Lithium battery internal temperature estimation method, system, device, and medium

Abstract

The application discloses a lithium battery internal temperature estimation method, system, device and medium. The method comprises the following steps: establishing a battery internal temperature estimation thermal model, which firstly estimates the temperature difference between the battery surface and the internal temperature by using the battery current data, and then adds the temperature difference to the battery surface temperature to obtain the battery internal temperature estimation value; measuring the lithium battery surface temperature in the running process; collecting the battery current in the running process; and substituting the battery surface temperature and the battery current into the battery internal temperature estimation thermal model to calculate the current battery internal temperature. The present scheme can accurately estimate the temperature difference between the battery surface and the internal temperature by using the in-line battery current data, and then estimate the lithium battery internal temperature. The present scheme can accurately and timely estimate the battery internal temperature with less collection amount, small calculation amount, and wide application prospect.

Description

Technical Field

[0001] This application relates to the field of electrochemical energy storage, specifically to a method, system, device, and medium for estimating the internal temperature of a lithium battery. Background Technology

[0002] In large-scale lithium battery energy storage power stations, it is necessary to monitor the internal temperature of the battery to assess its operating status. However, since the battery is encapsulated in the module, it is difficult to measure directly, and other methods are needed to estimate the internal temperature of the battery in real time.

[0003] Reference: Chen Dehai et al. Battery internal temperature estimation based on unscented Kalman filter algorithm [J]. Journal of Automotive Safety and Energy Saving, 2022, 13(01): 186-193. A nonlinear unscented Kalman filter (UKF) estimation algorithm is proposed. For a 2.6Ah ternary lithium-ion battery, an equivalent variable parameter thermal model is established; the correlation between the internal and external temperatures of the battery is established and discretized using the state equation analysis method; the recursive least squares (RLS) method is used to identify four thermal parameters in the thermal model: time, surface temperature, ambient temperature, and input current, and the parameter matrix of the system state and observation equations is updated in real time. Combined with the UKF algorithm, the internal temperature of the battery is estimated. Reference: Ji Fenzhu et al. Thermal model and heat dissipation characteristics of electric vehicle power battery [J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(01): 18-24. DOI: 10.13700 / j.bh.1001-5965.2014.01.015. Based on the Bernardi heat generation rate model, a thermal coupling model of the current collector of the positive electrode, the current collector of the negative electrode, and the battery plate of a single battery was established, as well as a heat transfer model of the battery pack. The heat generation characteristics of LiFePO4 single battery in a natural ventilation environment were simulated and analyzed using Fluent software. The heat generation and heat dissipation characteristics of the battery pack under forced air convection cooling conditions were simulated. The influence of the air outlet position of the battery box on the battery temperature was analyzed. The temperature change of the battery pack under different discharge rates was calculated.

[0004] Patent CN105260612A, "A Method for Online Temperature Estimation of a Battery," utilizes a first-order equivalent circuit model of the battery to be estimated to perform online estimation of battery state parameters during charging and discharging, obtaining the battery's open-circuit voltage V at time t-1. OCV,t-1 By utilizing the relationship between the battery temperature change during use and the battery's heat generation and dissipation conditions, a battery thermal balance model is established. The balance equation is obtained by estimating the temperature at time t using time t-1, thus yielding the battery's online temperature T at time t. tPatent CN104865534A, "A Method for Estimating the Internal Temperature of a Single-Cell Battery," proposes a method for estimating the internal temperature of a single-cell battery, comprising an offline part and an online part. The offline part includes: a1) obtaining the internal resistance characteristics of the battery cell under different temperature conditions; a2) obtaining a standard relationship between the battery's internal temperature and its internal resistance based on the internal resistance characteristics. The online part includes: b1) detecting the current, terminal voltage, and surface temperature of the currently operating battery online; b2) estimating the battery's internal resistance online based on the current and terminal voltage; b3) estimating the internal temperature of the currently operating battery based on the battery's internal resistance obtained in step b2), the surface temperature obtained in step b1), and the standard relationship obtained in step a2). Patent CN113884901A, "A Method and System for Estimating Battery Surface Temperature Distribution," discloses a method and system for estimating battery surface temperature distribution. This method proposes for the first time a three-heat-source electro-thermal coupling model, including a three-heat-source heat transfer model and a resistive distributed equivalent current model, to calculate the battery's thermal and electrical characteristics, thereby achieving instantaneous temperature estimation at characteristic temperature points. The specific steps include: establishing the three-heat-source heat transfer model and the resistive distributed equivalent circuit model; measuring and acquiring battery voltage, current, and temperature values ​​at characteristic points under different operating conditions; identifying relevant parameters in the model; verifying the model's accuracy and responsiveness under steady-state and transient operating conditions; and further improving the real-time temperature monitoring capability of the battery management system.

[0005] The above methods estimate models that are too complex, have cumbersome calculation processes, require high hardware computing power, and require too many analog samples. Summary of the Invention

[0006] The purpose of this application is to provide a method for estimating the internal temperature of a lithium battery, which solves the problems of complex estimation models and excessive collection of analog data in existing technologies.

[0007] To achieve the above objectives, this application adopts the following technical solution:

[0008] The first aspect of this application proposes a method for estimating the internal temperature of a lithium battery, comprising:

[0009] A thermal model for estimating the internal temperature of a battery is established. The thermal model first uses battery current data to estimate the temperature difference between the battery surface and the internal temperature, and then adds the temperature difference to the battery surface temperature to obtain the estimated value of the internal temperature of the battery.

[0010] Measuring the surface temperature T of the lithium battery during operation B ;

[0011] Collect the battery current I during operation;

[0012] The battery surface temperature and battery current are substituted into the battery internal temperature estimation thermal model to calculate the current battery internal temperature.

[0013] According to some embodiments, the thermal model for estimating the internal temperature of the battery is as follows:

[0014] T N (k)=T B (k)+ΔT(k)

[0015] ΔT(k)=h(k)θ

[0016] h(k) = [ΔT(k-1), ΔT(k-2), I 2 (k),I 2 (k-1)]

[0017] θ = [a1, a2, a3, a4] T

[0018] T N T represents the internal temperature of the battery. B Let ΔT be the battery surface temperature, ΔT be the temperature difference between the battery surface and internal temperatures, and I be the battery current. 2 Let θ be the square of the battery current, h(k) be the set of known quantities of the temperature difference current, k be the current sampling point, k-1 be the previous sampling point of k, k-2 be the previous sampling point of k-1, a1, a2, a3, a4 be the model parameters, and θ be the model parameter vector.

[0019] According to some embodiments, the model parameter vector θ is fitted using experimental data, including:

[0020] The room temperature, indoor ventilation, lithium battery fan power, and lithium battery placement in the experimental site were kept consistent with those in the actual operation.

[0021] A rate charge-discharge experiment was conducted on the battery to test constant current charging and discharging at different rates. The charging and discharging was stopped after reaching the charge-discharge cutoff voltage, and the battery was left to rest for a preset time between charging and discharging.

[0022] Record the surface temperature, internal temperature, and current of the lithium battery during the experiment.

[0023] The optimal estimate of parameter θ is calculated based on experimental process data.

[0024] According to some embodiments, the battery surface temperature is one of the surface temperature, top surface temperature, and side surface temperature.

[0025] According to some embodiments, the calculation of the optimal estimate of the parameter vector θ based on experimental process data includes:

[0026] Based on the surface temperature and internal temperature of the lithium battery during the experiment, a discrete sample ΔT(i) of the temperature difference between the surface temperature and the internal temperature of the battery is obtained, i = 0, 1, 2, ..., n, where n is the total number of sampling points;

[0027] The discrete sampling I(i) of the battery current is obtained based on the battery current during the experiment.

[0028] The set of known quantities h(i) of temperature difference current is calculated based on discrete sampling of the temperature difference between the battery surface and internal temperature and discrete sampling of the battery current.

[0029] The optimal estimate of the value of θ is calculated using the following formula:

[0030] θ=(H T H) -1 H T T

[0031] H is based on h T (i) is an n-row matrix composed of rows, and T is an n-dimensional column vector composed of elements of ΔT(i).

[0032] According to some embodiments, the optimal estimation method employs least squares, minimum variance, maximum likelihood, maximum a posteriori, or linear minimum variance.

[0033] According to some embodiments, the battery module contains several batteries, where j = 1, 2, ..., m, and m is the total number of batteries. The experiment should measure the surface and internal temperature of each battery j and calculate the parameter vector θ of battery j. j .

[0034] A second aspect of this application proposes a lithium battery internal temperature estimation system for estimating the temperature of several batteries in a battery module, the system comprising:

[0035] Several temperature acquisition modules collect several battery surface temperatures T. B And send it to the temperature estimation module;

[0036] The current acquisition module acquires the battery current I and sends it to the temperature estimation module;

[0037] The temperature estimation module includes a thermal model for estimating the internal temperature of the battery. The thermal model first estimates the temperature difference between the battery surface and the internal temperature using battery current data, and then adds the temperature difference to the battery surface temperature to obtain an estimated value of the internal temperature of the battery. The collected battery surface temperature and battery current are then substituted into the thermal model for estimating the internal temperature of the battery to calculate the current internal temperature of the battery.

[0038] A third aspect of this application discloses an electronic device comprising:

[0039] Processor; and

[0040] The memory stores computer instructions that, when executed by the processor, cause the processor to perform the lithium battery internal temperature estimation method described above.

[0041] The fourth aspect of this application proposes a non-transient computer storage medium storing a computer program that, when executed by multiple processors, causes the processors to perform the lithium battery internal temperature estimation method described above.

[0042] Compared with existing technologies, the advantages of this application are: It requires less real-time data acquisition, only real-time acquisition of battery surface temperature and battery current. The internal battery temperature can be accurately estimated in real-time using a temperature difference-based coupling model. The algorithm is simple, computationally inexpensive, and highly stable, requiring fewer measurement points, and thus has broad application prospects. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating an exemplary embodiment of a method for estimating the internal temperature of a lithium battery;

[0045] Figure 2 A flowchart illustrating the fitting of a model parameter vector θ to experimental data in an exemplary embodiment is shown.

[0046] Figure 3 A flowchart illustrating an exemplary embodiment of a method for calculating the optimal estimation of the parameter vector θ based on experimental process data is provided.

[0047] Figure 4 The waveforms of the fitted and measured temperature difference values ​​for cell 8 are shown.

[0048] Figure 5 A schematic diagram of an exemplary embodiment of a lithium battery internal temperature estimation system is shown.

[0049] Figure 6 This diagram illustrates the structure of an electronic device provided in this application. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] This application provides a method for estimating the internal temperature of a lithium battery, as shown in the embodiments below. Figure 1 As shown, it includes the following steps:

[0052] S101, Establish a thermal model for estimating the internal temperature of the battery.

[0053] The construction of the thermal model for estimating the internal temperature of a battery includes: first, estimating the temperature difference between the battery surface and the internal temperature using battery current data, and then adding the temperature difference to the battery surface temperature to obtain the estimated value of the internal temperature of the battery.

[0054] S102, Measure the surface temperature T of the lithium battery during operation. B Collect the battery current I during operation.

[0055] In some embodiments, the battery surface temperature can be the surface temperature, top surface temperature, or side surface temperature.

[0056] S103, substitute the battery surface temperature and battery current into the battery internal temperature estimation thermal model to calculate the current battery internal temperature.

[0057] In some embodiments, the thermal model for estimating the internal temperature of the battery is as follows:

[0058] T N (k)=T B (k)+ΔT(k)

[0059] ΔT(k)=h(k)θ

[0060] h(k) = [ΔT(k-1), ΔT(k-2), I 2 (k),I 2 (k-1)]

[0061] θ = [a1, a2, a3, a4] T

[0062] T N T represents the internal temperature of the battery. B Let ΔT be the battery surface temperature, ΔT be the temperature difference between the battery surface and internal temperatures, and I be the battery current. 2Let θ be the square of the battery current, h(k) be the set of known quantities of the temperature difference current, k be the current sampling point, k-1 be the previous sampling point of k, k-2 be the previous sampling point of k-1, a1, a2, a3, a4 be the model parameters, and θ be the model parameter vector.

[0063] In the thermal model, ΔT(k-1) of h(k) is the result of the previous calculation ΔT(k), ΔT(k-2) is the result of the previous calculation ΔT(k-1), and I 2 (k-1) is the square of the previous current sample, and then the process is repeated cyclically.

[0064] In some embodiments, the model parameter vector θ is fitted using experimental data, such as... Figure 2 The flowchart shows the following:

[0065] S201. The room temperature, indoor ventilation, lithium battery fan power, and lithium battery placement in the experimental site shall be consistent with those in the field operation.

[0066] S202. Conduct a rate charge-discharge experiment on the battery, testing constant current charging and discharging at different rates. Charge and discharge are stopped once the charging / discharging cutoff voltage is reached, and the battery is allowed to rest for a preset time between charges and discharges. The resting time is generally no less than 30 minutes.

[0067] For example, in one specific embodiment, a rate charge-discharge experiment is conducted on the battery to test constant current charging and discharging at rates of 0.1C, 0.2C, 0.5C, 1C, 2C, 3C, 4C, and 5C, where 1C represents the rated rate of the battery. The charging and discharging stops after reaching the charge-discharge cutoff voltage, and the battery is left to rest for 30 minutes between charging and discharging.

[0068] S203. Record the surface temperature, internal temperature and current of the lithium battery during the experiment.

[0069] S204. Calculate the optimal estimate of parameter θ based on experimental process data.

[0070] The optimal estimation method can be the least squares method, the minimum variance method, the maximum likelihood method, the maximum a posteriori method, or the linear minimum variance method.

[0071] In some embodiments, the optimal estimate of the parameter vector θ is calculated based on experimental process data, such as... Figure 3 The flowchart shows the following:

[0072] S301. Based on the surface temperature and internal temperature of the lithium battery during the experiment, obtain discrete sampling ΔT(i) of the temperature difference between the surface temperature and the internal temperature of the battery, i = 0, 1, 2, ..., n, where n is the total number of sampling points;

[0073] S302. Based on the battery current during the experiment, obtain discrete sampling I(i) of the battery current;

[0074] S303. Calculate the set of known quantities h(i) of temperature difference current based on discrete sampling of the temperature difference between the battery surface temperature and the internal temperature difference and discrete sampling of the battery current;

[0075] S304. Calculate the optimal estimate of θ using the following formula:

[0076] θ=(H T H) -1 H T T

[0077] H is based on h T (i) is an n-row matrix composed of rows, and T is an n-dimensional column vector composed of elements of ΔT(i).

[0078] In some embodiments, the battery module comprises several batteries, j = 1, 2, ..., m, where m is the total number of batteries. The experiment should measure the surface and internal temperature of each battery j and calculate the parameter vector θj of battery j. A specific case is introduced below using the linear least squares method.

[0079] The temperature difference ΔT between the aluminum handle and the large surface area was fitted to the square of the current I using the linear least squares method. 2 (Ignoring changes in battery internal resistance R), the model parameters are fitted to obtain parameter θ based on battery laboratory module test data (data from cell 8). The fitted model is then used to generate a temperature difference, and the fitted temperature difference is used iteratively to generate an estimated temperature difference value ΔT. fit ΔT fit The error between the measured value ΔT and the actual value is shown in the table below:

[0080] Table 1. Errors between measured values ​​and model iteration results

[0081]

[0082] ΔT fit See the results with ΔT. Figure 4 As shown.

[0083] ΔT fit The errors are very small, with MAE around 0.2 and RMSE around 0.25. The parameter identification results are shown in the table below:

[0084] Table 2 Parameter Identification Results

[0085] parameter value parameter value <![CDATA[a1]]> 5.34905213e-01 <![CDATA[a2]]> 4.47535946e-01 <![CDATA[a3]]> 4.06421006e-07 <![CDATA[a4]]> 1.06676300e-06

[0086] Figure 5 The system shown can execute the lithium battery internal temperature estimation method described above according to the embodiments of this application.

[0087] like Figure 5 As shown, the lithium battery internal temperature estimation system 400 includes: a temperature acquisition module 401, a current acquisition module 402, and a temperature estimation module 403.

[0088] Several temperature acquisition modules 401 acquire several battery surface temperatures TB and send them to the temperature estimation module;

[0089] The current acquisition module 402 acquires the battery current I and sends it to the temperature estimation module;

[0090] The temperature estimation module 403 includes a battery internal temperature estimation thermal model. The battery internal temperature estimation thermal model first estimates the temperature difference between the battery surface and the internal temperature using battery current data, and then adds the temperature difference to the battery surface temperature to obtain an estimated value of the battery internal temperature. The collected battery surface temperature and battery current are substituted into the battery internal temperature estimation thermal model to calculate the current battery internal temperature.

[0091] The system performs functions similar to those provided earlier. Other functions can be found in the previous descriptions and will not be repeated here.

[0092] Figure 6 This diagram illustrates the structure of an electronic device provided in this application.

[0093] See Figure 6 , Figure 6 An electronic device is provided, including a processor and a memory. The memory stores computer instructions, which, when executed by the processor, cause the processor to perform the computer instructions to achieve the following: Figure 1 The method and its detailed scheme are shown.

[0094] It should be understood that the above-described device embodiments are merely illustrative, and the device disclosed in this invention can be implemented in other ways. For example, the division of units / modules described in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, integrated into another system, or some features may be ignored or not executed.

[0095] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of the present invention can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0096] If the integrated unit / module is implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor or chip can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the on-chip cache, off-chip memory, and storage can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0097] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0098] This application embodiment also provides a non-transitory computer storage medium storing a computer program, which, when executed by multiple processors, causes the processors to perform actions such as... Figure 1 The method and its detailed scheme are shown.

[0099] It should be clearly understood that this application describes how specific examples are formed and used, but this application is not limited to any details of these examples. Rather, based on the teachings of the disclosure of this application, these principles can be applied to many other embodiments.

[0100] Furthermore, it should be noted that the above figures are merely illustrative representations of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0101] Exemplary embodiments of this application have been specifically shown and described above. It should be understood that this application is not limited to the detailed structures, arrangements, or implementation methods described herein; rather, this application is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A method for estimating the internal temperature of a lithium battery, characterized in that, include: A thermal model for estimating the internal temperature of a battery is established. The thermal model first uses battery current data to estimate the temperature difference between the battery surface and the internal temperature, and then adds the temperature difference to the battery surface temperature to obtain the estimated value of the internal temperature of the battery. Measuring the surface temperature T of the lithium battery during operation B ; Collect the battery current I during operation; The battery surface temperature and battery current are substituted into the battery internal temperature estimation thermal model to calculate the current battery internal temperature; The thermal model for estimating the internal temperature of the battery is as follows: T N (k)=T B (k)+ΔT(k) ΔT(k)=h(k)θ h(k)=[ΔT(k-1),ΔT(k-2),I 2 (k),I 2 (k-1)] θ=[a1,a2,a3,a4] T T N T represents the internal temperature of the battery. B Let ΔT be the battery surface temperature, ΔT be the temperature difference between the battery surface and internal temperatures, and I be the battery current. 2 Let θ be the square of the battery current, h(k) be the set of known quantities of the temperature difference current, k be the current sampling point, k-1 be the previous sampling point of k, k-2 be the previous sampling point of k-1, a1, a2, a3, a4 be the model parameters, and θ be the model parameter vector.

2. The method for estimating the internal temperature of a lithium battery as described in claim 1, characterized in that, The model parameter vector θ is fitted using experimental data, including: The room temperature, indoor ventilation, lithium battery fan power, and lithium battery placement in the experimental site were kept consistent with those in the actual operation. A rate charge-discharge experiment was conducted on the battery to test constant current charging and discharging at different rates. The charging and discharging was stopped after reaching the charge-discharge cutoff voltage, and the battery was left to rest for a preset time between charging and discharging. Record the surface temperature, internal temperature, and current of the lithium battery during the experiment. The optimal estimate of parameter θ is calculated based on experimental process data.

3. The method for estimating the internal temperature of a lithium battery as described in claim 1, characterized in that, The battery surface temperature is one of the surface temperature, top surface temperature, and side surface temperature.

4. The method for estimating the internal temperature of a lithium battery as described in claim 2, characterized in that, The optimal estimate of the parameter vector θ calculated based on experimental process data includes: Based on the surface temperature and internal temperature of the lithium battery during the experiment, a discrete sample ΔT(i) of the temperature difference between the battery surface and internal temperature is obtained, i = 0, 1, 2, ..., n, where n is the total number of sampling points; The discrete sampling I(i) of the battery current is obtained based on the battery current during the experiment. The set of known quantities h(i) of temperature difference current is calculated based on discrete sampling of the temperature difference between the battery surface and internal temperature and discrete sampling of the battery current. The optimal estimate of θ is calculated using the following formula: θ=(H T H) -1 H T T H is based on h T (i) is an n-row matrix composed of rows, and T is an n-dimensional column vector composed of elements of ΔT(i).

5. The method for estimating the internal temperature of a lithium battery as described in claim 2, characterized in that, The optimal estimation method employs the least squares method, the minimum variance method, the maximum likelihood method, the maximum a posteriori method, or the linear minimum variance method.

6. The method for estimating the internal temperature of a lithium battery as described in claim 2, characterized in that, The battery module contains several batteries, j = 1, 2, ..., m, where m is the total number of batteries. The experiment should measure the surface and internal temperature of each battery j and calculate the parameter vector θ of battery j. j .

7. A lithium battery internal temperature estimation system for estimating the temperature of several batteries in a battery module, characterized in that, The system includes: Several temperature acquisition modules collect several battery surface temperatures T. B And send it to the temperature estimation module; The current acquisition module acquires the battery current I and sends it to the temperature estimation module; The temperature estimation module includes a battery internal temperature estimation thermal model. The battery internal temperature estimation thermal model first estimates the temperature difference between the battery surface and the internal temperature using battery current data, and then adds the temperature difference to the battery surface temperature to obtain the estimated value of the battery internal temperature. The collected battery surface temperature and battery current are substituted into the battery internal temperature estimation thermal model to calculate the current battery internal temperature. The thermal model for estimating the internal temperature of the battery is as follows: T N (k)=T B (k)+ΔT(k) ΔT(k)=h(k)θ h(k)=[ΔT(k-1),ΔT(k-2),I 2 (k),I 2 (k-1)] θ=[a1,a2,a3,a4] T T N T represents the internal temperature of the battery. B Let ΔT be the battery surface temperature, ΔT be the temperature difference between the battery surface and internal temperatures, and I be the battery current. 2 Let θ be the square of the battery current, h(k) be the set of known quantities of the temperature difference current, k be the current sampling point, k-1 be the previous sampling point of k, k-2 be the previous sampling point of k-1, a1, a2, a3, a4 be the model parameters, and θ be the model parameter vector.

8. An electronic device, characterized in that, include: processor; as well as A memory storing computer instructions that, when executed by the processor, cause the processor to perform the estimation method according to any one of claims 1-6.

9. A non-transient computer storage medium storing a computer program that, when executed by a plurality of processors, causes the processors to perform the estimation method according to any one of claims 1-6.

Images