obtains a battery model of the power battery by simulating the battery parameters, identifies the relationship coefficients between the model parameters and temperature, and predicts the temperature of the power battery at the estimated time based on the relationship coefficients and the current battery parameters. The identification results are used for state estimation in real time, which ensures the accuracy of the estimation results. Furthermore, the recursive least squares method with low computational cost is used to solve the battery model to obtain the temperature-related relationship coefficients, which reduces the computational cost of the model and allows the algorithm to be used in an in-vehicle BMS.

[0077] Figure 8 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0078] When the processor 802 executes the program, it implements the power battery temperature prediction method provided in the above embodiments.

[0079] Furthermore, the vehicle also includes: Communication interface 803 is used for communication between memory 801 and processor 802.

[0080] The memory 801 is used to store computer programs that can run on the processor 802.

[0081] The memory 801 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0082] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0083] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0084] The processor 802 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0085] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described power battery temperature prediction method.

[0086] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0087] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0088] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0089] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0090] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0091] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting the temperature of a power battery, characterized in that, Includes the following steps: Obtain the current battery parameters of the power battery; A battery model of the power battery is obtained by simulation based on the current battery parameters, and the temperature-related coefficients in the model parameters of the battery model are identified. The battery model is as follows: ， in, Here, C represents the temperature of a single battery cell, C represents the specific heat capacity of a single battery cell, and m represents the mass of a single battery cell. This represents the average voltage of a single battery cell. The OCV is calculated based on SOC. This is the current current value of the battery. Where A is the inlet water temperature, and A is the relationship coefficient. Predicting the temperature of the power battery at an estimated time based on the relationship coefficient and the current battery parameters includes: Obtain the charging strategy table of the power battery; The internal resistance and current of the power battery at the estimated time are predicted based on the charging strategy table. The temperature of the power battery at the estimated time is calculated based on the current battery parameters, the internal resistance value, the current value, and the relationship coefficient. The expression for calculating the temperature of the power battery at the estimated time is as follows: ， in, This represents the battery's internal resistance at the estimated time. This is the estimated current value at the predicted time.

2. The method for predicting the temperature of a power battery according to claim 1, characterized in that, The identification of temperature-related coefficients in the model parameters of the battery model includes: The battery model was solved using the recursive least squares method to obtain the temperature-related coefficients.

3. The method for predicting the temperature of a power battery according to claim 1, characterized in that, The current battery parameters include one or more of the following: cell temperature, cell voltage, state of charge (SOC), inlet temperature, and current.

4. The method for predicting the temperature of a power battery according to claim 3, characterized in that, The process of simulating the power battery model based on the current battery parameters includes: The heat generated by the battery itself is calculated based on the individual cell voltage, SOC, and battery current. A differential equation for battery temperature change is established based on the heat generated by the battery cell itself, the temperature of the individual battery cell, and the temperature of the battery inlet. The battery model of the power battery is obtained by simplifying the differential equation for battery temperature change based on the heat dissipation of the power battery.

5. The method for predicting the temperature of a power battery according to claim 4, characterized in that, The calculation of the heat generated by the battery itself based on the cell voltage, SOC, and battery current includes: Obtain the relationship curve between the SOC and open-circuit voltage OCV of the power battery; The SOC is determined according to the relationship curve, the average cell voltage of the power battery is calculated according to the cell voltage, and the potential of the power battery is calculated according to the average cell voltage and the OCV. The heat generated by the battery itself is calculated based on the potential of the power battery and the battery current.

6. A power battery temperature prediction device, characterized in that, include: The acquisition module is used to acquire the current battery parameters of the power battery; The identification module is used to simulate the power battery model based on the current battery parameters to obtain the battery model, and to identify the temperature-related coefficients in the model parameters of the battery model. The battery model is as follows: ， in, Here, C represents the temperature of a single battery cell, C represents the specific heat capacity of a single battery cell, and m represents the mass of a single battery cell. This represents the average voltage of a single battery cell. The OCV is calculated based on SOC. This is the current current value of the battery. Where A is the inlet water temperature, and A is the relationship coefficient. The prediction module is used to predict the temperature of the power battery at an estimated time based on the relationship coefficient and the current battery parameters, including: Obtain the charging strategy table of the power battery; The internal resistance and current of the power battery at the estimated time are predicted based on the charging strategy table. The temperature of the power battery at the estimated time is calculated based on the current battery parameters, the internal resistance value, the current value, and the relationship coefficient. The expression for calculating the temperature of the power battery at the estimated time is as follows: ， in, This represents the battery's internal resistance at the estimated time. This is the estimated current value at the predicted time.

7. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the power battery temperature prediction method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the power battery temperature prediction method as described in any one of claims 1-5.

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