Cell internal resistance correction coefficient determination method, generation method, prediction method, device, equipment and medium

Abstract

The invention relates to a cell internal resistance correction coefficient determination method, generation method, prediction method, device, equipment and medium, and the determination method comprises the steps: simulating a discharging or charging process of a battery system under a specified working condition through an initial three-dimensional thermal simulation model, and obtaining simulation temperature rise data of a specified temperature sensing point; acquiring actually measured temperature rise data of a specified temperature sensing point; determining initial cell internal resistance correction data according to the actually measured temperature rise data and the simulated temperature rise data; the target three-dimensional thermal simulation model is used for simulating the discharging or charging process of the battery system under the specified working condition according to the initial battery cell internal resistance correction data, and the battery cell temperature of the whole specified battery cell changing along with time is obtained; and determining target battery cell internal resistance correction data according to the battery cell temperature of the whole specified battery cell changing with time and the initial battery cell internal resistance correction data. Therefore, the cell internal resistance correction coefficient of the battery system changing along with the cell temperature under the specified working condition can be determined, and the prediction accuracy of the temperature rise of the battery system is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of batteries, and in particular to a method for determining, generating, and predicting a correction coefficient of an internal resistance of a battery cell, an apparatus, a device, and a medium. Background Art

[0002] Battery thermal management design is a critical component of battery system development. Excessive temperature rise during charging and discharging can severely impact battery performance, lifespan, and safety. Three-dimensional thermal simulation is a common technique used during battery system development to predict and verify temperature rise.

[0003] In most existing three-dimensional thermal simulation processes, the heat generation of the battery cell is generally taken into account. The heat generation of the battery cell is simulated by assigning the battery cell heat generation power. The battery cell heat generation power is generally calculated based on the battery cell charge and discharge current and the battery cell internal resistance. The existing three-dimensional thermal simulation process usually uses a fixed battery cell internal resistance to calculate the battery cell heat generation power. However, because the battery cell internal resistance is related to many factors such as the rate, temperature, and State of Charge (SOC), there will be a certain deviation between the simulation results of the heat generation power calculated using a fixed battery cell internal resistance and the actual test results, which reduces the accuracy of the battery temperature rise prediction. Summary of the Invention

[0004] In view of this, the present disclosure proposes a method for determining, generating, predicting, apparatus, equipment and medium for a battery cell internal resistance correction coefficient, which can determine the battery cell internal resistance correction coefficient that changes with the battery cell temperature under specified operating conditions. The battery cell internal resistance correction coefficient that changes with the battery cell temperature can then be used to correct the battery cell internal resistance in the battery cell heat generation power, thereby improving the prediction accuracy of the battery system temperature rise.

[0005] According to one aspect of the present disclosure, a method for determining an internal resistance correction coefficient of a battery cell in a battery system is provided, the method comprising: using an initial three-dimensional thermal simulation model of the battery system to simulate a process of discharging or charging the battery system under specified operating conditions, and obtaining simulated temperature rise data of a specified temperature-sensing point on a specified battery cell in the battery system; wherein the simulated temperature rise data includes a simulated temperature of the specified temperature-sensing point changing with time; wherein the battery cell simulation module in the initial three-dimensional thermal simulation model is endowed with original battery cell heat generation power; obtaining actual temperature rise data of a specified temperature-sensing point on the specified battery cell in the battery system, wherein the actual temperature rise data includes an actual measured temperature of the specified temperature-sensing point changing with time during the process of discharging or charging the battery system under the specified operating conditions; according to The measured temperature rise data and the simulated temperature rise data are used to determine initial battery cell internal resistance correction data, and the initial battery cell internal resistance correction data includes a battery cell internal resistance correction coefficient that changes with time; the target three-dimensional thermal simulation model of the battery system is used to simulate the process of discharging or charging the battery system under the specified operating conditions according to the initial battery cell internal resistance correction data, so as to obtain the battery cell temperature of the specified battery cell that changes with time; wherein the battery cell simulation module in the target three-dimensional thermal simulation model is endowed with a target battery cell heat generation power corrected by the battery cell internal resistance correction coefficient; the target battery cell internal resistance correction data is determined according to the battery cell temperature of the specified battery cell that changes with time and the initial battery cell internal resistance correction data, and the target battery cell internal resistance correction data includes the battery cell internal resistance correction coefficient that changes with the battery cell temperature.

[0006] In one possible implementation, the initial battery cell internal resistance correction data is determined based on the measured temperature rise data and the simulated temperature rise data, including: determining the target simulated temperature difference corresponding to the designated temperature sensing point based on the simulated temperature at the nth moment in the simulated temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data; determining the measured temperature difference corresponding to the designated temperature sensing point based on the measured temperature at the nth moment in the measured temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data; and determining the ratio between the target simulated temperature difference and the measured temperature difference as the battery cell internal resistance correction coefficient from the nth moment to the n+1th moment.

[0007] In one possible implementation, the specified operating conditions include: a specified initial state of charge and a specified rate; the original battery cell heat generation power is determined based on the battery cell capacity of the battery cell in the battery system, the original internal resistance of the battery cell and the specified rate; the target battery cell heat generation power is determined based on the battery cell capacity of the battery cell in the battery system, the original internal resistance of the battery cell, the specified rate and the battery cell internal resistance correction coefficient.

[0008] In one possible implementation, the battery system includes multiple battery cells, and the designated battery cell includes the battery cell located in the middle position among the multiple battery cells; each battery cell in the battery system is connected to a tab, and the tab simulation module in the initial three-dimensional thermal simulation model is also assigned an original tab heat generation power, and the original tab heat generation power is a specified proportion of the corresponding original battery cell heat generation power; the tab simulation module in the target three-dimensional thermal simulation model is also assigned a target tab heat generation power, and the target tab heat generation power is a specified proportion of the corresponding target battery cell heat generation power.

[0009] According to another aspect of the present disclosure, a method for predicting the temperature rise of a battery system is provided, comprising: using the method for determining a battery cell internal resistance correction coefficient to determine target battery cell internal resistance correction data for the battery system under specified operating conditions; and using a target three-dimensional thermal simulation model of the battery system to simulate a discharge or charge process of the battery system under the specified operating conditions based on the target battery cell internal resistance correction data, thereby obtaining a temperature rise prediction result for the battery system.

[0010] According to another aspect of the present disclosure, a method for generating a prediction model for a battery cell internal resistance correction coefficient is provided, comprising: obtaining a plurality of specified operating conditions of a battery system, wherein at least one of the specified rates and the specified initial states of charge in the different specified operating conditions is different; using the method for determining the battery cell internal resistance correction coefficient, determining target battery cell internal resistance correction data for each of the plurality of specified operating conditions; generating a battery cell internal resistance correction coefficient prediction model corresponding to the battery system based on the target battery cell internal resistance correction data for each of the plurality of specified operating conditions, wherein the battery cell internal resistance correction coefficient prediction model is a model with the rate, the initial state of charge and the battery cell temperature as independent variables, and with the battery cell internal resistance correction coefficient as the dependent variable.

[0011] According to another aspect of the present disclosure, a method for predicting the temperature rise of a battery system is provided, comprising: utilizing the method for generating a battery cell internal resistance correction coefficient prediction model to generate a battery cell internal resistance correction coefficient prediction model corresponding to the battery system; utilizing a target three-dimensional thermal simulation model of the battery system to simulate the discharge or charging process of the battery system under specified operating conditions based on the battery cell internal resistance correction coefficient prediction model, thereby obtaining a temperature rise prediction result of the battery system.

[0012] According to another aspect of the present disclosure, a device for determining an internal resistance correction coefficient of a battery cell in a battery system is provided, the device comprising: a first simulation module for simulating a process of discharging or charging the battery system under specified operating conditions using an initial three-dimensional thermal simulation model of the battery system, and obtaining simulated temperature rise data of a specified temperature-sensing point on a specified battery cell in the battery system; wherein the simulated temperature rise data includes a simulated temperature of the specified temperature-sensing point changing with time; wherein the battery cell simulation module in the initial three-dimensional thermal simulation model is endowed with the original battery cell heat generation power; an acquisition module for obtaining actual temperature rise data of a specified temperature-sensing point on the specified battery cell in the battery system, wherein the actual temperature rise data includes an actual measured temperature of the specified temperature-sensing point changing with time during the process of discharging or charging the battery system under the specified operating conditions; a first determination module , used to determine the initial battery cell internal resistance correction data according to the measured temperature rise data and the simulated temperature rise data, the initial battery cell internal resistance correction data including the battery cell internal resistance correction coefficient that changes with time; a second simulation module, used to use the target three-dimensional thermal simulation model of the battery system to simulate the process of discharging or charging of the battery system under the specified working conditions according to the initial battery cell internal resistance correction data, and obtain the battery cell temperature of the specified battery cell that changes with time; wherein the battery cell simulation module in the target three-dimensional thermal simulation model is assigned a target battery cell heat generation power corrected by the battery cell internal resistance correction coefficient; a second determination module, based on the battery cell temperature of the specified battery cell that changes with time and the initial battery cell internal resistance correction data, determines the target battery cell internal resistance correction data, the target battery cell internal resistance correction data including the battery cell internal resistance correction coefficient that changes with the battery cell temperature.

[0013] According to another aspect of the present disclosure, a battery system temperature rise prediction device is provided, comprising: a target determination module for determining target battery cell internal resistance correction data of the battery system under specified operating conditions using the battery cell internal resistance correction coefficient determination device; and a prediction module for simulating a discharge or charge process of the battery system under the specified operating conditions based on the target battery cell internal resistance correction data using a target three-dimensional thermal simulation model of the battery system, thereby obtaining a temperature rise prediction result of the battery system.

[0014] According to another aspect of the present disclosure, a device for generating a prediction model for a battery cell internal resistance correction coefficient is provided, comprising: an operating condition acquisition module for acquiring a plurality of specified operating conditions of a battery system, wherein at least one of the specified rates and the specified initial states of charge in different specified operating conditions is different; a data determination module for determining target battery cell internal resistance correction data under each of the plurality of specified operating conditions using the battery cell internal resistance correction coefficient determination device; and a generation module for generating a battery cell internal resistance correction coefficient prediction model corresponding to the battery system based on the target battery cell internal resistance correction data under each of the plurality of specified operating conditions, wherein the battery cell internal resistance correction coefficient prediction model is a model with the rate, the initial state of charge and the battery cell temperature as independent variables and the battery cell internal resistance correction coefficient as a dependent variable.

[0015] According to another aspect of the present disclosure, a battery system temperature rise prediction device is provided, comprising: a model generation module for generating a battery cell internal resistance correction coefficient prediction model corresponding to a battery system using the battery cell internal resistance correction coefficient prediction model generation device; and a temperature rise prediction module for simulating a discharge or charge process of the battery system under specified operating conditions based on the battery cell internal resistance correction coefficient prediction model using a target three-dimensional thermal simulation model of the battery system to obtain a temperature rise prediction result of the battery system.

[0016] According to another aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0017] According to another aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0018] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0019] According to various aspects of the present disclosure, by using the initial three-dimensional thermal simulation model of the battery system to simulate the charging or discharging process under specified working conditions, the simulated temperature rise data of the specified temperature sensing point of the specified battery cell is obtained, and the measured temperature rise data of the specified temperature sensing point of the specified battery cell is obtained through actual measurement, the battery cell internal resistance correction coefficient that changes with time is determined, and then the target three-dimensional thermal simulation model is used to simulate the battery cell temperature of the entire specified battery cell that changes with time based on the battery cell internal resistance correction coefficient that changes with time, so that the battery cell internal resistance correction coefficient that changes with the battery cell temperature under specified working conditions can be obtained. The battery cell internal resistance correction coefficient that changes with the average battery cell temperature can be used to more accurately correct the target battery cell heat generation power assigned by each battery cell simulation module in the target three-dimensional thermal simulation model, so that the simulation results are consistent with the measured results, and then the battery cell internal resistance correction coefficient that changes with the battery cell temperature and the target three-dimensional thermal simulation model can be used to predict the temperature rise of the battery system during the charging and discharging process more accurately.

[0020] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0022] Figure 1 A flow chart of a method for determining a cell internal resistance correction coefficient in a battery system according to an embodiment of the present disclosure is shown.

[0023] Figure 2 An exploded view of a three-dimensional thermal simulation model of a battery system according to an embodiment of the present disclosure is shown.

[0024] Figure 3 An internal module view of a three-dimensional thermal simulation model of a battery system according to an embodiment of the present disclosure is shown.

[0025] Figure 4 A schematic diagram of a battery cell simulation module for a single battery cell in a three-dimensional thermal simulation model of a battery system according to an embodiment of the present disclosure is shown.

[0026] Figure 5 A schematic diagram showing the arrangement positions of two temperature sensing points according to an embodiment of the present disclosure.

[0027] Figure 6 A schematic diagram of a curve showing how the measured temperatures of the temperature sensing points NTC1 and NTC2 vary with time according to an embodiment of the present disclosure is shown.

[0028] Figure 7A comparison diagram of a simulated temperature change curve and a measured temperature change curve at a specified temperature sensing point NTC1 according to an embodiment of the present disclosure is shown.

[0029] Figure 8 A comparison diagram of a simulated temperature change curve of the temperature sensing point NTC1 obtained based on the initial cell internal resistance correction data according to an embodiment of the present disclosure and a measured temperature change curve is shown.

[0030] Figure 9 A comparison diagram of a simulated temperature change curve of the temperature sensing point NTC2 obtained based on the initial cell internal resistance correction data according to an embodiment of the present disclosure and a measured temperature change curve is shown.

[0031] Figure 10 A schematic diagram showing a curve of a correction coefficient of internal resistance of a battery cell that varies with the temperature of the battery cell according to an embodiment of the present disclosure is shown.

[0032] Figure 11 A comparison diagram shows a measured temperature change curve of NTC2 according to an embodiment of the present disclosure, a simulated temperature change curve of NTC2 obtained by performing a three-dimensional thermal simulation using the initial battery cell internal resistance correction data, and a simulated temperature change curve of NTC2 obtained by performing a three-dimensional thermal simulation using the target battery cell internal resistance correction data.

[0033] Figure 12 A comparison diagram is shown of the measured temperature change curve of NTC1 according to an embodiment of the present disclosure, the simulated temperature change curve of NTC2 obtained by performing a three-dimensional thermal simulation using the initial battery cell internal resistance correction data, and the simulated temperature change curve of NTC2 obtained by performing a three-dimensional thermal simulation using the target battery cell internal resistance correction data.

[0034] Figure 13 A schematic diagram showing a curve of a change in the internal resistance correction coefficient of a battery cell as the battery cell temperature changes at 1C, 2C, 3C, and 4C discharge rates according to an embodiment of the present disclosure is shown.

[0035] Figure 14 A schematic diagram showing a series of curves showing changes in the internal resistance correction coefficient of a battery cell with respect to the battery cell temperature at different initial SOCs and different discharge rates according to an embodiment of the present disclosure.

[0036] Figure 15 A block diagram of a device for determining a cell internal resistance correction coefficient in a battery system according to an embodiment of the present disclosure is shown.

[0037] Figure 16 A block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0038] Reference numerals

[0039] exist Figure 2 、 Figure 3and Figure 4 Among them, 201 represents the cover simulation module, 202 represents the air simulation module, 203 represents the copper bus simulation module, 204 represents the foam simulation module, 205 represents the battery cell simulation module, 206 represents the aluminum-plastic film simulation module, 207 represents the tab simulation module, 208 represents the structural adhesive simulation module, and 209 represents the box simulation module. DETAILED DESCRIPTION

[0040] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0041] As used herein, the terms "comprises," "comprising," "having," or variations thereof are open ended and include one or more stated features, integers, elements, steps, parts, or functions, but do not preclude the presence or addition of one or more other features, integers, elements, steps, parts, functions, or groups thereof.

[0042] When an element is referred to as being "connected," "coupled," "responsive" or variations thereof to another element, it can be directly connected, coupled or responsive to the other element or intervening elements may be present.

[0043] Although the terms first, second, third, etc. may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another element / operation. Therefore, without departing from the teachings of the present invention, the first element / operation in some embodiments may be referred to as the second element / operation in other embodiments.

[0044] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0045] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0046] As mentioned above, since the internal resistance of the battery cell is related to many factors such as rate, temperature, and SOC, there will be a certain deviation between the three-dimensional thermal simulation results of the battery system and the actual test results. How to correct the three-dimensional thermal simulation model, that is, how to correct the heat generation power of the battery cell, is one of the research contents in the field of three-dimensional thermal simulation of battery systems. At present, the common practice in the industry to obtain the heat generation power of battery cells is: 1. Give the battery cell a constant power or constant internal resistance value, without considering the change of internal resistance with SOC, rate, and temperature. This method has general accuracy and is not universal; 2. Obtain the battery cell internal resistance MAP through one-dimensional electrochemical simulation of the battery. The battery cell internal resistance MAP includes internal resistance values ​​at different temperatures and different rates, but this method requires detailed battery cell design parameters such as the ratio, concentration, conductivity and other parameters of positive and negative electrode materials. These parameters are often difficult to obtain, and the electrochemical simulation model also has certain errors; 3. Obtain the battery cell internal resistance MAP through a one-dimensional equivalent circuit method. This method also requires a large amount of rigorous battery cell test data, such as using the hybrid pulse power characteristic (HPPC) test method to test the battery cell internal resistance, but this method will increase the test cost and simulation cost.

[0047] The disclosed embodiments are intended to improve the accuracy of three-dimensional thermal simulation and propose a method for determining a correction coefficient of the internal resistance of a battery cell, a method for generating a prediction model, and a method for estimating temperature rise. By assigning a certain correction coefficient to the internal resistance of the battery cell, the simulation result of the heat generation power of the battery cell is consistent with the measured result, that is, accurate heat generation power of the battery cell is obtained, and the correction coefficient of the internal resistance of the battery cell is related to the temperature, SOC, rate and other operating conditions of the battery cell. A prediction model for the correction coefficient of the internal resistance of the battery cell can also be obtained. The prediction model can be applied to predict the internal resistance correction coefficient of the battery cell at any rate, any initial SOC, and any battery cell temperature. The prediction model can be used for three-dimensional thermal simulation evaluation of other battery systems with the same type of battery cells, which is beneficial to improving the accuracy of the three-dimensional thermal simulation evaluation of the battery system (such as predicting battery temperature rise, etc.).

[0048] The method proposed in the embodiment of the present disclosure does not require one-dimensional simulation analysis such as electrochemical simulation of the battery cell and equivalent circuit model simulation of the battery cell to obtain the internal resistance of the battery cell or the heat generation power of the battery cell, nor does it require battery cell parameter testing for the battery cell. That is, when it is difficult to obtain the battery cell parameters, the three-dimensional simulation technology can be used to achieve accurate correction of the heat generation power of the battery cell and prediction of the temperature rise of the battery system. It has the characteristics of simple parameter acquisition, strong feasibility and higher accuracy.

[0049] In practical applications, the method of the embodiment of the present disclosure can be deployed on various terminal devices through software or hardware modification. The terminal device involved in the embodiment of the present disclosure may refer to a device with a wireless connection function and / or a wired connection function. The wireless connection function refers to the ability to connect to other devices through wireless connection methods such as wifi and Bluetooth. The terminal device involved in the embodiment of the present disclosure may also communicate with other devices through a wired connection function. The terminal device involved in the embodiment of the present disclosure may be a touch screen, a non-touch screen, or a screenless device. The touch screen device can be controlled by clicking, sliding, etc. on the display screen with a finger or a stylus. The non-touch screen device can be connected to an input device such as a mouse, keyboard, touch panel, etc., and the terminal device can be controlled through the input device. For example, a device without a screen can be a Bluetooth speaker without a screen. For example, the terminal device of the present application may include but is not limited to user equipment (UE), mobile device, user terminal, terminal, handheld device, tablet computer, laptop computer, PDA, computing device, etc.

[0050] The method of the embodiment of the present disclosure can also be deployed on a server, which can be located in the cloud or locally, and can be a physical device or a virtual device, such as a virtual machine, a container, etc., with a wireless communication function, wherein the wireless communication function can be set in the chip (system) or other parts or components of the server. It can refer to a device with a wireless connection function, and the wireless connection function means that it can be connected to other servers or terminal devices through wireless connection methods such as Wi-Fi and Bluetooth. The server involved in the embodiment of the present disclosure can also have the function of communicating through a wired connection. For example, the server can receive a specified working condition sent by the terminal device, and the server executes the battery system temperature rise prediction method of the embodiment of the present disclosure to obtain the temperature rise prediction result of the battery system, and returns the temperature rise prediction result to the terminal device to display the determined temperature rise prediction result to the user in the terminal device.

[0051] Figure 1 A flow chart showing a method for determining a cell internal resistance correction coefficient in a battery system according to an embodiment of the present disclosure is shown. Figure 1 As shown, the method includes:

[0052] Step S11, using an initial three-dimensional thermal simulation model of the battery system to simulate a discharge or charge process of the battery system under specified operating conditions, to obtain simulated temperature rise data of a specified temperature-sensing point on a specified battery cell in the battery system; wherein the simulated temperature rise data includes a simulated temperature change of the specified temperature-sensing point over time; wherein the battery cell simulation module in the initial three-dimensional thermal simulation model is assigned an original battery cell heat generation power;

[0053] Step S12, obtaining measured temperature rise data of a specified temperature sensing point on a specified battery cell in the battery system, wherein the measured temperature rise data includes the measured temperature change of the specified temperature sensing point over time during the discharge or charging process of the battery system under specified operating conditions;

[0054] Step S13, determining initial cell internal resistance correction data based on the measured temperature rise data and the simulated temperature rise data, wherein the initial cell internal resistance correction data includes a cell internal resistance correction coefficient that varies with time;

[0055] Step S14, using a target three-dimensional thermal simulation model of the battery system and the initial cell internal resistance correction data to simulate a discharge or charge process of the battery system under specified operating conditions, and obtain a cell temperature of the entire specified cell that changes over time; wherein the cell simulation module in the target three-dimensional thermal simulation model is assigned a target cell heat generation power corrected by the cell internal resistance correction coefficient;

[0056] Step S15 , determining target cell internal resistance correction data according to the cell temperature of the designated cell that changes with time and the initial cell internal resistance correction data, wherein the target cell internal resistance correction data includes a cell internal resistance correction coefficient that changes with the cell temperature.

[0057] In step S11, the battery system can be understood as a battery pack. It should be understood that the battery system may include a plurality of battery cells, a plurality of tabs connected to the plurality of battery cells, copper bars, structural adhesives, aluminum-plastic films, foams, shells (including box bodies, covers) and other structures. Among them, the battery cells, tabs, copper bars, structural adhesives, aluminum-plastic films, foams, shells and air in the battery system are the main heat transfer media, and the battery cells and tabs play the role of heat generation and heat conduction, and the copper bars, structural adhesives, foams, aluminum-plastic films, shells and air and other media only play the role of heat conduction. Therefore, the three-dimensional thermal simulation modeling technology known in the art can be used to establish an initial three-dimensional thermal simulation model of the battery system based on the actual structure of the battery system. For example, the battery system can be constructed according to the actual structure of the battery system. The size and shape of each heat transfer medium are used to build a three-dimensional geometric model of the battery system, and then the corresponding original battery cell heat generation power is assigned to the battery cell simulation module of each battery cell in the three-dimensional geometric model. Optionally, the corresponding original tab heat generation power can also be assigned to the tab simulation module of the tab, and parameters related to heat conduction are assigned to each other heat transfer medium, that is, copper busbar, structural adhesive, aluminum-plastic film, foam, shell (including box body, cover body), etc., such as parameters such as specific heat capacity, density and thermal conductivity coefficient. It should be understood that the parameters assigned to each heat transfer medium are related to the material properties of each heat transfer medium. Those skilled in the art can customize the parameters assigned to each heat transfer medium according to the material of each heat transfer medium, and the embodiments of the present disclosure are not limited to this.

[0058] For example, Figure 2An exploded view of a three-dimensional thermal simulation model of a battery system is shown. Figure 3 An internal module view of a three-dimensional thermal simulation model of a battery system is shown. Figure 4 A schematic diagram of a battery cell simulation module for a single battery cell in a three-dimensional thermal simulation model of a battery system is shown. Figures 2 to 4 In the simulation model shown, each battery cell in the battery system can be modeled using an equivalent hexahedron block, and the tab can be modeled using a thin solid, that is, the battery cell simulation module 205 of each battery cell can be an equivalent hexahedron block, and the tab simulation module 207 of the tab connected to each battery cell can be a thin solid. The battery cell simulation module 205 and the tab simulation module 207 can be connected using a grid common node, and the two are connected in a conformal surface on the top surface of the battery cell simulation module. Other heat transfer media such as copper busbars, structural adhesives, aluminum-plastic films, foams, and shells (including box bodies and covers) can be modeled according to actual physical shapes or simplified physical shapes, and the mutual contact areas between simulation modules of other media are also connected in a conformal surface, and the embodiments of the present disclosure do not limit this. It should be understood that the above Figures 2 to 4 The three-dimensional thermal simulation model of the battery system shown is a possible implementation method provided by the embodiment of the present disclosure, and does not mean that the embodiment of the present disclosure is only applicable to battery systems of this type of structure. In fact, those skilled in the art can construct an initial three-dimensional thermal simulation model of the battery system based on the specific structure of the battery system, and the embodiment of the present disclosure does not limit this.

[0059] In practical applications, an initial three-dimensional thermal simulation model of the battery system can be built in three-dimensional thermal simulation software known in the art. Then, based on the simulation computing capability of the three-dimensional thermal simulation software, the initial three-dimensional thermal simulation model can be used to simulate the process of discharging or charging the battery system under specified working conditions, and the simulated temperature rise data of the specified temperature-sensing point on the specified battery cell in the battery system can be obtained. The embodiment of the present disclosure does not limit the simulation computing process within the three-dimensional thermal simulation software, as long as the required simulation computing function is realized. It can be seen that the three-dimensional thermal simulation software can calculate the temperature of each position in the battery system at each time. In step S11, only the temperature of the specified temperature-sensing point on the battery simulation module corresponding to the specified battery cell can be extracted to obtain the simulated temperature of the specified temperature-sensing point on the specified battery cell that changes with time.

[0060] It should be understood that the operating conditions of the battery system during charging or discharging can be customized by battery cell professionals or based on customer needs. The specified operating conditions may include a specified initial state of charge and a specified rate. The specified rate includes a specified charge rate or a specified discharge rate. If the user wants to obtain the target cell internal resistance correction data during the battery system's discharge process, the initial three-dimensional thermal simulation model of the battery system can be used to simulate the battery system's discharge process under the specified operating conditions. Specifically, it can be used to simulate the battery system's discharge process from a specified initial state of charge at a specified discharge rate. In this case, the simulated temperature rise data of a specified temperature sensing point on the specified cell during the discharge process is obtained. Correspondingly, if the user wants to obtain the target cell internal resistance correction data during the battery system's charging process, the initial three-dimensional thermal simulation model of the battery system can be used to simulate the battery system's charging process under the specified operating conditions. Specifically, it can be used to simulate the battery system's charging process from a specified initial state of charge at a specified charge rate. In this case, the simulated temperature rise data of a specified temperature sensing point on the specified cell during the charging process is obtained.

[0061] As described above, the battery system includes multiple battery cells, and the designated battery cell can be any battery cell among the multiple battery cells. In actual applications, considering that the battery system is usually located in the middle position, the temperature is the highest, and the battery cell located at the outermost position has the lowest temperature during the charging and discharging process, and it is generally necessary to focus on the highest temperature of the battery cell or battery pack, the temperature sensing point on the middle battery cell can be used as the benchmark object, and the designated battery cell can include the battery cell located in the middle position among the multiple battery cells; the designated temperature sensing point can be any position on the designated battery cell, specifically, it can be the location point where the temperature sensor is arranged on the designated battery cell, and the embodiments of the present disclosure do not limit this.

[0062] Based on the specified rate in the above-mentioned specified working conditions, the original cell heat generation power assigned by the cell simulation module of each cell can be determined based on the cell capacity of the cell in the battery system, the original internal resistance of the cell and the specified rate. Specifically, the original cell heat generation power P_cell is calculated using the current I and the cell internal resistance R and assigned to the cell simulation module of each cell, that is, P_cell=I 2 R, where the current I is calculated from the cell capacity Ca and the specified rate Cr (i.e., the specified discharge rate or the specified charge rate), i.e., I = Cr * Ca, then P_cell = I 2 R=(Cr*Ca) 2 R, wherein the cell capacity can be understood as the maximum amount of electricity that the cell can store, and the cell capacity can also be customized by cell professionals or based on customer needs; wherein the original internal resistance of the cell can be an average internal resistance estimated by cell professionals based on experience, which can be understood as a preliminary estimate of the cell internal resistance, and the embodiments of the present disclosure do not limit this.

[0063] In step S12, the user can arrange a designated temperature sensing point on a designated battery cell of the battery system. The designated temperature sensing point is a point on the designated battery cell where a temperature sensor is used to sense the temperature. The point is generally located on the top surface of the battery cell. As mentioned above, the highest temperature point during the charge and discharge process is generally located on the battery cell in the middle position. The designated battery cell can be the battery cell in the middle position. The designated temperature sensing point where the temperature sensor is located on the battery cell in the middle position can be recorded as NTC1. In order to facilitate subsequent verification of the effective effect of the battery cell internal resistance correction coefficient on temperature rise prediction, a temperature sensor can also be set on the outermost battery cell with a lower temperature. The temperature sensing point where the temperature sensor set on the outermost battery cell is located can be recorded as NTC2. For example, Figure 5 A schematic diagram showing the setting positions of the two temperature sensing points is shown; then the battery system can be actually charged or discharged according to the same specified working conditions. In this process, the measured temperature rise data collected by the temperature sensors located at NTC1 and NTC2 can be obtained, wherein the measured temperature rise data collected by the temperature sensor located at NTC1 is also the measured temperature rise data of the specified temperature sensing point on the specified battery cell. For example, if the sampling interval of the temperature sensor is 1s, the measured temperature rise data collected by the temperature sensors at NTC1 and NTC2 can be expressed as follows: Figure 6 The curves of the measured temperatures of the temperature sensing points NTC1 and NTC2 changing with time are shown.

[0064] For example, by performing a three-dimensional thermal simulation calculation of the battery system under the specified working conditions in the above step S11, the simulation software calculates the simulated temperature of the specified temperature sensing point NTC1 at each moment, and sets the simulation software to output a curve of the simulated temperature of the specified temperature sensing point NTC1 changing with time, and the result output interval is 1s, and the curve of the measured temperature of NTC1 changing with time is obtained in step S12, it can be obtained Figure 7 The comparison diagram of the simulated temperature change curve and the measured temperature change curve at the specified temperature sensing point NTC1 is shown in FIG. Figure 7 It can be seen that there is a certain difference between the simulated temperature change curve at NTC1 and the measured temperature change curve. This may be because the original cell heat generation power assigned in the initial three-dimensional thermal simulation model uses the original fixed internal resistance of the cell, resulting in a certain difference between the simulated temperature of the specified temperature sensing point calculated using the initial three-dimensional thermal simulation model and the measured temperature, resulting in low simulation accuracy.

[0065] Therefore, after obtaining the measured temperature rise data and the simulated temperature rise data of the specified temperature sensing point, in step S13, the time-varying battery cell internal resistance correction coefficient can be first determined based on the difference between the measured temperature rise data and the simulated temperature rise data. The time-varying battery cell internal resistance correction coefficient can correct the battery cell internal resistance of the specified battery cell during the charging and discharging process, which is also equivalent to correcting the battery cell heat generation power of the specified battery cell. It should be understood that the time-varying battery cell internal resistance correction coefficient calculated by step S13 is calculated based on the measured temperature rise data and the simulated temperature rise data at the specified temperature sensing point of the specified battery cell. Therefore, it can only accurately correct the battery cell internal resistance of the specified battery cell (that is, the battery cell heat generation power), but cannot accurately correct The internal resistance of each battery cell in the battery system (that is, the battery cell heat generation power correction). Therefore, the embodiment of the present disclosure also obtains the battery cell temperature of the specified battery cell as a whole that changes with time through step S14, and then obtains the battery cell internal resistance correction coefficient that changes with the battery cell temperature through step S15, so that the battery cell internal resistance correction coefficient under the specified working condition is related to the battery cell temperature. The battery cell temperatures of different battery cells are different during the charging and discharging process under the specified working condition. By using the battery cell internal resistance correction coefficient that changes with the battery cell temperature, the different battery cell internal resistance correction coefficients corresponding to each battery cell at its respective battery cell temperature can be obtained, and then the battery cell heat generation power of each battery cell can be corrected to obtain a more accurate battery cell heat generation power for each battery cell.

[0066] In practical applications, in step S14, the target three-dimensional thermal simulation model of the battery system is used to simulate the process of discharging or charging the battery system under specified working conditions based on the initial battery cell internal resistance correction data. The simulated temperatures at the temperature sensing points NTC1 and NTC2 can also be extracted at the same time to compare with the actual measured temperatures at NTC1 and NTC2 obtained in step S12. For example, the simulation software can calculate the simulated temperature of the temperature sensing point NTC1 at each moment based on the initial battery cell internal resistance correction data, and set the simulation software to output a curve of the simulated temperature of the temperature sensing point NTC1 changing with time. The result output interval is 1s, and the following can be obtained: Figure 8 The comparison diagram of the simulated temperature change curve of the temperature sensing point NTC1 and the measured temperature change curve is shown in the figure. Figure 8 As shown, the NTC1 simulated temperature change calculated by the time-varying cell internal resistance correction coefficient obtained in the above step S13 is basically consistent with the measured temperature change and is in a coincidence state; at the same time, the simulation software also calculates the simulated temperature of the temperature sensing point NTC2 at each moment based on the initial cell internal resistance correction data, and sets the simulation software to output the curve of the simulated temperature change of the temperature sensing point NTC2 over time. The result is output at an interval of 1s, and the following can be obtained: Figure 9 The comparison diagram of the simulated temperature change curve of the temperature sensing point NTC2 and the measured temperature change curve is shown in FIG. Figure 9As shown, there is still a certain degree of deviation between the simulated temperature change of the temperature sensing point NTC2 and the measured temperature change. This is because the cell internal resistance correction coefficient used in the target cell heat generation power of the cell where the temperature sensing point NTC2 is located is determined based on the cell internal resistance correction coefficient that changes with time calculated based on the measured temperature rise data and the simulated temperature rise data at the temperature sensing point NTC1. It cannot truly reflect the cell internal resistance error of the cell where the temperature sensing point NTC2 is located. Therefore, as mentioned above, the embodiment of the present disclosure also obtains the cell temperature of the specified cell that changes with time as a whole through step S14, and then obtains the cell internal resistance correction coefficient that changes with the cell temperature through step S15, which can make the specified cell internal resistance correction coefficient The cell internal resistance correction coefficient under the working condition is related to the cell temperature. The cell temperatures of different cells are different during the charge and discharge process under the specified working condition. Therefore, a different cell internal resistance correction coefficient can be obtained for each cell to correct the cell heat generation power of each cell. That is, the cell internal resistance correction coefficient that changes with the cell temperature obtained in step S15 can be applied to the cell heat generation power correction of each cell in the battery system. Compared with using the cell heat generation power that changes with time, using the cell internal resistance correction coefficient that changes with the cell temperature can enable the target three-dimensional thermal simulation model of the battery system to achieve more accurate three-dimensional thermal simulation calculation and obtain more accurate temperature rise prediction results.

[0067] In step S14, the original cell heat generation power assigned by each cell simulation module in the initial three-dimensional thermal simulation model can be corrected to the product of the current I, the cell internal resistance R and the cell internal resistance correction coefficient β in the three-dimensional thermal simulation software, that is, the target cell heat generation power assigned by each cell simulation module is expressed as P_pell_s=I 2 Rβ=(Cr*Ca) 2 Rβ, thereby obtaining the target three-dimensional thermal simulation model, that is, the battery cell simulation module in the target three-dimensional thermal simulation model is endowed with the target battery cell heat generation power corrected by the battery cell internal resistance correction coefficient, and the target battery cell heat generation power is determined based on the battery cell capacity of the battery cell in the battery system, the original internal resistance of the battery cell, the specified rate and the battery cell internal resistance correction coefficient; then the simulation computing capability of the target three-dimensional thermal simulation model can be used to realize the use of the target three-dimensional thermal simulation model according to the initial battery cell internal resistance correction data to simulate the process of discharging or charging of the battery system under specified working conditions, and obtain the battery cell temperature of the specified battery cell that changes with time as a whole. For example, the battery cell temperature can be the average body temperature of the battery cell simulation module corresponding to the specified battery cell, or the overall average temperature of the specified battery cell. It should be understood that when using the target three-dimensional thermal simulation model to simulate the charging or discharging process of the battery system under specified working conditions, the battery cell internal resistance correction coefficient at each moment in the simulation process can be obtained from the initial battery cell internal resistance correction data, so that the battery cell internal resistance correction coefficient at each moment can be brought into the target battery cell heat generation power assigned by each battery cell simulation module to obtain the battery cell temperature through simulation calculation.

[0068] As mentioned above, the three-dimensional thermal simulation software can calculate the temperature of each location in the battery system at each time. The simulation software can also extract the average temperature of a certain volume. For example, within the three-dimensional thermal simulation software, the battery cell simulation module can be divided into many units, and the units are composed of node connections. The three-dimensional simulation software can calculate the temperature value of each node, and the temperature values ​​of all nodes under the entire volume of the battery cell simulation module can be obtained. Then, by averaging or weighted averaging, the average temperature of the entire volume of the battery cell simulation module (i.e., the body average temperature) can be obtained. The average temperature of the entire volume of the battery cell simulation module is used as the battery cell temperature calculated by simulation.

[0069] It should be understood that the cell internal resistance correction coefficient at each moment in the charging or discharging process under the specified working conditions can be obtained through step S13. At the same time, in step S14, the simulation software can also output the average body temperature of the specified cell at each moment (i.e., the cell temperature) during the simulation process. For example, the simulation software can be set to output the average body temperature of the cell simulation module of the specified cell where NTC1 is located, and the output time interval is 1s, so that the cell temperature of the specified cell that changes with time can be obtained; then, in step S15, the cell internal resistance correction coefficient that changes with time and the cell temperature of the specified cell that changes with time can be converted to obtain the cell internal resistance correction coefficient that changes with the cell temperature. It should be understood that because the cell internal resistance correction coefficient and the cell temperature both change with time, the cell internal resistance correction coefficient that changes with the cell temperature can be obtained by removing the time term. For example, Figure 10 A schematic diagram of a cell internal resistance correction coefficient curve that varies with cell temperature is shown. The cell internal resistance correction coefficient β (ie, target cell internal resistance correction data) that varies with cell temperature Tcell can be expressed as a β-Tcell curve.

[0070] In practical applications, after obtaining the target cell internal resistance correction data under specified operating conditions, that is, the above-mentioned cell internal resistance correction coefficient that changes with the cell temperature, the above-mentioned target three-dimensional thermal simulation model can be used to predict the temperature rise of each cell when the battery system is charging and discharging under the specified operating conditions based on the cell internal resistance correction coefficient that changes with the cell temperature, that is, predict the temperature rise of the entire battery system. In this prediction process, the target cell internal resistance correction data (that is, the cell internal resistance correction coefficient that changes with the cell temperature) can be brought into the target cell heat generation power assigned by each cell simulation module in the target three-dimensional thermal simulation model to correct the target cell heat generation power, that is, the corrected cell internal resistance, thereby improving the prediction accuracy of the battery system temperature rise.

[0071] According to the method of the embodiment of the present disclosure, the initial three-dimensional thermal simulation model of the battery system is used to simulate the charging or discharging process under specified working conditions to obtain simulated temperature rise data of the specified temperature sensing point of the specified battery cell, and the measured temperature rise data of the specified temperature sensing point of the specified battery cell is obtained through actual measurement, and the battery cell internal resistance correction coefficient that changes with time is determined. Then, the target three-dimensional thermal simulation model is used to simulate the battery cell temperature of the entire specified battery cell that changes with time based on the battery cell internal resistance correction coefficient that changes with time, so that the battery cell internal resistance correction coefficient that changes with battery cell temperature under specified working conditions can be obtained. The battery cell internal resistance correction coefficient that changes with the average battery cell temperature can be used to more accurately correct the target battery cell heat generation power assigned by each battery cell simulation module in the target three-dimensional thermal simulation model, so that the simulation results are consistent with the measured results. Then, the battery cell internal resistance correction coefficient that changes with battery cell temperature and the target three-dimensional thermal simulation model can be used to predict the temperature rise of the battery system during the charging and discharging process more accurately.

[0072] As described above, in step S13, the initial battery cell internal resistance correction data can be determined by the difference between the measured temperature rise data and the simulated temperature rise data. For example, the battery cell internal resistance correction coefficient at each moment can be obtained by calculating the ratio of the simulated temperature at each moment in the simulated temperature rise data to the measured temperature at each moment in the measured temperature rise data, that is, the initial battery cell internal resistance correction data is obtained. However, the battery cell internal resistance correction coefficient determined by this method is not accurate enough.

[0073] In one possible implementation, the embodiment of the present disclosure also provides another implementation for determining the initial cell internal resistance correction data. Specifically, in the above step S13, determining the initial cell internal resistance correction data based on the measured temperature rise data and the simulated temperature rise data may include:

[0074] Step S131, determining a target simulated temperature difference corresponding to a designated temperature sensing point based on the simulated temperature at the nth moment in the simulated temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data;

[0075] Step S132, determining the measured temperature difference corresponding to the designated temperature sensing point based on the measured temperature at the nth moment in the measured temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data;

[0076] Step S133 , determining the ratio between the target simulated temperature difference and the measured temperature difference as the cell internal resistance correction coefficient from the nth moment to the n+1th moment.

[0077] Among them, it is assumed that the simulated temperature at the nth moment in the simulated temperature rise data is Tsn, and the simulated temperature at the n+1th moment is Ts(n+1); the measured temperature at the nth moment in the measured temperature rise data is Ttn, and the measured temperature at the n+1th moment is Tt(n+1); it can be known that the calculation formula for the transferred heat of any medium is Q=CpMΔT, where Q is heat, Cp is specific heat capacity, M is mass, and ΔT is the temperature difference between the previous and next two moments. Based on the heat transfer calculation formula, it can be obtained that the simulated heat transfer of the specified temperature sensing point from the nth moment to the n+1th moment is Q=CpM(Ts(n+1)-Tsn), and the measured heat transfer corresponding to the specified temperature sensing point is Q'=CpM(Tt(n+1)-Ttn). It should be understood that the specific heat capacity Cp and mass M used in the above-mentioned target simulated heat transfer and measured heat transfer are the specific heat capacity and mass of the specified battery cell. The embodiments of the present disclosure do not limit the method of obtaining the specific heat capacity and mass of the battery cell.

[0078] It can be understood that the goal of the cell internal resistance correction coefficient β is to make the simulated temperature Ts(n+1) ≈ the measured temperature Tt(n+1) at each moment. Then, from the nth moment to the n+1th moment, the target simulated heat transfer that the specified temperature sensing point should transfer can be expressed as Q*=CpM(Tt(n+1)-Tsn). Then, the cell internal resistance correction coefficient β from the nth moment to the n+1th moment, i.e., the n+1th step, is Q* / Q'=(Tt(n+1)-Tsn) / (Tt(n+1)-Ttn); wherein, Ts0=Tt0, i.e., the initial temperature in the simulated temperature rise data is the same as that in the measured temperature rise data, and n≥0. It should be understood that , the measured data that changes with time in the measured temperature rise data can be used as the simulation target input, and when calculating the battery cell internal resistance correction coefficient corresponding to each time step (such as the (n+1) step), the simulated temperature Tsn of the specified temperature sensing point in the previous time step, the measured temperature Ttn of the specified temperature sensing point in the previous time step, and the measured temperature Tt(n+1) of the specified temperature sensing point in the current time step can be extracted, and the above formula β=(Tt(n+1)-Tsn) / (Tt(n+1)-Ttn) is used to calculate the battery cell internal resistance correction coefficient corresponding to each time step, that is, the battery cell internal resistance correction coefficient that changes with time is obtained.

[0079] According to an embodiment of the present disclosure, by calculating the initial cell internal resistance correction data through steps S131 to S133, an accurate cell internal resistance correction coefficient that can correct the cell internal resistance can be calculated using the simulated temperature rise data and the measured temperature rise data.

[0080] Taking into account that each battery cell in the battery system is connected to a tab, the tab will also generate heat during the charging and discharging process of the battery system. The tab heat generation is also one of the factors affecting the temperature change of the battery cell, and the battery cell temperature will affect the internal resistance of the battery cell. Therefore, in order to further obtain more accurate target battery cell internal resistance correction data to improve the simulation accuracy of the three-dimensional thermal simulation model, in step S11, the tab simulation module in the initial three-dimensional thermal simulation model of the battery system can also be assigned an original tab heat generation power, and the original tab heat generation power is a specified proportion of the corresponding original battery cell heat generation power, that is, the original tab heat generation power assigned by a certain tab simulation module can be a specified proportion of the original battery cell heat generation power assigned by the battery cell simulation module connected to the tab simulation module. For example, according to experience, the tab heat generation power of a single tab is approximately 5% of the battery cell heat generation power of the battery cell to which it is connected, that is, the original tab heat generation power can be P_tab=0.05*P_cell, and the original tab heat generation power of 0.05*P_cell can be assigned to the tab simulation module. Correspondingly, in the above step S14, the tab simulation module in the target three-dimensional thermal simulation model of the battery system can also be assigned a target tab heat generation power, and the target tab heat generation power is a specified proportion of the corresponding target battery cell heat generation power. For example, if the specified proportion is 5%, that is, the target tab heat generation power P_tab_s = 0.05*P_cell_s, then the target tab heat generation power of 0.05*P_cell_s can be assigned to the tab simulation module, or in other words, the original tab heat generation power assigned by each tab simulation module in the initial three-dimensional thermal simulation model can be corrected to the target tab heat generation power, so that the target three-dimensional thermal simulation model can combine the battery cell heat generation and the tab heat generation to achieve more accurate battery temperature prediction, which is conducive to obtaining more accurate target battery cell internal resistance correction data.

[0081] It should be understood that the three-dimensional thermal simulation technology can be used to calculate the temperature field of the entire battery system by assigning heat generation power to each battery cell in the battery system. The heat generation power of each battery cell is characterized by the internal resistance of the battery cell. Therefore, it is ultimately necessary to obtain the target battery cell internal resistance correction data of a single battery cell under specified operating conditions. Furthermore, after obtaining the target battery cell internal resistance correction data under specified operating conditions, that is, the battery cell internal resistance correction coefficient that varies with the battery cell temperature, the target three-dimensional thermal simulation model can be used to predict the temperature rise of each battery cell when the battery system is charged and discharged under specified operating conditions based on the battery cell internal resistance correction coefficient that varies with the battery cell temperature. That is, the temperature rise of the entire battery system is predicted. Therefore, the embodiment of the present disclosure also provides a battery system temperature rise prediction method, including:

[0082] Step S31, using the cell internal resistance correction coefficient determination method, determining the target cell internal resistance correction data of the battery system under the specified operating conditions; it should be understood that the implementation of step S31 can refer to the implementation of steps S11 to S15 in the above-mentioned embodiment of the present disclosure, and will not be repeated here;

[0083] Step S32 , using the target three-dimensional thermal simulation model of the battery system and correcting the target cell internal resistance data, simulates the process of discharging or charging the battery system under specified working conditions to obtain a temperature rise prediction result of the battery system.

[0084] Among them, when using the target three-dimensional thermal simulation model of the battery system to simulate the discharge or charging process of the battery system under specified working conditions according to the target battery cell internal resistance correction data, the battery cell internal resistance correction coefficient used in the target battery cell heat generation power and the target tab heat generation power assigned by the battery cell simulation module and the tab simulation module in the target three-dimensional thermal simulation model is determined based on the battery cell temperature and target battery cell internal resistance correction data of each battery cell during the simulation process.

[0085] As mentioned above, the target cell internal resistance correction data can be expressed as a β-Tcell curve. In the three-dimensional thermal simulation software, the β-Tcell curve can be input into the target three-dimensional thermal simulation model, that is, the relationship between the cell internal resistance correction coefficient and the cell temperature is obtained, and the β-Tcell curve is applied to each cell simulation module, and then the cell internal resistance correction coefficient corresponding to each cell simulation module is determined according to the real-time cell temperature of each cell simulation module, so as to obtain the target cell heat generation power corresponding to each cell, thereby realizing the three-dimensional thermal simulation calculation of the entire battery system; wherein, in the process of performing simulation calculations based on the target three-dimensional thermal simulation model, the simulation software can obtain the average body temperature (i.e., cell temperature) of each cell simulation module at the current moment, recorded as Tcell1, Tcell2, ..., Tcelln. It should be understood that the average body temperature of each cell at the same moment is different; then, the average body temperature of each cell at the current moment can be determined from the β-Tcell curve. The cell internal resistance correction coefficient used is brought into the target cell heat generation power assigned to each cell simulation module in the target three-dimensional thermal simulation model, so that the target cell heat generation power of each cell is different and the target tab heat generation power connected to each cell is also different, and the heat generation power depends on the cell temperature of each cell at the current moment; for example, there are a total of 10 cells in the battery system. During the simulation process, the body average temperature of the 10 cell simulation modules can be obtained at a certain moment. Based on the body average temperature of these 10 cell simulation modules, the simulation software queries the input β-Tcell curve to obtain the corresponding 10 cell internal resistance correction coefficients, and then applies them to the target cell heat generation power in the 10 cell simulation models respectively. Then, these 10 cells have different heat generation powers. At the same time, the target tab heat generation power of the tab simulation module connected to each cell simulation module is 5% of the target cell heat generation power of the cell simulation module to which it is connected, so that the heat generation power of the tab connected to each cell can also depend on the body average temperature of each cell.

[0086] Therefore, the three-dimensional thermal simulation software can use its simulation computing capability to simulate the process of discharging or charging the battery system under specified working conditions based on the above-mentioned β-Tcell curve and the target three-dimensional thermal simulation model, and obtain the cell temperature of each cell in the battery system that changes with time (such as the average body temperature of each cell that changes with time). The temperature rise prediction result of the battery system may include the cell temperature of each cell that changes with time, or may also include the battery temperature of the battery system as a whole that changes with time. For example, the battery temperature of the battery system as a whole that changes with time can be obtained by calculating the mean or weighted average of the cell temperatures of each cell that changes with time. This is not limited to the embodiments of the present disclosure.

[0087] In practical applications, the target three-dimensional thermal simulation model of the battery system is used to correct the data of the target cell internal resistance to simulate the discharge or charging process of the battery system under specified working conditions. The simulated temperature of the two temperature sensing points NTC1 and NTC2 changing with time can also be extracted at the same time, and the Figure 11 and Figure 12 The temperature rise comparison diagrams of NTC1 and NTC2 are shown respectively, where: Figure 11 The figure shows the measured temperature change curve of NTC2 (i.e., the orange curve indicated by NTC2-measured temperature in the figure), the simulated temperature change curve of NTC2 obtained by performing a three-dimensional thermal simulation using the initial battery cell internal resistance correction data (i.e., the black curve indicated by NTC2-simulated temperature A in the figure), and the simulated temperature change curve of NTC2 obtained by performing a three-dimensional thermal simulation using the target battery cell internal resistance correction data (i.e., the green curve indicated by NTC2-simulated temperature B in the figure). Figure 12 The figure shows a curve showing the measured temperature of NTC1 changing with time (i.e., the orange curve indicated by NTC1-measured temperature in the figure), a curve showing the simulated temperature of NTC1 changing with time obtained by performing a three-dimensional thermal simulation using the initial cell internal resistance correction data (i.e., the black curve indicated by NTC1-simulated temperature A in the figure), and a curve showing the simulated temperature of NTC1 changing with time obtained by performing a three-dimensional thermal simulation using the target cell internal resistance correction data (i.e., the green curve indicated by NTC1-simulated temperature B in the figure); Figure 11 It can be seen that compared with the three-dimensional thermal simulation using the initial battery cell internal resistance correction data, the deviation between the simulated temperature change curve and the measured temperature change curve obtained by the three-dimensional thermal simulation using the target battery cell internal resistance correction data is further reduced, and Figure 12 It can be seen that no matter whether the initial cell internal resistance correction data or the target cell internal resistance correction data is used, the deviation between the simulated temperature change curve of NTC1 and the measured temperature change curve is still very small, and the three curves almost overlap; Figure 11 and Figure 12 It can be shown that the target cell internal resistance correction data can be applied to the heat generation power correction of each cell in the battery system, and the temperature rise obtained by performing three-dimensional thermal simulation using the target cell internal resistance correction data is more accurate, thereby effectively improving the prediction accuracy of the temperature rise of the battery system during charging and discharging under specified working conditions.

[0088] According to the battery system temperature rise prediction method of the embodiment of the present disclosure, the target battery cell internal resistance correction data can be used to more accurately predict the temperature rise of the battery system.

[0089] It should be understood that through the above embodiments of the present disclosure Figure 1The method for determining the cell internal resistance correction coefficient shown can determine the cell internal resistance correction coefficient that changes with the cell temperature under specified working conditions. In actual situations, the battery system may be charged and discharged under any working conditions (i.e., any initial state of charge and any rate). Therefore, in a possible implementation, the above-mentioned Figure 1 The cell internal resistance correction coefficient determination method shown is used to determine target cell internal resistance correction data under multiple specified operating conditions, and then construct a cell internal resistance correction coefficient prediction model to predict the cell internal resistance correction coefficient for any cell temperature under any operating condition. Specifically, the embodiment of the present disclosure further provides a cell internal resistance correction coefficient prediction model generation method, including:

[0090] Step S31, obtaining multiple specified operating conditions of the battery system, wherein at least one of the specified rate and the specified initial state of charge in different specified operating conditions is different;

[0091] Step S32, using the battery cell internal resistance correction coefficient determination method, determining target battery cell internal resistance correction data under each of a plurality of specified operating conditions;

[0092] Step S33, based on the target cell internal resistance correction data under each of the multiple specified operating conditions, generate a cell internal resistance correction coefficient prediction model corresponding to the battery system, wherein the cell internal resistance correction coefficient prediction model is a model with the rate, initial state of charge and cell temperature as independent variables and the cell internal resistance correction coefficient as the dependent variable.

[0093] In step S31, for example, multiple specified discharge rates can be set to 1C, 2C, 3C, and 4C, and multiple specified initial states of charge can be set to 100%, 80%, and 60%, respectively; and by arranging and combining multiple specified discharge rates and multiple specified initial states of charge, multiple specified operating conditions can be obtained, which is not limited in the embodiments of the present disclosure.

[0094] In step S32, the above-mentioned embodiment of the present disclosure may be referred to. Figure 1 The method for determining the cell internal resistance correction coefficient is shown to determine the target cell internal resistance correction coefficient under each specified working condition. For example, the specified initial state of charge can be kept unchanged, and the above method can be repeated by changing the specified discharge rate C. Figure 1 Steps S11 to S15 are shown, thereby obtaining a series of curves of the cell internal resistance correction coefficient and the cell temperature under various discharge rates. For example, Figure 13The curves of the change of the internal resistance correction coefficient of the battery cell as the battery cell temperature changes at 1C, 2C, 3C, and 4C discharge rates are shown (i.e., correction coefficient -1C, correction coefficient -2C, correction coefficient -3C, and correction coefficient -4C in the figure); then, by changing the specified initial SOC, the above steps S11 to S15 are repeated to obtain a series of curves of the internal resistance correction coefficient of the battery cell and the battery cell temperature at different initial SOCs and different discharge rates C. For example, Figure 14 A schematic diagram showing a series of curves showing the change of the internal resistance correction factor of the battery cell with the battery cell temperature at different initial SOCs and different discharge rates.

[0095] In step S33, after obtaining the target battery cell internal resistance correction data under each specified operating condition in a plurality of specified operating conditions, that is, obtaining the battery cell internal resistance correction coefficient that changes with the battery cell temperature under different initial SOCs and different rates, the battery cell internal resistance correction coefficient that changes with the battery cell temperature under different initial SOCs and different rates can be used to fit a model with the rate, initial state of charge and battery cell temperature as independent variables and the battery cell internal resistance correction coefficient as the dependent variable, thereby obtaining a battery cell internal resistance correction coefficient prediction model. For example, the battery cell internal resistance correction coefficient prediction model can be expressed as β=f(SOC, C, Tcell), that is, the battery cell internal resistance correction coefficient prediction model is a function of the initial SOC, rate and battery cell temperature.

[0096] Based on the above steps S31 to S33, the embodiment of the present disclosure also provides a flow chart of the process of generating a prediction model for the internal resistance correction coefficient of the battery cell, which includes: step S01, obtaining the original internal resistance R of the battery cell, the specified discharge rate C of the cross-flow, and the initial SOC of the battery cell discharge; step S02, performing a cross-flow discharge test under the initial SOC and the specified discharge rate to obtain the measured temperature rise curve of the temperature sensing point at the highest temperature in the battery system; step S03, building a three-dimensional thermal simulation model of the battery system, setting the battery cell internal resistance correction coefficient β, performing simulation calibration with the measured temperature rise curve as the target, and obtaining the curve of the battery cell internal resistance correction coefficient β changing with the battery cell temperature T_cell. Line β-T_cell curve; Step S04 changes the constant current discharge rate C to C', and uses the above steps S01 to S03 to perform calibration to obtain the battery cell internal resistance correction coefficient β and the battery cell temperature T_cell change curves under multiple discharge rate conditions; Step S04, changes the initial SOC of the battery cell discharge, and repeats the above steps S02 to S05; Step S06, through the above calibration, obtains the battery cell internal resistance correction coefficient β and the battery cell temperature T_cell change curves under multiple initial SOCs and multiple discharge rate conditions; Step S07, generates a battery cell internal resistance correction coefficient prediction model β=f(SOC, C, Tcell) from the above curves. Furthermore, after obtaining the cell internal resistance correction coefficient prediction model, this model can be used to perform thermal simulation temperature prediction on the battery pack or other similar battery packs using the same cell model under any operating conditions. In other words, the cell internal resistance correction coefficient prediction model can be used to determine the cell internal resistance correction coefficient corresponding to the current cell temperature at any initial SOC and any power factor, and thus its heat generation power. For other battery systems using this cell model, the cell internal resistance correction coefficient prediction model can be used to predict the temperature rise of the battery system using this cell.

[0097] According to the method for generating a cell internal resistance correction coefficient prediction model in an embodiment of the present disclosure, by generating a cell internal resistance correction coefficient prediction model corresponding to the battery system, the cell internal resistance correction coefficient at any rate, any initial state of charge, and any cell temperature can be predicted.

[0098] Based on the above-mentioned method for generating a prediction model for the internal resistance correction coefficient of a battery cell, an embodiment of the present disclosure further provides a method for predicting the temperature rise of a battery system, comprising:

[0099] Step S41, using the cell internal resistance correction coefficient prediction model generation method, generating a cell internal resistance correction coefficient prediction model corresponding to the battery system; it should be understood that the implementation of step S41 can refer to the implementation of steps S31 to S32 in the above-mentioned embodiment of the present disclosure, and will not be repeated here;

[0100] Step S42, using the target three-dimensional thermal simulation model of the battery system and the cell internal resistance correction coefficient prediction model to simulate the discharge or charge process of the battery system under specified operating conditions, to obtain a temperature rise prediction result of the battery system;

[0101] Among them, when using the target three-dimensional thermal simulation model of the battery system to simulate the process of discharging or charging the battery system under specified working conditions according to the target cell internal resistance correction data, the cell internal resistance correction coefficient used in the target cell heat generation power and the target tab heat generation power assigned by the cell simulation module and the tab simulation module in the target three-dimensional thermal simulation model is determined based on the cell temperature of each cell and the cell internal resistance correction coefficient prediction model during the simulation process. For example, the above-mentioned cell internal resistance correction coefficient prediction model can be imported into the three-dimensional thermal simulation software. The simulation software can obtain the average body temperature (i.e., cell temperature) of each cell simulation module at the current moment during the three-dimensional simulation calculation process under the specified disclosure based on the target three-dimensional thermal simulation model; then, the cell internal resistance correction coefficient to be used for each cell can be determined from the prediction model based on the average body temperature of each cell at the current moment and the specified disclosure, and brought into the target cell heat generation power assigned by each cell simulation module in the target three-dimensional thermal simulation model, so that the target cell heat generation power of each cell is different and the target tab heat generation power connected to each cell is also different; for example, the battery There are a total of 10 battery cells in the system. During the simulation process, the average body temperature of the 10 battery cell simulation modules can be obtained at a certain moment. Based on the average body temperature of these 10 battery cell simulation modules, the simulation software queries the input prediction model to obtain the corresponding 10 battery cell internal resistance correction coefficients, and then applies them to the target battery cell heat generation power in the 10 battery cell simulation models respectively. Then, these 10 battery cells have different heat generation powers. At the same time, the target battery cell heat generation power of the tab simulation module connected to each battery cell simulation module is 5% of the target battery cell heat generation power of the battery cell simulation module to which it is connected, so that the heat generation power of the tab connected to each battery cell also depends on the average body temperature of each battery cell.

[0102] Therefore, the three-dimensional thermal simulation software can use its simulation computing capabilities to simulate the process of discharging or charging the battery system under any working conditions based on the above-mentioned prediction model and the target three-dimensional thermal simulation model, and obtain the cell temperature of each battery cell in the battery system that changes with time (such as the average body temperature of each battery cell that changes with time). The temperature rise prediction result of the battery system may include the cell temperature of each battery cell that changes with time, or may also include the battery temperature of the entire battery system that changes with time. This is not limited to the embodiments of the present disclosure.

[0103] According to the prediction method of the embodiment of the present disclosure, the cell internal resistance correction coefficient prediction model can be used to more accurately predict the temperature rise of the battery system during charging or discharging under any operating conditions.

[0104] The cell internal resistance correction coefficient determination method, prediction model generation method, and temperature rise estimation method according to the embodiments of the present disclosure do not require cell parameter testing and simulation, and are still applicable even when cell parameters are unavailable, thus having wider applicability. There is no need to perform one-dimensional simulation analysis, such as electrochemical simulation of the cell or equivalent circuit model simulation, to obtain the cell internal resistance or cell heating power, thereby greatly reducing simulation difficulty and testing costs and enhancing operability. The cell internal resistance correction coefficient is real-time and changes with temperature. Rather than being a single value, it can be corrected for the entire heat generation process. The cell internal resistance correction coefficient is temperature-dependent, allowing different heat generation powers to be determined for different cells, resulting in higher accuracy. The constructed three-dimensional thermal simulation model also considers tab heat generation and proposes a specific method for assigning tab heat generation power, resulting in higher accuracy. The cell internal resistance correction coefficient is corrected based on multiple operating conditions, and a prediction model for the correction coefficient can be obtained, which correlates the cell internal resistance correction coefficient with cell temperature, initial SOC, and current operating condition, i.e., rate, and can be used to predict battery system temperature rise under various operating conditions.

[0105] Figure 15 A block diagram of a device for determining a cell internal resistance correction coefficient in a battery system according to an embodiment of the present disclosure is shown. Figure 15 As shown, the device includes:

[0106] A first simulation module 151 is configured to simulate a discharge or charge process of the battery system under specified operating conditions using an initial three-dimensional thermal simulation model of the battery system, and obtain simulated temperature rise data of a specified temperature-sensing point on a specified battery cell in the battery system; wherein the simulated temperature rise data includes a simulated temperature of the specified temperature-sensing point changing over time; wherein the battery cell simulation module in the initial three-dimensional thermal simulation model is assigned an original battery cell heat generation power;

[0107] an acquisition module 152, configured to acquire measured temperature rise data of a specified temperature sensing point on the specified battery cell in the battery system, wherein the measured temperature rise data includes the measured temperature of the specified temperature sensing point as it changes over time during discharge or charging of the battery system under the specified operating conditions;

[0108] A first determining module 153 is configured to determine initial cell internal resistance correction data based on the measured temperature rise data and the simulated temperature rise data, wherein the initial cell internal resistance correction data includes a cell internal resistance correction coefficient that varies with time;

[0109] A second simulation module 154 is configured to use a target three-dimensional thermal simulation model of the battery system to simulate a discharge or charge process of the battery system under the specified operating conditions based on the initial cell internal resistance correction data, and obtain a cell temperature of the entire specified cell that changes over time; wherein the cell simulation module in the target three-dimensional thermal simulation model is assigned a target cell heat generation power corrected by the cell internal resistance correction coefficient;

[0110] The second determining module 155 determines target cell internal resistance correction data according to the cell temperature of the entire designated cell that changes with time and the initial cell internal resistance correction data. The target cell internal resistance correction data includes a cell internal resistance correction coefficient that changes with cell temperature.

[0111] In one possible implementation, the initial battery cell internal resistance correction data is determined based on the measured temperature rise data and the simulated temperature rise data, including: determining the target simulated temperature difference corresponding to the designated temperature sensing point based on the simulated temperature at the nth moment in the simulated temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data; determining the measured temperature difference corresponding to the designated temperature sensing point based on the measured temperature at the nth moment in the measured temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data; and determining the ratio between the target simulated temperature difference and the measured temperature difference as the battery cell internal resistance correction coefficient from the nth moment to the n+1th moment.

[0112] In one possible implementation, the specified operating conditions include: a specified initial state of charge and a specified rate; the original battery cell heat generation power is determined based on the battery cell capacity of the battery cell in the battery system, the original internal resistance of the battery cell and the specified rate; the target battery cell heat generation power is determined based on the battery cell capacity of the battery cell in the battery system, the original internal resistance of the battery cell, the specified rate and the battery cell internal resistance correction coefficient.

[0113] In one possible implementation, the battery system includes multiple battery cells, and the designated battery cell includes the battery cell located in the middle position among the multiple battery cells; each battery cell in the battery system is connected to a tab, and the tab simulation module in the initial three-dimensional thermal simulation model is also assigned an original tab heat generation power, and the original tab heat generation power is a specified proportion of the corresponding original battery cell heat generation power; the tab simulation module in the target three-dimensional thermal simulation model is also assigned a target tab heat generation power, and the target tab heat generation power is a specified proportion of the corresponding target battery cell heat generation power.

[0114] The present disclosure also provides a battery system temperature rise prediction device, including:

[0115] a target determination module, configured to determine target cell internal resistance correction data of the battery system under specified operating conditions using the cell internal resistance correction coefficient determination device;

[0116] A prediction module is used to simulate the process of discharging or charging the battery system under the specified operating conditions using the target three-dimensional thermal simulation model of the battery system according to the target battery cell internal resistance correction data, so as to obtain a temperature rise prediction result of the battery system.

[0117] The present disclosure also provides a device for generating a prediction model for a cell internal resistance correction coefficient, comprising:

[0118] A working condition acquisition module, configured to acquire a plurality of specified working conditions of the battery system, wherein at least one of the specified rate and the specified initial state of charge in different specified working conditions is different;

[0119] a data determination module, configured to determine target cell internal resistance correction data under each of the plurality of specified operating conditions using the cell internal resistance correction coefficient determination device;

[0120] A generation module is used to generate a cell internal resistance correction coefficient prediction model corresponding to the battery system based on the target cell internal resistance correction data under each of the multiple specified operating conditions, wherein the cell internal resistance correction coefficient prediction model is a model with the rate, initial state of charge and cell temperature as independent variables and the cell internal resistance correction coefficient as the dependent variable.

[0121] The present disclosure also provides a battery system temperature rise prediction device, including:

[0122] A model generation module, configured to generate a cell internal resistance correction coefficient prediction model corresponding to the battery system using the cell internal resistance correction coefficient prediction model generation device;

[0123] The temperature rise prediction module is used to use the target three-dimensional thermal simulation model of the battery system according to the battery cell internal resistance correction coefficient prediction model to simulate the process of discharging or charging the battery system under specified working conditions to obtain a temperature rise prediction result of the battery system.

[0124] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0125] An embodiment of the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0126] An embodiment of the present disclosure further provides a non-volatile computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0127] An embodiment of the present disclosure further provides a computer program product, including a computer program, or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0128] Figure 16 FIG1 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 can be provided as a server or a terminal device. Figure 16 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described method.

[0129] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server 2003. TM , Mac OS X TM , Unix TM ,Linux TM , FreeBSD TM or similar.

[0130] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0131] A computer-readable storage medium can be a tangible device that can hold and store programs / instructions used by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0132] The computer programs (or computer-readable program instructions) described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0133] The computer program (or computer program instructions) for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, by utilizing state information of computer-readable program instructions to personalize and customize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0134] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0135] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0136] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0137] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0138] While various embodiments of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for determining a cell internal resistance correction coefficient in a battery system, characterized in that: The method comprises: The initial three-dimensional thermal simulation model of the battery system is used to simulate a discharge or charge process of the battery system under specified operating conditions to obtain simulated temperature rise data of a specified temperature-sensing point on a specified battery cell in the battery system; wherein the simulated temperature rise data includes a simulated temperature of the specified temperature-sensing point changing over time; wherein the battery cell simulation module in the initial three-dimensional thermal simulation model is assigned an original battery cell heat generation power; Obtaining measured temperature rise data of a specified temperature-sensing point on the specified battery cell in the battery system, wherein the measured temperature rise data includes the measured temperature of the specified temperature-sensing point as a function of time during discharge or charge of the battery system under the specified operating conditions; Determining initial cell internal resistance correction data according to the measured temperature rise data and the simulated temperature rise data, wherein the initial cell internal resistance correction data includes a cell internal resistance correction coefficient that changes with time; Using the target three-dimensional thermal simulation model of the battery system and the initial cell internal resistance correction data, simulating the process of discharging or charging the battery system under the specified operating conditions to obtain the cell temperature of the entire specified cell that changes over time; wherein the cell simulation module in the target three-dimensional thermal simulation model is assigned a target cell heat generation power corrected by the cell internal resistance correction coefficient; Target cell internal resistance correction data is determined according to the cell temperature of the entire designated cell that changes with time and the initial cell internal resistance correction data. The target cell internal resistance correction data includes a cell internal resistance correction coefficient that changes with the cell temperature.

2. The method according to claim 1, characterized in that The determining of initial cell internal resistance correction data according to the measured temperature rise data and the simulated temperature rise data includes: Determining a target simulated temperature difference corresponding to the designated temperature sensing point based on the simulated temperature at the nth moment in the simulated temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data; Determining the measured temperature difference corresponding to the designated temperature sensing point based on the measured temperature at the nth moment in the measured temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data; The ratio of the target simulated temperature difference to the measured temperature difference is determined as the cell internal resistance correction coefficient from the nth moment to the n+1th moment.

3. The method according to claim 1, characterized in that The specified operating conditions include: specifying an initial state of charge and a specified rate; The original cell heat generation power is determined based on the cell capacity of the cell in the battery system, the original internal resistance of the cell, and the specified rate; The target cell heat generation power is determined based on the cell capacity of the cell in the battery system, the original internal resistance of the cell, the specified rate, and the cell internal resistance correction coefficient.

4. The method according to any one of claims 1 to 3, characterized in that The battery system includes a plurality of battery cells, and the designated battery cell includes a battery cell located in the middle of the plurality of battery cells; Each battery cell in the battery system is connected to a tab, and the tab simulation module in the initial three-dimensional thermal simulation model is further assigned an original tab heat generation power, where the original tab heat generation power is a specified proportion of the corresponding original battery cell heat generation power; The tab simulation module in the target three-dimensional thermal simulation model is also assigned a target tab heat generation power, and the target tab heat generation power is a specified proportion of the corresponding target battery cell heat generation power.

5. A method for predicting temperature rise of a battery system, characterized in that: include: Determine the target cell internal resistance correction data of the battery system under specified operating conditions using the cell internal resistance correction coefficient determination method according to any one of claims 1 to 4; The target three-dimensional thermal simulation model of the battery system is used to simulate the discharge or charge process of the battery system under the specified working condition according to the target cell internal resistance correction data to obtain a temperature rise prediction result of the battery system.

6. A method for generating a prediction model for a cell internal resistance correction coefficient, characterized in that: include: Acquire multiple specified operating conditions of the battery system, wherein at least one of the specified rate and the specified initial state of charge in different specified operating conditions is different; Determine the target cell internal resistance correction data under each of the multiple specified operating conditions using the method for determining the cell internal resistance correction coefficient according to any one of claims 1 to 4; Based on the target battery cell internal resistance correction data under each of the multiple specified operating conditions, a battery cell internal resistance correction coefficient prediction model corresponding to the battery system is generated. The battery cell internal resistance correction coefficient prediction model is a model with the rate, initial state of charge and battery cell temperature as independent variables and the battery cell internal resistance correction coefficient as the dependent variable.

7. A method for predicting temperature rise of a battery system, characterized in that: include: Generate a cell internal resistance correction coefficient prediction model corresponding to the battery system using the cell internal resistance correction coefficient prediction model generation method according to claim 6; The target three-dimensional thermal simulation model of the battery system is used to simulate the discharge or charging process of the battery system under specified working conditions according to the battery cell internal resistance correction coefficient prediction model to obtain a temperature rise prediction result of the battery system.

8. A device for determining a correction coefficient of internal resistance of a battery cell in a battery system, characterized in that: The device comprises: a first simulation module, configured to simulate a discharge or charge process of the battery system under specified operating conditions using an initial three-dimensional thermal simulation model of the battery system, and obtain simulated temperature rise data of a specified temperature-sensing point on a specified battery cell in the battery system; wherein the simulated temperature rise data includes a simulated temperature of the specified temperature-sensing point changing over time; wherein the battery cell simulation module in the initial three-dimensional thermal simulation model is assigned an original battery cell heat generation power; an acquisition module, configured to acquire measured temperature rise data of a specified temperature-sensing point on the specified battery cell in the battery system, wherein the measured temperature rise data includes the measured temperature of the specified temperature-sensing point as a function of time during discharge or charge of the battery system under the specified operating conditions; A first determining module is configured to determine initial cell internal resistance correction data based on the measured temperature rise data and the simulated temperature rise data, wherein the initial cell internal resistance correction data includes a cell internal resistance correction coefficient that varies with time; a second simulation module, configured to simulate a discharge or charge process of the battery system under the specified operating condition using a target three-dimensional thermal simulation model of the battery system and based on the initial cell internal resistance correction data, to obtain a cell temperature of the entire specified cell that changes over time; wherein the cell simulation module in the target three-dimensional thermal simulation model is assigned a target cell heat generation power corrected by the cell internal resistance correction coefficient; The second determining module determines target cell internal resistance correction data according to the cell temperature of the entire designated cell that changes with time and the initial cell internal resistance correction data, wherein the target cell internal resistance correction data includes a cell internal resistance correction coefficient that changes with the cell temperature.

9. The device according to claim 8, characterized in that The determining of initial cell internal resistance correction data according to the measured temperature rise data and the simulated temperature rise data includes: Determining a target simulated temperature difference corresponding to the designated temperature sensing point based on the simulated temperature at the nth moment in the simulated temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data; Determining the measured temperature difference corresponding to the designated temperature sensing point based on the measured temperature at the nth moment in the measured temperature rise data and the measured temperature at the n+1th moment in the measured temperature rise data; The ratio of the target simulated temperature difference to the measured temperature difference is determined as the cell internal resistance correction coefficient from the nth moment to the n+1th moment.

10. The device according to claim 8, characterized in that The specified operating conditions include: specifying an initial state of charge and a specified rate; The original cell heat generation power is determined based on the cell capacity of the cell in the battery system, the original internal resistance of the cell, and the specified rate; The target cell heat generation power is determined based on the cell capacity of the cell in the battery system, the original internal resistance of the cell, the specified rate, and the cell internal resistance correction coefficient.

11. The device according to any one of claims 8 to 10, characterized in that The battery system includes a plurality of battery cells, and the designated battery cell includes a battery cell located in the middle of the plurality of battery cells; Each battery cell in the battery system is connected to a tab, and the tab simulation module in the initial three-dimensional thermal simulation model is further assigned an original tab heat generation power, where the original tab heat generation power is a specified proportion of the corresponding original battery cell heat generation power; The tab simulation module in the target three-dimensional thermal simulation model is also assigned a target tab heat generation power, and the target tab heat generation power is a specified proportion of the corresponding target battery cell heat generation power.

12. A battery system temperature rise prediction device, characterized in that: include: a target determination module, configured to determine target cell internal resistance correction data of the battery system under specified operating conditions using the cell internal resistance correction coefficient determination device according to any one of claims 8 to 10; A prediction module is used to simulate the process of discharging or charging the battery system under the specified operating conditions using the target three-dimensional thermal simulation model of the battery system according to the target battery cell internal resistance correction data, so as to obtain a temperature rise prediction result of the battery system.

13. A device for generating a prediction model for a battery cell internal resistance correction coefficient, characterized in that: include: A working condition acquisition module, configured to acquire a plurality of specified working conditions of the battery system, wherein at least one of the specified rate and the specified initial state of charge in different specified working conditions is different; a data determination module, configured to determine target cell internal resistance correction data under each of the plurality of specified operating conditions using the cell internal resistance correction coefficient determination device according to any one of claims 8 to 10; A generation module is used to generate a cell internal resistance correction coefficient prediction model corresponding to the battery system based on the target cell internal resistance correction data under each of the multiple specified operating conditions, wherein the cell internal resistance correction coefficient prediction model is a model with the rate, initial state of charge and cell temperature as independent variables and the cell internal resistance correction coefficient as the dependent variable.

14. A battery system temperature rise prediction device, characterized in that: include: a model generation module, configured to generate a cell internal resistance correction coefficient prediction model corresponding to the battery system using the cell internal resistance correction coefficient prediction model generation device according to claim 13; The temperature rise prediction module is used to use the target three-dimensional thermal simulation model of the battery system according to the battery cell internal resistance correction coefficient prediction model to simulate the process of discharging or charging the battery system under specified working conditions to obtain a temperature rise prediction result of the battery system.

15. An electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

16. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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