Lithium battery heat monitoring method, device and equipment and storage medium thereof

Abstract

The invention provides a lithium battery heat monitoring method, device and equipment and a storage medium thereof.The lithium battery heat monitoring method comprises the steps that firstly, operation parameters of a lithium battery pack are obtained and calculated based on a Bernard i model to generate theoretical heat, and the theoretical heat comprises chemical reaction heat and Joule heat; then, temperature data collected by a sensor network configured on the lithium battery pack are obtained, the theoretical heat and the temperature data are fused to generate fused data, and the temperature data comprise the battery internal temperature and the battery surface temperature; and finally, acquiring a temperature error value compensation item of the last period, and generating temperature field distribution of the lithium battery pack based on the fusion data and the temperature error value compensation item of the last moment, thereby solving the problem of poor thermal management effect caused by insufficient monitoring precision of the temperature field of the lithium battery pack.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicles, and in particular to a method, device, equipment and storage medium for monitoring heat of a lithium battery. Background Art

[0002] With the rapid development of new energy technologies, lithium-ion batteries have been widely used in electric vehicles, energy storage systems, portable electronic devices and other fields. However, lithium batteries generate a lot of heat during high-rate charging and discharging, mainly from irreversible resistance heat (Joule heat) and reversible chemical reaction heat. If this heat cannot be effectively dissipated, the battery temperature will rise sharply, which will not only accelerate the aging of internal battery materials and reduce the battery cycle life, but may also cause thermal runaway and cause safety accidents in severe cases.

[0003] Existing thermal management systems for lithium batteries mainly focus on external heat dissipation, such as passive cooling methods such as air cooling and liquid cooling, but do not adequately consider the heat generation mechanism inside the battery, especially the heat from chemical reactions. In addition, existing systems usually use fixed heat dissipation strategies and are unable to implement adaptive adjustments based on the heat generation characteristics under different working conditions, resulting in poor thermal management of the battery under high-rate working conditions, or excessive heat dissipation under low-rate working conditions, resulting in energy waste.

[0004] In view of this, this application is filed. Summary of the invention

[0005] The present invention discloses a method, device, equipment and storage medium for monitoring the heat of a lithium battery, aiming to solve the problem that the temperature field monitoring accuracy of a lithium battery pack is insufficient, resulting in poor thermal management effect.

[0006] A first embodiment of the present invention provides a method for monitoring heat of a lithium battery, comprising:

[0007] Acquire operating parameters of the lithium battery pack, and calculate the operating parameters based on the Bernard i model to generate theoretical heat, wherein the theoretical heat includes chemical reaction heat and Joule heat;

[0008] Acquire temperature data collected by a sensor network configured on the lithium battery pack, and fuse the theoretical heat with the temperature data to generate fused data, wherein the temperature data includes the internal temperature of the battery and the surface temperature of the battery;

[0009] The temperature error value compensation item of the previous cycle is obtained, and the temperature field distribution of the lithium battery pack is generated based on the fusion data and the temperature error value compensation item of the previous moment.

[0010] Preferably, the calculation process of calculating the operating parameters based on the Bernard i model to generate theoretical heat is:

[0011] Q theory =I 2 Rt-I T(dU / dT)t;

[0012] Among them, Q theory is the theoretical heat, I is the current, R is the internal resistance, dU / dT is the temperature entropy coefficient, and t is the charge and discharge time.

[0013] Preferably, the calculation process of the temperature error value compensation item of the previous cycle is:

[0014] Calling a pre-built multivariable nonlinear compensation function, and calculating the battery state of charge, current, temperature, and cycle number of the previous cycle based on the multivariable nonlinear compensation function to generate a compensation value;

[0015] The difference between the theoretical heat and the temperature data of the previous cycle is calculated, and the compensation value and the difference are processed by the gradient descent method to generate a temperature error value compensation item.

[0016] A second embodiment of the present invention provides a thermal monitoring device for a lithium battery, comprising:

[0017] A theoretical heat calculation unit, used to obtain operating parameters of the lithium battery pack and calculate the operating parameters based on the Bernard i model to generate theoretical heat, wherein the theoretical heat includes chemical reaction heat and Joule heat;

[0018] A data fusion unit, used to obtain temperature data collected by a sensor network configured on the lithium battery pack, and fuse the theoretical heat with the temperature data to generate fused data, wherein the temperature data includes a battery internal temperature and a battery surface temperature;

[0019] The temperature field distribution unit is used to obtain the temperature error value compensation item of the previous cycle, and generate the temperature field distribution of the lithium battery pack based on the fusion data and the temperature error value compensation item of the previous moment.

[0020] Preferably, the calculation process of calculating the operating parameters based on the Bernard i model to generate theoretical heat is:

[0021] Q theory =I 2 Rt-I T(dU / dT)t;

[0022] Among them, Q theory is the theoretical heat, I is the current, R is the internal resistance, dU / dT is the temperature entropy coefficient, and t is the charge and discharge time.

[0023] Preferably, the calculation process of the temperature error value compensation item of the previous cycle is:

[0024] Calling a pre-built multivariable nonlinear compensation function, and calculating the battery state of charge, current, temperature, and cycle number of the previous cycle based on the multivariable nonlinear compensation function to generate a compensation value;

[0025] The difference between the theoretical heat and the temperature data of the previous cycle is calculated, and the compensation value and the difference are processed by the gradient descent method to generate a temperature error value compensation item.

[0026] A third embodiment of the present invention provides a thermal monitoring device for a lithium battery, including a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a thermal monitoring method for a lithium battery as described in any one of the above.

[0027] A fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored therein, and the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a thermal monitoring method for a lithium battery as described in any one of the above items.

[0028] A thermal monitoring method, device, equipment and storage medium of a lithium battery provided by the present invention first obtains operating parameters of a lithium battery pack and calculates the operating parameters based on a Bernard i model to generate theoretical heat, wherein the theoretical heat includes chemical reaction heat and Joule heat; then, temperature data collected by a sensor network configured on the lithium battery pack is obtained, and the theoretical heat and the temperature data are fused to generate fused data, wherein the temperature data includes the internal temperature of the battery and the surface temperature of the battery; finally, a temperature error value compensation item of the previous cycle is obtained, and a temperature field distribution of the lithium battery pack is generated based on the fused data and the temperature error value compensation item of the previous moment, thereby solving the problem of insufficient accuracy in monitoring the temperature field of the lithium battery pack, resulting in poor thermal management effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow chart of a heat monitoring method for a lithium battery provided by the first embodiment of the present invention;

[0030] Figure 2 It is a module schematic diagram of a heat monitoring device for a lithium battery provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0033] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0035] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0036] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0037] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0038] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] The present invention discloses a method, device, equipment and storage medium for monitoring the heat of a lithium battery, aiming to solve the problem that the temperature field monitoring accuracy of a lithium battery pack is insufficient, resulting in poor thermal management effect.

[0040] See also Figure 1 The first embodiment of the present invention provides a method for thermal monitoring of a lithium battery, which can be performed by a thermal monitoring device of a lithium battery (hereinafter referred to as a monitoring device), and in particular, by one or more processors in the monitoring device, to implement at least the following steps:

[0041] S101, obtaining operating parameters of a lithium battery pack, and calculating the operating parameters based on a Bernard i model to generate theoretical heat, wherein the theoretical heat includes chemical reaction heat and Joule heat;

[0042] In this embodiment, the monitoring device may be the vehicle controller of the electric vehicle, which can communicate with the battery pack and collect the operating parameters of the lithium battery pack in real time. The corresponding operating system and application software may be installed in the monitoring device, and the functions required by this embodiment can be realized through the combination of the operating system and application software.

[0043] It should be noted that, in this embodiment, the operating parameters of the lithium battery pack are first obtained, including but not limited to the battery current (I), voltage (U), state of charge (SOC), temperature (T), and charge and discharge time (t). These operating parameters can be collected by a distributed temperature sensor network configured in the battery pack, which can include a surface temperature sensor and an embedded temperature sensor. The embedded temperature sensor is integrated between the battery electrode and the diaphragm through micro-nano processing technology and does not affect the electrochemical performance of the battery.

[0044] For current and voltage, the distributed temperature sensor network sensor obtains the current intensity and voltage change of the battery during the charging and discharging process by accurately measuring the battery. The battery state of charge (SOC) is estimated by the battery management system (BMS), which can comprehensively evaluate the remaining power and health of the battery based on the battery's charging and discharging history, voltage changes and temperature changes.

[0045] Next, the theoretical heat is generated based on the Bernard i model. The Bernard i model is a heat generation model widely used in battery thermal management. It can accurately predict the heat generation of lithium batteries during the charging and discharging process. Specifically, the theoretical heat calculated by the Bernard i model consists of two parts: chemical reaction heat and Joule heat. It is expressed as:

[0046] Q theory =I 2 Rt-I T(dU / dT)t;

[0047] Among them, Q theory is the theoretical heat, I is the current, R is the internal resistance, dU / dT is the temperature entropy coefficient, and t is the charge and discharge time.

[0048] By combining Joule heat and chemical reaction heat, the Bernardi model can provide a theoretical total heat generation Q for each charge and discharge cycle. theory In this calculation process, Joule heat mainly depends on the internal resistance and current of the battery, while chemical reaction heat is mainly affected by the chemical reaction of the battery.

[0049] The calculation method of theoretical heat not only provides a theoretical basis for the thermal management system of lithium batteries, but also helps to predict the temperature rise of batteries under different working conditions. By real-time monitoring of the operating parameters of the battery and combining the calculation results of the Bernard i model, the potential overheating risk of the battery can be discovered in advance, thereby providing data support for subsequent temperature control and thermal management strategies. For example, by comparing and analyzing the theoretical heat of the battery with the actual temperature field, the charge and discharge current can be adjusted in time before the battery temperature reaches the warning value, or the active cooling system can be started to avoid problems such as performance degradation, shortened life or thermal runaway of the battery due to excessive temperature.

[0050] S102, acquiring temperature data collected by a sensor network configured on the lithium battery pack, and fusing the theoretical heat with the temperature data to generate fused data, wherein the temperature data includes a battery internal temperature and a battery surface temperature;

[0051] In this embodiment, the theoretical heat and temperature data are preliminarily weighted and fused. The changing trends of the internal temperature of the battery and the surface temperature of the battery are different, so different weights should be assigned to each temperature data during the fusion process. For example, the internal temperature of the battery reflects the heat change inside the battery, which is more directly affected by the heat generation during the battery charging and discharging process, while the surface temperature of the battery is more easily affected by external heat dissipation conditions. Therefore, the internal temperature of the battery is usually given a higher weight during the fusion process.

[0052] Assume the theoretical heat is Q theory , the internal temperature of the battery is T internal , the battery surface temperature is T surface , the weighted average formula can be used for preliminary fusion:

[0053] T fused =w 1 *T internal +w 2 *T surface +w 3 *Q theory

[0054] Among them, w 1 、w 2 、w 3 They are weight coefficients, which indicate the importance of each data in the fusion. The weight coefficients can be adjusted by algorithms or obtained through historical data training according to actual conditions.

[0055] After the initial weighted fusion, the Kalman filter algorithm is used to further optimize the fusion result. Kalman filtering can update the state estimate of the system in real time based on the sensor's measurement values ​​and the calculation results of the theoretical model, thereby reducing noise and improving the accuracy of temperature data.

[0056] The Kalman filter process first sets an initial state estimate, which is the predicted value of the battery's temperature field and theoretical heat. Then, the Kalman filter makes corrections based on the difference between the actual temperature data collected by the sensor and the theoretical heat calculated based on the Bernard i model, and outputs a weighted optimal estimate. Through continuous feedback correction, the Kalman filter can gradually reduce errors in the time series.

[0057] During each charge and discharge cycle, as the battery temperature changes, the error between the theoretical heat and the sensor data may change. In order to achieve more accurate temperature field prediction, the weighting coefficient and Kalman filter parameters are continuously corrected through the error dynamic adjustment mechanism, so that the fusion result of temperature data always remains optimal under different working conditions. For example, in the case of high charging rate, the temperature inside the battery changes rapidly, and the weight of the internal temperature of the battery is automatically increased; when the temperature changes slowly or the external environment has a greater impact, the weights of the surface temperature and theoretical heat will be adjusted accordingly to maintain the accuracy of data fusion. Finally, the temperature data after weighted fusion and Kalman filtering will be used as the output of the system, that is, "fused data."

[0058] S103, obtaining a temperature error value compensation item of a previous cycle, and generating a temperature field distribution of the lithium battery pack based on the fusion data and the temperature error value compensation item of the previous moment.

[0059] The calculation process of the temperature error value compensation item of the previous cycle is:

[0060] Using the temperature field data collected by the temperature sensor, the actual heat generation Q is reversely calculated through the reverse heat conduction method. theory

[0061] Calculate the error between the theoretical heat production and the actual heat production, defined as ΔQ = Q actual -Q theory

[0062] Construct a multivariable nonlinear compensation function f(SOC, I, T, cycle), where the input includes the battery state of charge SOC, current I, temperature T and cycle number cycle, and the output compensation value (i.e., the temperature error compensation term of the previous cycle) is used to correct the theoretical heat generation;

[0063] Since the corrected heat generation is Q compensated =Q theory +f(SOC,I,T,cycle),

[0064] The target mean square error function is defined as:

[0065] E=(Q actual -Q theory -f(SOC,I,T,cycle)) 2 =(ΔQ-f(SOC,I,T,cycle)) 2

[0066] In other words, the target error E is the square of the difference between ΔQ and the output of the compensation function f. This relationship shows that the design goal of the compensation function f is to make its output as close to ΔQ as possible, thereby minimizing E (ideally, E = 0 means that f completely compensates for ΔQ)

[0067] Furthermore, in the process of generating the temperature field distribution, numerical simulation is used to combine the geometric structure and thermal conduction characteristics of the battery pack to accurately depict the temperature distribution of the battery. It should be noted that in the process of generating each temperature field distribution, the temperature error compensation term will be continuously fed back to ensure the adaptability of the model. During the battery charging and discharging process, the change in the temperature field will be affected by many factors, including the working state of the battery, the change in the heat source inside the battery, and the fluctuation of the external ambient temperature. Therefore, the temperature error compensation term will continue to be dynamically adjusted according to the deviation between the actual battery temperature and the theoretical heat to ensure that the battery temperature field always remains in an ideal state.

[0068] See also Figure 2 The second embodiment of the present invention provides a heat monitoring device for a lithium battery, comprising:

[0069] Theoretical heat calculation unit 201, used to obtain operating parameters of the lithium battery pack, and calculate the operating parameters based on the Bernard i model to generate theoretical heat, wherein the theoretical heat includes chemical reaction heat and Joule heat;

[0070] A data fusion unit 202 is used to obtain temperature data collected by a sensor network configured on the lithium battery pack, and fuse the theoretical heat with the temperature data to generate fused data, wherein the temperature data includes the internal temperature of the battery and the surface temperature of the battery;

[0071] The temperature field distribution unit 203 is used to obtain the temperature error value compensation item of the previous cycle, and generate the temperature field distribution of the lithium battery pack based on the fusion data and the temperature error value compensation item of the previous moment.

[0072] Preferably, the calculation process of calculating the operating parameters based on the Bernard i model to generate theoretical heat is:

[0073] Q theory =I 2 Rt-I T(dU / dT)t;

[0074] Among them, Q theory is the theoretical heat, I is the current, R is the internal resistance, dU / dT is the temperature entropy coefficient, and t is the charge and discharge time.

[0075] Preferably, the calculation process of the temperature error value compensation item of the previous cycle is:

[0076] Calling a pre-built multivariable nonlinear compensation function, and calculating the battery state of charge, current, temperature, and cycle number of the previous cycle based on the multivariable nonlinear compensation function to generate a compensation value;

[0077] The difference between the theoretical heat and the temperature data of the previous cycle is calculated, and the compensation value and the difference are processed by the gradient descent method to generate a temperature error value compensation item.

[0078] A third embodiment of the present invention provides a thermal monitoring device for a lithium battery, including a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a thermal monitoring method for a lithium battery as described in any one of the above.

[0079] A fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored therein, and the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a thermal monitoring method for a lithium battery as described in any one of the above items.

[0080] A thermal monitoring method, device, equipment and storage medium of a lithium battery provided by the present invention first obtains operating parameters of a lithium battery pack and calculates the operating parameters based on a Bernard i model to generate theoretical heat, wherein the theoretical heat includes chemical reaction heat and Joule heat; then, temperature data collected by a sensor network configured on the lithium battery pack is obtained, and the theoretical heat and the temperature data are fused to generate fused data, wherein the temperature data includes the internal temperature of the battery and the surface temperature of the battery; finally, a temperature error value compensation item of the previous cycle is obtained, and a temperature field distribution of the lithium battery pack is generated based on the fused data and the temperature error value compensation item of the previous moment, thereby solving the problem of insufficient accuracy in monitoring the temperature field of the lithium battery pack, resulting in poor thermal management effect.

[0081] Exemplarily, the computer program described in the third and fourth embodiments of the present invention may be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the device for implementing a thermal monitoring device for a lithium battery. For example, the device described in the second embodiment of the present invention.

[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. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the thermal monitoring method for a lithium battery, and uses various interfaces and lines to connect the various parts of the thermal monitoring method for a lithium battery.

[0083] The memory can be used to store the computer program and / or module, and the processor realizes various functions of a heat monitoring method for a lithium battery by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, a text conversion function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0084] Wherein, if the implemented module 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 such an 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. Wherein, 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), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0085] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0086] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for monitoring heat of a lithium battery, characterized in that: include: Acquire operating parameters of the lithium battery pack, and calculate the operating parameters based on the Bernardi model to generate theoretical heat, wherein the theoretical heat includes chemical reaction heat and Joule heat; Acquire temperature data collected by a sensor network configured on the lithium battery pack, and fuse the theoretical heat with the temperature data to generate fused data, wherein the temperature data includes the internal temperature of the battery and the surface temperature of the battery; The temperature error value compensation item of the previous cycle is obtained, and the temperature field distribution of the lithium battery pack is generated based on the fusion data and the temperature error value compensation item of the previous moment.

2. A method for monitoring the heat of a lithium battery according to claim 1, characterized in that: The calculation process of calculating the operating parameters based on the Bernardi model to generate theoretical heat is: Q theory =I 2 Rt-IT(dU / dT)t; Among them, Q theory is the theoretical heat, I is the current, R is the internal resistance, dU / dT is the temperature entropy coefficient, and t is the charge and discharge time.

3. The method for thermal monitoring of a lithium battery according to claim 1, characterized in that: The calculation process of the temperature error value compensation item of the previous cycle is: Calling a pre-built multivariable nonlinear compensation function, and calculating the battery state of charge, current, temperature, and cycle number of the previous cycle based on the multivariable nonlinear compensation function to generate a compensation value; The difference between the theoretical heat and the temperature data of the previous cycle is calculated, and the compensation value and the difference are processed by the gradient descent method to generate a temperature error value compensation item.

4. A thermal monitoring device for a lithium battery, characterized in that: include: A theoretical heat calculation unit, used to obtain operating parameters of the lithium battery pack and calculate the operating parameters based on the Bernardi model to generate theoretical heat, wherein the theoretical heat includes chemical reaction heat and Joule heat; A data fusion unit, used to obtain temperature data collected by a sensor network configured on the lithium battery pack, and fuse the theoretical heat with the temperature data to generate fused data, wherein the temperature data includes a battery internal temperature and a battery surface temperature; The temperature field distribution unit is used to obtain the temperature error value compensation item of the previous cycle, and generate the temperature field distribution of the lithium battery pack based on the fusion data and the temperature error value compensation item of the previous moment.

5. A thermal monitoring device for a lithium battery according to claim 4, characterized in that: The calculation process of calculating the operating parameters based on the Bernardi model to generate theoretical heat is: Q theory =I 2 Rt-IT(dU / dT)t; Among them, Q theory is the theoretical heat, I is the current, R is the internal resistance, dU / dT is the temperature entropy coefficient, and t is the charge and discharge time.

6. A thermal monitoring device for a lithium battery according to claim 4, characterized in that: The calculation process of the temperature error value compensation item of the previous cycle is: Calling a pre-built multivariable nonlinear compensation function, and calculating the battery state of charge, current, temperature, and cycle number of the previous cycle based on the multivariable nonlinear compensation function to generate a compensation value; The difference between the theoretical heat and the temperature data of the previous cycle is calculated, and the compensation value and the difference are processed by the gradient descent method to generate a temperature error value compensation item.

7. A thermal monitoring device for a lithium battery, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a thermal monitoring method for a lithium battery as claimed in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that: A computer program is stored, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement a thermal monitoring method for a lithium battery as described in any one of claims 1 to 3.

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