Lithium battery temperature estimation method and system, and computer program product

Abstract

The invention belongs to a temperature monitoring method, and provides a lithium battery temperature estimation method, a lithium battery temperature estimation system and a computer program product in order to solve the technical problem that when a thermal model of a lithium battery is built at present, parameters of a material attribute category and a thermal attribute category in a dependent geometric model are difficult to obtain, and consequently the thermal model is difficult to build. After working parameters of the lithium battery are obtained, the working parameters are input into the lumped heat parameter model, the temperature change situation in the charging or discharging process of the lithium battery is obtained, and when the heat production behavior of the lithium battery is considered in the lumped heat parameter model, the reversible heat production behavior and the irreversible heat production behavior are considered at the same time; the optimal value of the thermal performance of the lithium battery in the lumped thermal parameter model can be determined through a genetic algorithm. According to the estimation method, the change condition of the temperature of the lithium battery can be accurately estimated only by acquiring the working parameters of the lithium battery.

Description

Technical Field

[0001] The present application relates to a temperature monitoring method, and specifically to a lithium battery temperature estimation method, system, and computer program product. Background Art

[0002] When lithium batteries are used in new energy vehicles and energy storage systems, in order to prevent thermal runaway and fire incidents, their internal temperature needs to be accurately estimated. Therefore, it is necessary to build a thermal model that can simulate the heat generation and temperature changes of lithium batteries.

[0003] Traditional thermal models have many parameters and rely on the geometric model of lithium batteries. However, it is difficult to obtain the parameters of material property categories and thermal property categories in the geometric model, which makes it difficult to establish a thermal model. Summary of the invention

[0004] This application aims to address the technical problem that when constructing a thermal model of a lithium battery, the parameters of the material property categories and thermal property categories in the geometric model that is relied on are difficult to obtain, resulting in difficulty in establishing the thermal model. The application provides a lithium battery temperature estimation method, system, and computer program product.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application proposes a lithium battery temperature estimation method, comprising: Get the working parameters of lithium battery; Input the working parameters of the lithium battery into the lumped thermal parameter model to obtain the temperature change of the lithium battery during charging or discharging; Among them, the lumped thermal parameter model is used to simulate the relationship between the change of lithium battery temperature over time and the heat generation behavior of the lithium battery, wherein the heat generation behavior of the lithium battery includes reversible heat generation behavior and irreversible heat generation behavior; the lumped thermal parameter model includes lithium battery thermal performance parameters, and the optimal values ​​of the lithium battery thermal performance parameters are determined by genetic algorithms.

[0006] Furthermore, the operating parameters of the lithium battery include the current, voltage and ambient temperature during operation of the lithium battery.

[0007] Furthermore, the lumped thermal parameter model comprises:

[0008] in, Indicates the lithium battery temperature. represents thermal impedance, represents the heat capacity, Indicates the current ambient temperature. represents the sum of the reversible heat production rate and the irreversible heat production rate, Indicates the system running time.

[0009] Furthermore, the reversible heat generation rate includes:

[0010] in, represents the reversible heat generation rate, represents the current, Indicates the positive open circuit voltage of the lithium battery. Indicates the open circuit voltage of the negative electrode of the lithium battery. It represents the positive electrode entropy increase fitting curve of lithium battery, It represents the negative electrode entropy increase fitting curve of lithium battery. It represents the percentage of the average lithium ion concentration of the positive electrode to the maximum lithium ion concentration of the positive electrode. It represents the percentage of the average lithium ion concentration of the negative electrode to the maximum lithium ion concentration of the negative electrode. Indicates the reference temperature of lithium battery as the positive and negative open circuit voltages change; The irreversible heat generation rate includes:

[0011] in, is the irreversible heat generation rate, Indicates the open circuit voltage of lithium battery. Indicates terminal voltage.

[0012] Furthermore, when determining the optimal value of the thermal performance parameter of the lithium battery by genetic algorithm, the fitness function used includes:

[0013] in, represents the fitness value, Indicates the number of sampling points, express t The actual lithium battery temperature collected at all times, express t The temperature of the lithium battery cell calculated by the lumped thermal parameter model at all times, Represents the estimated value of the heat generation parameter of the lithium battery.

[0014] In a second aspect, the present application proposes a lithium battery temperature estimation system, comprising: A parameter acquisition module is used to obtain the working parameters of the lithium battery; An estimation module is used to input the operating parameters of the lithium battery into the lumped thermal parameter model to obtain the temperature change of the lithium battery during charging or discharging; Among them, the lumped thermal parameter model is used to simulate the relationship between the change of lithium battery temperature over time and the heat generation behavior of the lithium battery, wherein the heat generation behavior of the lithium battery includes reversible heat generation behavior and irreversible heat generation behavior; the lumped thermal parameter model includes lithium battery thermal performance parameters, and the optimal values ​​of the lithium battery thermal performance parameters are determined by genetic algorithms.

[0015] Furthermore, the lumped thermal parameter model comprises:

[0016] in, Indicates the lithium battery temperature. represents thermal impedance, represents the heat capacity, Indicates the current ambient temperature. represents the sum of the reversible heat production rate and the irreversible heat production rate, Indicates the system running time.

[0017] Furthermore, the reversible heat generation rate includes:

[0018] in, represents the reversible heat generation rate, represents the current, Indicates the positive open circuit voltage of the lithium battery. Indicates the open circuit voltage of the negative electrode of the lithium battery. It represents the positive electrode entropy increase fitting curve of lithium battery, It represents the negative electrode entropy increase fitting curve of lithium battery. It represents the percentage of the average lithium ion concentration of the positive electrode to the maximum lithium ion concentration of the positive electrode. It represents the percentage of the average lithium ion concentration of the negative electrode to the maximum lithium ion concentration of the negative electrode. Indicates the reference temperature of lithium battery as the positive and negative open circuit voltages change; The irreversible heat generation rate includes:

[0019] in, is the irreversible heat generation rate, Indicates the open circuit voltage of lithium battery. Indicates terminal voltage.

[0020] Furthermore, when determining the optimal value of the thermal performance parameter of the lithium battery by genetic algorithm, the fitness function used includes:

[0021] in, represents the fitness function, Indicates the number of sampling points, express t The actual lithium battery temperature collected at all times, express t The temperature of the lithium battery cell calculated by the lumped thermal parameter model at all times, Represents the estimated value of the heat generation parameter of the lithium battery.

[0022] In a third aspect, the present application proposes a computer program product, which includes instructions, and when the instructions are executed by a processor, the above-mentioned lithium battery temperature estimation method is implemented.

[0023] Compared with the prior art, this application has the following beneficial effects: The present application proposes a method for estimating the temperature of a lithium battery. After obtaining the working parameters of the lithium battery, they are input into a lumped thermal parameter model to obtain the temperature change during the charging or discharging process of the lithium battery. When the lumped thermal parameter model considers the heat generation behavior of the lithium battery, it also considers the reversible heat generation behavior and the irreversible heat generation behavior. In addition, when determining the optimal value of the thermal performance of the lithium battery in the lumped thermal parameter model, it can be determined by a genetic algorithm. The estimation method of the present application only needs to obtain the working parameters of the lithium battery to accurately estimate the temperature change of the lithium battery. After actual test verification, the estimation result has high accuracy and the error is less than 1°C. In addition, the estimation method of the present application has high computational efficiency, which saves the current cost of collecting the temperature of the lithium battery cell through a temperature sensor.

[0024] The present application also proposes a lithium battery temperature estimation and a computer program product, which have all the advantages of the above-mentioned lithium battery temperature estimation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A schematic diagram of a lithium battery temperature estimation method of the present application; Figure 2 Another schematic diagram of the lithium battery temperature estimation method of the present application; Figure 3 A schematic diagram of a lithium battery temperature estimation system of the present application; Figure 4 It is a schematic diagram of the genetic algorithm parameter iteration process; Figure 5Schematic diagram of actual verification of temperature estimation using the estimated optimal parameter results. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0028] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0029] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0030] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invented product is usually placed when used. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0031] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0032] In the description of the embodiments of the present application, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0033] Lithium-ion batteries are widely used in new energy vehicles and energy storage systems due to their long service life, safety, and low material cost. However, the safe use of lithium batteries is an important issue. In order to prevent thermal runaway and fire in lithium batteries, their internal temperature must be accurately estimated. Build a model that can simulate the heat generation and temperature changes of lithium batteries.

[0034] However, classic thermal models usually contain a large number of parameters, which may involve multiple aspects such as the material properties, thermal properties, and geometric dimensions of lithium batteries. For example, the thermal conductivity, specific heat capacity, density, internal resistance and other parameters of lithium batteries are all key factors affecting the heat generation and temperature distribution of lithium batteries. The acquisition of these parameters usually requires a large number of experiments and tests, and the parameter values ​​of lithium batteries of different models may also vary. In addition, the establishment of thermal models usually needs to be based on the geometric model of lithium batteries. However, the geometric structure of lithium batteries is often complex, containing multiple levels and components (such as positive electrodes, negative electrodes, separators, current collectors, etc.), and the materials and sizes of these components may also vary. This complexity makes it difficult to establish geometric models, and also increases the complexity and time cost of thermal model calculations.

[0035] Based on the above situation, the present application proposes a lithium battery temperature estimation method, system, and computer program product. The present application is described in detail below in conjunction with embodiments and drawings.

[0036] like Figure 1 As shown, it is a schematic diagram of a lithium battery temperature estimation method of the present application, which may include: S101, obtaining the operating parameters of the lithium battery.

[0037] In practical applications, the specific working parameters of lithium batteries to be obtained are determined according to the specific form of the lumped thermal parameter model. These working parameters can usually include the voltage, current, capacity, internal resistance, charge and discharge rate, and working environment temperature of the lithium battery. Among them, voltage and current are basic indicators reflecting the immediate working status of the lithium battery, while capacity is directly related to the remaining power and endurance of the lithium battery. Changes in internal resistance can reflect the aging degree and health of the lithium battery. The charge and discharge rate determines the speed of charging and discharging of the lithium battery, and is particularly important for the temperature management of the lithium battery. The working environment temperature is an external factor that affects the performance and safety of the lithium battery, and it also needs to be accurately recorded. The acquisition of these parameters usually relies on high-precision sensors and real-time monitoring of the lithium battery management system.

[0038] S102, inputting the operating parameters of the lithium battery into a lumped thermal parameter model to obtain temperature changes during the charging or discharging process of the lithium battery.

[0039] The lumped thermal parameter model is a simplified thermodynamic model used to predict the temperature change of lithium batteries during the charging and discharging process. The model simplifies the complex heat transfer process into a series of easily calculable parameters and equations by comprehensively considering the heat generation behavior and heat dissipation mechanism of lithium batteries.

[0040] Among them, the lumped thermal parameter model is used to simulate the relationship between the change of lithium battery temperature over time and the heat generation behavior of the lithium battery, wherein the heat generation behavior of the lithium battery includes reversible heat generation behavior and irreversible heat generation behavior; the lumped thermal parameter model includes lithium battery thermal performance parameters, and the optimal values ​​of the lithium battery thermal performance parameters are determined by genetic algorithms.

[0041] It should be noted that reversible heat generation behavior mainly refers to the heat generated by the reversibility of chemical reactions during the charging and discharging process of lithium batteries. This part of heat is closely related to the charging and discharging efficiency of lithium batteries. Irreversible heat generation behavior includes ohmic heat, polarization heat, etc., which are mainly caused by the resistance inside the lithium battery and the limitation of chemical reaction kinetics, and are inevitable energy losses.

[0042] In addition, in the lumped thermal parameter model, the accurate setting of the thermal performance parameters of lithium batteries is crucial to the accuracy of model prediction. These parameters may include the heat capacity, thermal conductivity, surface heat dissipation coefficient, etc. of lithium batteries, which together determine the ability of lithium batteries to absorb, conduct and dissipate heat. In order to obtain the optimal values ​​of these parameters, genetic algorithms are introduced as an efficient optimization method. Genetic algorithms simulate natural selection and genetic mechanisms to search for the optimal solution in the parameter space, so that the temperature changes predicted by the model are most consistent with the experimental data, thereby improving the prediction accuracy and reliability of the model.

[0043] This application can accurately predict the temperature changes of lithium batteries during charging or discharging by accurately acquiring the operating parameters of lithium batteries and combining the lumped thermal parameter model and the thermal performance parameters of lithium batteries optimized by genetic algorithms, providing strong support for the thermal management, safety monitoring and performance optimization of lithium batteries.

[0044] like Figure 2 As shown, another schematic diagram of the lithium battery temperature estimation method of the present application may include: S201, construct a lumped thermal parameter model.

[0045] In this embodiment, the lumped thermal parameter model is established based on the laws of thermodynamics and characteristic thermal parameters, and the heat generation behavior of the lithium battery is defined as reversible heat generation behavior and irreversible heat generation behavior.

[0046] The irreversible heat generation rate, which characterizes the irreversible heat generation behavior, is defined as , the specific calculation equation is expressed as:

[0047] in, is the irreversible heat generation rate, represents the current, Indicates the open circuit voltage of lithium battery. Indicates terminal voltage.

[0048] The reversible heat generation rate, which characterizes the reversible heat generation behavior, is defined as , the specific calculation equation is expressed as:

[0049] in, represents the reversible heat generation rate, Indicates the positive open circuit voltage of the lithium battery. Indicates the open circuit voltage of the negative electrode of the lithium battery. It represents the positive electrode entropy increase fitting curve of lithium battery, It represents the negative electrode entropy increase fitting curve of lithium battery. It represents the percentage of the average lithium ion concentration of the positive electrode to the maximum lithium ion concentration of the positive electrode. It represents the percentage of the average lithium ion concentration of the negative electrode to the maximum lithium ion concentration of the negative electrode. Indicates the reference temperature of a lithium battery as the open circuit voltage of the positive and negative electrodes changes.

[0050] The sum of the reversible heat production rate and the irreversible heat production rate is for:

[0051] The heat generation and temperature change law of lithium batteries can be expressed by the following equation as a lumped thermal parameter model:

[0052] in, Indicates the lithium battery temperature. represents thermal impedance, represents the heat capacity, Indicates the current ambient temperature. represents the sum of the reversible heat production rate and the irreversible heat production rate, Indicates the system running time.

[0053] In this embodiment, the thermal performance parameters of the lithium battery include heat capacity and thermal impedance, and the operating parameters of the lithium battery include current, voltage and ambient temperature.

[0054] Different from the traditional complex and difficult to calculate thermal model formulas, the lumped thermal parameter model of the present application uses a simple formula form to efficiently describe the heat generation behavior of lithium batteries.

[0055] S202, collecting the working parameters of the lithium battery.

[0056] A lithium battery intelligent management chip with an integrated temperature sensor is used to collect data on the working parameters of the lithium battery, including the current, voltage and ambient temperature of the lithium battery during operation.

[0057] It should be noted that the lithium battery management chip is an integrated circuit that is mainly used to monitor, control and protect lithium batteries. Lithium battery management chips usually integrate modules such as microcontrollers, voltage monitoring circuits, temperature monitoring circuits, current measurement circuits and protection circuits, and can play a key role in lithium battery packs to ensure the safety and stable performance of lithium batteries. Among them, the temperature monitoring circuit uses a temperature sensor to monitor the temperature of the lithium battery pack and single lithium battery in real time, which is also an important means to prevent safety problems caused by overheating or overcooling of lithium batteries.

[0058] S203, performing parameter identification on the thermal performance parameters of the lithium battery in the lumped thermal parameter model by using a genetic algorithm.

[0059] In the modeling process of the lumped thermal parameter model, the two thermal performance parameters of lithium batteries, heat capacity and thermal impedance, are unknown. Among them, thermal impedance refers to the resistance encountered by heat on the heat flow path, reflecting the heat transfer capacity of the medium or between media, and is used to describe the difficulty of heat transfer. Heat capacity refers to the heat required when an object absorbs or releases heat, reflecting the ability of the object to store heat.

[0060] This application proposes to use a genetic algorithm to identify these two parameters. The fitness function used is:

[0061] in, represents the fitness value, Indicates the number of sampling points, express t The actual lithium battery temperature collected at all times, express t The temperature of the lithium battery cell calculated by the lumped thermal parameter model at all times, Represents the estimated value of the heat generation parameter of the lithium battery.

[0062] It should be noted that the basic steps of genetic algorithm include: (1) Initialize the population: Randomly generate a set of possible solutions (i.e., parameter values ​​to be optimized). Each solution is called an individual, and all individuals constitute the population.

[0063] (2) Evaluate fitness: Use the fitness function to evaluate the quality of each individual. The fitness function is defined according to the specific requirements of the problem and reflects the degree of closeness between each solution and the ideal solution.

[0064] (3) Selection: Select excellent individuals as parents based on their fitness values ​​to generate the next generation.

[0065] (4) Crossover (or hybridization): Pair the selected parent individuals and generate offspring individuals through a crossover operation.

[0066] (5) Mutation: Make small-probability random mutations on offspring individuals to increase the diversity of the population.

[0067] (6) Iteration: Repeat steps (2) to (5) until the stopping condition is met (such as reaching the maximum number of iterations or the fitness value converges).

[0068] (7) Output the optimal solution: Select the individual with the highest fitness value from the final population as the optimal solution.

[0069] Obtain the optimal thermal impedance by minimizing the error with experimental data and heat capacity value.

[0070] like Figure 4 The following is a schematic diagram of the genetic algorithm parameter iteration process. Figure 4 It can be seen that the fitness value The lower the convergence, the better, indicating thermal impedance and heat capacity The better the estimation accuracy.

[0071] S204, temperature estimation.

[0072] After obtaining the thermal performance parameters of the lithium battery, the accuracy of the lumped thermal parameter model is guaranteed, and the temperature changes during the charging and discharging process of the lithium battery can be basically simulated.

[0073] like Figure 5 As shown, it is a schematic diagram of actual verification of temperature estimation using the estimated optimal parameter results. Figure 5 The comparison diagram of the temperature estimation results under the conditions of 1x discharge rate and 2x discharge rate is shown in the figure. The red circle represents the estimated temperature obtained by using the lumped thermal parameter model of this application, and the blue line represents the actual temperature measured experimentally under the same conditions. The vertical axis represents the unit of K Temperature value T .

[0074] The present application provides a lithium battery parameter identification method based on a lumped thermal parameter model, and uses the model to quantitatively analyze the heating behavior of the lithium battery during charging and discharging to achieve temperature estimation.

[0075] like Figure 3 As shown, it is a schematic diagram of a lithium battery temperature estimation system of the present application, which may include: A parameter acquisition module is used to obtain the working parameters of the lithium battery; An estimation module is used to input the operating parameters of the lithium battery into the lumped thermal parameter model to obtain the temperature change of the lithium battery during charging or discharging; Among them, the lumped thermal parameter model is used to simulate the relationship between the change of lithium battery temperature over time and the heat generation behavior of the lithium battery, wherein the heat generation behavior of the lithium battery includes reversible heat generation behavior and irreversible heat generation behavior; the lumped thermal parameter model includes lithium battery thermal performance parameters, and the optimal values ​​of the lithium battery thermal performance parameters are determined by genetic algorithms.

[0076] It should be noted that in the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of each module is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another device, or some features can be ignored or not executed. The module described as a separate component may or may not be physically separated. The component displayed as a module may be a physical unit or multiple physical units, that is, it may be located in one place, or it may be distributed in multiple different places. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0077] In addition, each module in each embodiment of the present invention may be integrated into a processing unit, each module may exist physically separately, or two or more modules may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0078] The present application also proposes a computer program product, which includes instructions. When the instructions are executed by a processor, the steps in the above-mentioned lithium battery temperature estimation method embodiment are implemented.

[0079] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in a memory and executed by the processor to accomplish the present invention.

[0080] The carrier for implementing the above-mentioned computer program product may be a computer device, or the computer program product may be stored in a computer storage medium.

[0081] The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The computer device may include, but is not limited to, a processor and a memory.

[0082] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0083] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0084] If the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0085] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A lithium battery temperature estimation method, characterized in that: include: Get the working parameters of lithium battery; Input the working parameters of the lithium battery into the lumped thermal parameter model to obtain the temperature change of the lithium battery during charging or discharging; Among them, the lumped thermal parameter model is used to simulate the relationship between the change of lithium battery temperature over time and the heat generation behavior of the lithium battery, wherein the heat generation behavior of the lithium battery includes reversible heat generation behavior and irreversible heat generation behavior; the lumped thermal parameter model includes lithium battery thermal performance parameters, and the optimal values ​​of the lithium battery thermal performance parameters are determined by genetic algorithms.

2. A lithium battery temperature estimation method according to claim 1, characterized in that: The operating parameters of the lithium battery include the current, voltage and ambient temperature of the lithium battery during operation.

3. A lithium battery temperature estimation method according to claim 2, characterized in that: The lumped thermal parameter model comprises: in, Indicates the lithium battery temperature. represents thermal impedance, represents the heat capacity, Indicates the current ambient temperature. represents the sum of the reversible heat production rate and the irreversible heat production rate, Indicates the system running time.

4. A lithium battery temperature estimation method according to claim 3, characterized in that: The reversible heat generation rate comprises: in, represents the reversible heat generation rate, represents the current, Indicates the positive open circuit voltage of the lithium battery. Indicates the open circuit voltage of the negative electrode of the lithium battery. It represents the positive electrode entropy increase fitting curve of lithium battery, It represents the negative electrode entropy increase fitting curve of lithium battery. It represents the percentage of the average lithium ion concentration of the positive electrode to the maximum lithium ion concentration of the positive electrode. It represents the percentage of the average lithium ion concentration of the negative electrode to the maximum lithium ion concentration of the negative electrode. Indicates the reference temperature of lithium battery as the positive and negative open circuit voltages change; The irreversible heat generation rate includes: in, is the irreversible heat generation rate, Indicates the open circuit voltage of lithium battery, Indicates terminal voltage.

5. A lithium battery temperature estimation method according to claim 3, characterized in that: When determining the optimal value of thermal performance parameters of lithium batteries through genetic algorithms, the fitness function used includes: in, represents the fitness value, Indicates the number of sampling points, express t The actual lithium battery temperature collected at all times, express t The temperature of the lithium battery cell calculated by the lumped thermal parameter model at all times, Represents the estimated value of the heat generation parameter of the lithium battery.

6. A lithium battery temperature estimation system, characterized in that: include: A parameter acquisition module is used to obtain the working parameters of the lithium battery; An estimation module is used to input the operating parameters of the lithium battery into the lumped thermal parameter model to obtain the temperature change of the lithium battery during charging or discharging; Among them, the lumped thermal parameter model is used to simulate the relationship between the change of lithium battery temperature over time and the heat generation behavior of the lithium battery, wherein the heat generation behavior of the lithium battery includes reversible heat generation behavior and irreversible heat generation behavior; the lumped thermal parameter model includes lithium battery thermal performance parameters, and the optimal values ​​of the lithium battery thermal performance parameters are determined by genetic algorithms.

7. A lithium battery temperature estimation system according to claim 6, characterized in that: The lumped thermal parameter model comprises: in, Indicates the lithium battery temperature. represents thermal impedance, represents the heat capacity, Indicates the current ambient temperature. represents the sum of the reversible heat production rate and the irreversible heat production rate, Indicates the system running time.

8. A lithium battery temperature estimation system according to claim 7, characterized in that: The reversible heat generation rate comprises: in, represents the reversible heat generation rate, represents the current, Indicates the positive open circuit voltage of the lithium battery. Indicates the open circuit voltage of the negative electrode of the lithium battery. It represents the positive electrode entropy increase fitting curve of lithium battery, It represents the negative electrode entropy increase fitting curve of lithium battery. It represents the percentage of the average lithium ion concentration of the positive electrode to the maximum lithium ion concentration of the positive electrode. It represents the percentage of the average lithium ion concentration of the negative electrode to the maximum lithium ion concentration of the negative electrode. Indicates the reference temperature of lithium battery as the positive and negative open circuit voltages change; The irreversible heat generation rate includes: in, is the irreversible heat generation rate, Indicates the open circuit voltage of lithium battery, Indicates terminal voltage.

9. A lithium battery temperature estimation system according to claim 8, characterized in that: When determining the optimal value of thermal performance parameters of lithium batteries through genetic algorithms, the fitness function used includes: in, represents the fitness function, Indicates the number of sampling points, express t The actual lithium battery temperature collected at all times, express t The temperature of the lithium battery cell calculated by the lumped thermal parameter model at all times, Represents the estimated value of the heat generation parameter of the lithium battery.

10. A computer program product, characterized in that: The computer program product comprises instructions, and when the instructions are executed by a processor, a lithium battery temperature estimation method according to any one of claims 1 to 5 is implemented.

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