Battery module cell temperature prediction method, electronic device, and storage medium

Abstract

The application provides a battery module cell temperature prediction method, an electronic device and a storage medium. The battery module includes (K+L) cells, wherein K is a number of non-sampling cells and L is a number of sampling cells, and K>=1 and L>=2. The prediction method includes: establishing a heat conduction equation of the battery module; performing a discretization processing on the heat conduction equation based on a position distance and a temperature transmission time between any two sampling cells in the battery module to obtain a recursive equation of the cell temperature in the battery module; and predicting the temperature of the non-sampling cell in the battery module based on the recursive equation. The battery module cell temperature prediction method provided by the application solves the problem that the prior art cannot obtain the temperature of all cells in the battery module.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to a method for predicting the temperature of battery cells in a battery module, an electronic device, and a storage medium. Background Technology

[0002] The importance of temperature for lithium-ion batteries is self-evident. Operating at high temperatures for extended periods not only affects battery life but may also pose safety hazards. Conversely, operating at low temperatures for extended periods may lead to insufficient battery capacity, lithium plating, or even short circuits.

[0003] For battery management systems, monitoring the temperature of battery cells is essential. However, due to the large number of cells in a battery, the temperature monitoring interface of the battery management system cannot sample the temperature of each individual cell. Therefore, the basic approach of existing technologies is to determine the temperature field distribution of the battery through simulation and preliminary experiments, rationally arrange temperature sensors (such as at the highest and lowest temperatures), and use the highest and lowest temperatures of the temperature sensors for logic control to achieve temperature monitoring of the entire battery.

[0004] The above engineering practices worked without problems for a long time. However, with the popularization of lithium-ion batteries, some problems have emerged under various operating conditions, such as shorter battery life and charging fires. Industry analysis has determined that one of the reasons for these problems is inaccurate temperature sampling. Therefore, temperature monitoring of all battery cells has become an engineering issue. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method, electronic device and storage medium for predicting the temperature of battery cells in a battery module, so as to solve the problem that the prior art cannot know the temperature of all battery cells in the battery module.

[0006] To achieve the above and other related objectives, this invention provides a method for predicting the temperature of battery cells in a battery module, wherein the battery module comprises (K+L) cells, of which K are non-sampling cells and L are sampling cells, and K≥1, L≥2; the prediction method includes:

[0007] Establish the heat conduction equation for the battery module;

[0008] Based on the positional distance and temperature transfer time between any two sampled cells in the battery module, the heat conduction equation is discretized to obtain the recursive equation for the cell temperature in the battery module.

[0009] The temperature of the non-sampled cells in the battery module is predicted based on the recursive equation.

[0010] Optionally, the heat conduction equation satisfies: Its boundary conditions satisfy:

[0011] u(a,t)=γ1(t), u(b,t)=γ2(t);

[0012] in, Let be the first-order partial derivative of the temperature at position x in the battery module at time t. Let f(x,t) be the second partial derivative of the temperature at position x in the battery module at time t, f(x,t) be the heat source at position x in the battery module at time t, and α be a constant. Let γ1(t) be the initial temperature at position x in the battery module at the start time, γ2(t) be the temperature of the a-th cell in the battery module at time t, and γ2(t) be the temperature of the b-th cell in the battery module at time t. The a-th cell and the b-th cell are sampling cells.

[0013] Optionally, the method for obtaining the recursive equation includes:

[0014] Discretize the positional distance and temperature transfer time between the a-th cell and the b-th cell in a planar coordinate system to obtain a temperature equation related to position and time;

[0015] The first-order partial derivative of temperature is obtained by performing a one-way Taylor expansion on time in the temperature equation, and the second-order partial derivative of temperature is obtained by performing a two-way Taylor expansion on position in the temperature equation.

[0016] Substituting the first-order and second-order partial derivatives of the temperature into the heat conduction equation and then performing a matrix transformation, the recursive equation is obtained.

[0017] Optionally, the recursive equation satisfies:

[0018] Among them, u m+1 For all cells in the battery module at t m+1 Temperature at time, u m For all cells in the battery module at t m The temperature at a given time, where A is a temperature-related coefficient matrix based on location and time, and f m For all cells in the battery module at t m The heat source at any given time, Δx is the unit step size corresponding to dividing the positional distance between the a-th cell and the b-th cell in the battery module into N equal parts, N≥2, and Δt is the unit time interval.

[0019] Optionally, the method for obtaining the heat source of all cells in the battery module includes:

[0020] Several sample modules are provided, wherein (K+L) cells in the sample modules are all sampling cells;

[0021] The heat source of each cell is obtained based on the sampling temperature of each cell in the sample module, and the heat source ratio coefficient of each cell is obtained by fitting the heat source of each cell.

[0022] The heat source of the corresponding sampled cell is obtained based on the sampling temperature of any sampled cell in the battery module, and the heat source of any non-sampled cell is obtained based on the heat source ratio coefficient, thereby obtaining the heat source of all cells in the battery module.

[0023] Optionally, the method for predicting the temperature of non-sampled cells in the battery module based on the recursive equation includes: obtaining the initial temperature of all cells in the battery module at the start time; substituting the initial temperature of all cells in the battery module at the start time into the recursive equation to predict the temperature of non-sampled cells in the battery module; wherein, the method for obtaining the initial temperature of all cells in the battery module at the start time includes: during the battery module startup phase, obtaining the sampling temperature of each sampled cell in the battery module as the initial temperature of each sampled cell, and obtaining the initial temperature of each non-sampled cell based on each of the sampling temperatures, thereby obtaining the initial temperature of all cells in the battery module at the start time.

[0024] Optionally, the average of the sampled temperatures can be used as the initial temperature of each of the non-sampled cells.

[0025] Optionally, before or after predicting the temperature of the non-sampled cells in the battery module, the prediction method further includes:

[0026] The predicted temperature is obtained by predicting the temperature of the sampled cells in the battery module based on the recursive equation.

[0027] The predicted temperature of the corresponding sampled cell is compared with its sampled temperature, and the sampling error of the battery module is determined based on the comparison result.

[0028] The present invention also provides an electronic device, the electronic device comprising: a memory and a processor, the memory for storing a computer program, and the processor for executing the computer program stored in the memory to enable the electronic device to perform the method for predicting the cell temperature in the battery module as described above.

[0029] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for predicting the cell temperature in the battery module as described above.

[0030] As described above, the method, electronic device, and storage medium for predicting cell temperature in the battery module of the present invention use the sampling temperature of the known sampled cells in the battery module to predict the temperature of the non-sampled cells, thereby obtaining the temperature of all cells in the battery module, and enabling accurate monitoring of the battery module temperature. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the existing battery module temperature monitoring system.

[0032] Figure 2 The flowchart shown is a prediction method of the present invention.

[0033] Figure 3 The diagram shown is a structural schematic of the electronic device of the present invention.

[0034] Component designation explanation

[0035] 100 Electronic devices

[0036] 101 Memory

[0037] 102 processor Detailed Implementation

[0038] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0039] Please see Figures 1 to 3 It should be noted that the illustrations provided in this embodiment are only schematic representations of the basic concept of the present invention. Although the illustrations only show components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation, the shape, quantity and proportion of each component in the actual implementation can be arbitrarily changed, and the layout of the components may also be more complex.

[0040] like Figure 1 As shown, taking a battery module consisting of 9 cells (such as B1-B9) as an example, the existing scheme for monitoring the temperature of the battery module is briefly explained.

[0041] In existing solutions, for a battery module consisting of 9 cells, three sampling points are typically set up. Each sampling point is equipped with a temperature sensor (e.g., T1-T3). That is, temperature sensor T1 samples the temperature of the first cell B1, temperature sensor T2 samples the temperature of the fourth cell B4, and temperature sensor T3 samples the temperature of the ninth cell B9. Of course, for battery modules consisting of other numbers of cells, the number of sampling points can be increased or decreased appropriately, but the number of sampling points for each battery module must be no less than 2. In practical applications, three sampling points are generally set up, at both ends of the battery module and at the cells near the middle.

[0042] The existing solution only samples the temperature of three cells (such as B1, B4, and B9) in the battery module to achieve temperature monitoring of the entire battery module; however, since the temperature of all cells in the battery module cannot be known, accurate temperature monitoring cannot be achieved.

[0043] In view of this, the applicant proposed the solution of the embodiment of this application, which can predict the temperature of non-sampled cells using the existing structure without adding additional temperature sampling resources (such as temperature sensors, temperature monitoring interfaces of battery management systems, etc.), thereby knowing the temperature of all cells in the battery module, realizing temperature acquisition of all cells in the battery module, and improving the accuracy of temperature monitoring.

[0044] For ease of description, the battery module in this embodiment is defined as including (K+L) cells, where K are non-sampling cells and L are sampling cells, and K≥1, L≥2. Non-sampling cells refer to cells that do not have temperature sensors and cannot be sampled using temperature sensors, such as... Figure 1 Cells B2 (2nd), B3 (3rd), B5 (5th), B6 ​​(6th), B7 (7th), and B8 (8th) are included in the battery configuration. Sampling cells are cells equipped with temperature sensors that can be used for temperature sampling, such as… Figure 1 The first battery cell B1, the fourth battery cell B4, and the ninth battery cell B9.

[0045] In the battery module of this application embodiment, when K≥2, the number of sampled cells can be set to be no more than half of the total number of cells, that is, 2≤L≤(K+L) / 2; in practical applications, K is generally greater than 5 and L is generally equal to 3; in this way, the temperature of all cells in the battery module can be obtained by executing the prediction method of this application embodiment, while avoiding excessive consumption of temperature acquisition resources, ensuring the accuracy of temperature monitoring while reducing the cost of temperature monitoring.

[0046] like Figure 2As shown, this application provides a method for predicting the temperature of battery cells in a battery module. The method includes steps S1, S2, and S3. This prediction method can be executed by an electronic device, such as a computer; however, other entities capable of executing this prediction method are also applicable to this application, and no limitation is made. The following description uses a computer as the execution entity as an example:

[0047] Step S1: Establish the heat conduction equation for the battery module.

[0048] Specifically, a heat conduction equation is established based on the characteristics of the battery module itself, satisfying: Its boundary conditions satisfy: u(a,t)=γ1(t), u(b,t)=γ2(t). This heat conduction equation is a partial differential equation that describes how the temperature changes over time within a region.

[0049] In the heat conduction equation, Let be the first-order partial derivative of the temperature at position x in the battery module at time t. Let f(x,t) be the second partial derivative of the temperature at position x in the battery module at time t, f(x,t) be the heat source at position x in the battery module at time t, and α be a constant. k is the thermal conductivity coefficient, c is the specific heat capacity, and ρ is the density; for battery modules, k, c, and ρ are all constants, so α is a constant.

[0050] In the boundary conditions, Let γx be the initial temperature at position x in the battery module at the start time, γ1(t) be the temperature of the a-th cell in the battery module at time t, and γ2(t) be the temperature of the b-th cell in the battery module at time t. Here, the start time refers to the moment when the battery module initially starts or begins operation from a state of rest.

[0051] In this embodiment, position x is any position in the battery module, which can be the position of the sampling cell in the battery module, the position of the non-sampling cell in the battery module, or other positions in the battery module besides the sampling cell and the non-sampling cell; the a-th cell and the b-th cell are sampling cells.

[0052] Step S2: Based on the positional distance and temperature transfer time between any two sampled cells in the battery module, the heat conduction equation is discretized to obtain the recursive equation for the cell temperature in the battery module.

[0053] Specifically, taking any two sampled battery cells, including cell a and cell b, as an example, the method for obtaining the recursive equation includes: discretizing the positional distance and temperature transfer time between cell a and cell b in a planar coordinate system (e.g., discretizing the positional distance on the horizontal axis and the temperature transfer time on the vertical axis) to obtain a temperature equation related to position and time; performing a one-way Taylor expansion on the time in the temperature equation to obtain the first-order partial derivative of temperature, and performing a two-way Taylor expansion on the position in the temperature equation to obtain the second-order partial derivative of temperature; substituting the first-order and second-order partial derivatives of temperature into the heat conduction equation and performing matrix transformation to obtain the recursive equation. Among these steps,

[0054] The recurrence relation satisfies: Among them, u m+1 For all cells in the battery module at t m+1 Temperature at time, u m For all cells in the battery module at t m The temperature at a given time, where A is a temperature-related coefficient matrix based on location and time, and f m For all cells in the battery module at t m The heat source at any given time, Δx is the unit step size corresponding to dividing the positional distance between the a-th cell and the b-th cell in the battery module into N (N≥2) equal parts, Δt is the unit time interval, and α is a constant.

[0055] The specific method for obtaining this recurrence equation is explained in detail below:

[0056] The location distance is discretized on the horizontal axis: the location distance between the a-th cell and the b-th cell is divided into N equal parts, and the unit step size is Δx. The j-th division point satisfies x j =x a +(j-1)Δx; where x a Let x be the position of the a-th cell. b Let x be the position of the b-th cell. j Let Δx be the position of the j-th division point, where 1 ≤ j ≤ N+1. In fact, the positions of each cell in the battery module can be known during the design phase. Once the value of N is determined, the value of Δx can be obtained. The value of N is also a design value (which can be designed according to specific application requirements). Therefore, Δx can be considered a known value. It should be noted that the position of the j-th division point can be the position of a cell (including sampled cells and non-sampled cells), or it can be any other position besides a cell.

[0057] Temperature transfer time is discretized on the vertical axis: For the battery module, the calculation task (i.e., the temperature prediction program) is executed periodically, and its scheduling period, i.e., the unit time interval Δt, is a design value, such as 100ms, 1000ms, etc.; taking the starting time as time 0, the time corresponding to the calculation task being scheduled m times satisfies t. m = (m-1)Δt.

[0058] Therefore, x in the battery module can be obtained. j Location at t m The temperature at time t is a function (i.e., the temperature equation), which can be approximated as: in, For x j Location at t m The temperature of a moment.

[0059] t m Time is unidirectional, such as from t m to t m +Δt, using the Taylor expansion formula to expand any t with respect to Δt (i.e., performing a one-way Taylor expansion with respect to time), we obtain Formula 1:

[0060]

[0061] This function is approximately expressed as Formula 2 (i.e., the first partial derivative of temperature) can be obtained:

[0062] x j Position has a front and back, such as from x j To x j +Δx and from x j To x j -Δx, using the Taylor expansion formula, expand any x with respect to Δx and -Δx (i.e., perform a two-way Taylor expansion with respect to position), to obtain Formulas 3 and 4:

[0063]

[0064] and

[0065]

[0066] Combining formulas 3 and 4, and approximating the function, we can obtain... Thus, we can obtain Formula 5 (i.e., the second partial derivative of temperature):

[0067] Substituting Equations 2 and 5 into the heat conduction equation, we obtain Equation 6:

[0068] in, For xj Location at t m+1 Temperature at any moment For x j Location at t m Temperature at any moment For x j+1 Location at t m Temperature at any moment For x j-1 Location at t m Temperature at any moment For x j Location at t m The heat source at all times;

[0069] Specifically, two time vectors (t) are established. m Time and t m+1 (Time), transform Formula 6 into matrix form, where,

[0070] The positions of the (N+1) equally spaced points between the a-th cell and the b-th cell are at t m Temperature at any moment The positions of the (N+1) equally spaced points between the a-th cell and the b-th cell are at t m+1 The temperature of a moment.

[0071] Based on location and time, a coefficient matrix related to temperature is set, thereby establishing a (N+1)*(N+1) coefficient matrix A, where the rows represent the influence of time on temperature and the columns represent the influence of location on temperature.

[0072] For example, the coefficient matrix A satisfies:

[0073]

[0074] It should be noted that the coefficient matrix can be set manually based on experience. A in this embodiment is only an illustrative example. In other possible embodiments, the coefficient matrix can also be other values.

[0075] The positions of the (N+1) equally spaced points between the a-th cell and the b-th cell are at t m A constant source of heat.

[0076] Substituting the above matrix into Formula 6, we obtain the recurrence equation.

[0077] More specifically, the method for obtaining the heat sources of all cells in the battery module in this recursive equation includes: providing several sample modules, in which (K+L) cells are all sampled cells; obtaining the heat source of each cell based on the sampling temperature of each cell in the sample modules, and fitting the heat source of each cell to obtain the heat source ratio coefficient of each cell; obtaining the heat source of the corresponding sampled cell based on the sampling temperature of any sampled cell in the battery module, and obtaining the heat source of any non-sampled cell based on the heat source ratio coefficient, thereby obtaining the heat sources of all cells in the battery module.

[0078] In fact, the heat source ratio coefficient of each cell can be obtained during the product development stage, because a special sample module will be produced during the product development stage. This sample module is exactly the same as the battery module in the product testing stage (i.e., the battery module in the embodiment of this application) in terms of cell selection and arrangement. The difference is that the sample module sets temperature sensors on all cells to sample the temperature.

[0079] During the startup phase of the sample module's control program, the sampling temperature of all cells is acquired (which can be directly measured by the temperature sensor at each cell). At a sampling time a certain period later, the sampling temperature of all cells is acquired again. The difference between the two sampling temperatures is used to obtain the temperature change of each cell, and the heat source of each cell is determined based on the formula Q = cmΔT. The same method is used to acquire the heat source of each cell in all sample modules. The heat source proportionality coefficient of each cell in each sample module is obtained by fitting the heat source of each cell in each sample module; where Q is the heat source, c is the specific heat capacity, m is the mass of the cell, and ΔT is the temperature change.

[0080] After obtaining the heat source ratio coefficient of each cell in the sample module, it can be used as the heat source ratio coefficient of each cell in the battery module of this application embodiment.

[0081] When predicting cell temperature during product testing, the same method can be used to obtain the heat source of any sampled cell in the battery module. Then, based on the heat source ratio coefficient, the heat source of any non-sampled cell can be obtained. In this way, the heat sources of all cells in the battery module can be obtained. In fact, it is generally believed that the heat source of each cell in the battery module does not change with time, that is, the heat source of each cell is the same at different times.

[0082] Step S3: Predict the temperature of non-sampled cells in the battery module based on the recursive equation.

[0083] Specifically, the method for predicting the temperature of non-sampled cells in a battery module based on a recursive equation includes: obtaining the initial temperature of all cells in the battery module at the start time; and substituting the initial temperatures of all cells in the battery module at the start time into the recursive equation to predict the temperature of the non-sampled cells in the battery module. The method for obtaining the initial temperature of all cells in the battery module at the start time includes: during the battery module startup phase, obtaining the sampling temperature of each sampled cell in the battery module (which can be directly measured by a temperature sensor located at each sampled cell) as the initial temperature of each sampled cell, and obtaining the initial temperature of each non-sampled cell based on each sampling temperature. In one possible implementation, the mean of each sampling temperature is used as the initial temperature of each non-sampled cell; of course, in other possible implementations, the variance, standard deviation, median, etc., of each sampling temperature can also be used to set the initial temperature of each non-sampled cell.

[0084] In practice, by substituting the initial temperature of all cells in the battery module at the starting moment (e.g., time t0) into the recursive equation, the temperature of all cells in the battery module at time t1 can be obtained. Substituting the temperature of all cells in the battery module at time t1 into the recursive equation, the temperature of all cells in the battery module at time t2 can be obtained, and so on, the temperature of all cells in the battery module at the current moment can be obtained. However, for the sake of prediction accuracy, the recursive equation is generally only used to predict the temperature of each non-sampled cell in the battery module, while the temperature of each sampled cell in the battery module is directly measured by the temperature sensor at each cell.

[0085] by Figure 1 For example, if the initial temperatures of B1-B9 at time t0 are substituted into the recursive equation, the temperatures of B2, B3, and B5-B8 at time t1 are obtained. The temperatures of B1, B4, and B9 at time t1 are measured by the temperature sensors T1-T3 at each cell. If the temperatures of B1-B9 at time t1 are substituted into the recursive equation, the temperatures of B2, B3, and B5-B8 at time t2 are obtained. The temperatures of B1, B4, and B9 at time t2 are measured by the temperature sensors T1-T3 at each cell. In this way, the temperature of each non-sampling cell in the battery module can be predicted using this recursive equation.

[0086] Furthermore, the prediction method also includes step S4: predicting the temperature of the sampled cells in the battery module based on the recursive equation, comparing the predicted temperature and the sampled temperature of the corresponding sampled cells, and determining whether there is a sampling error in the battery module based on the comparison result.

[0087] by Figure 1For example, if the initial temperatures of B1-B9 at time t0 are substituted into the recursive equation, in addition to obtaining the temperatures of B2, B3, and B5-B8 at time t1, the temperature of B4 at time t1 can also be obtained, which is the predicted temperature of B4. The predicted temperature of B4 is compared with the sampled temperature. If the difference between the predicted temperature and the sampled temperature is within an acceptable range, it is determined that the battery module has no sampling error. If the difference between the predicted temperature and the sampled temperature exceeds the acceptable range, it is determined that the battery module has a sampling error, thereby improving the safety and reliability of battery module temperature sampling.

[0088] Correspondingly, such as Figure 3 As shown, this embodiment also provides an electronic device 100, which includes a memory 101 and a processor 102; wherein the memory 101 is used to store a computer program, and the processor 102 is used to execute the computer program stored in the memory 101 so that the electronic device 100 performs the prediction method described above.

[0089] Specifically, memory 101 may include, but is not limited to, high-speed random access memory, non-volatile memory, such as one or more disk storage devices, flash memory devices, or other non-volatile solid-state storage devices.

[0090] The processor 102 can be a general-purpose processor, including one or more central processing units (CPUs), network processors (NPs), etc.; it can also be a microcontroller unit (MCU), digital signal processor (DSP), application specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0091] Accordingly, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the prediction method described above.

[0092] Specifically, computer-readable storage media may include, but are not limited to, floppy disks, optical disks, CD-ROMs (compact disc read-only memory), magneto-optical disks, ROMs (read-only memory), RAMs (random access memory), EPROMs (erasable programmable read-only memory), EEPROMs (electrically erasable programmable read-only memory), magnetic cards or optical cards, flash memory, or other types of media / machine-readable media suitable for storing machine-executable instructions. Furthermore, the computer-readable storage medium may be a product not connected to a computer device or a component used in a computer device.

[0093] In summary, the present invention provides a method, electronic device, and storage medium for predicting the temperature of battery cells in a battery module. By utilizing the sampling temperature of known sampled cells in the battery module, the temperature of non-sampled cells is predicted, thereby obtaining the temperature of all cells in the battery module and enabling accurate monitoring of the battery module temperature. Therefore, the present invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0094] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for predicting the temperature of battery cells in a battery module, characterized in that, The battery module comprises (K+L) cells, of which K are non-sampling cells and L are sampling cells, and K≥1, L≥2; the prediction method includes: Establish the heat conduction equation for the battery module; Based on the positional distance and temperature transfer time between any two sampled cells in the battery module, the heat conduction equation is discretized to obtain the recursive equation for the cell temperature in the battery module. The temperature of the non-sampling cells in the battery module is predicted based on the recursive equation. The recursive equation satisfies: ;in, For all cells in the battery module at t m+1 Temperature at any moment For all cells in the battery module at t m Temperature at any moment A temperature-related coefficient matrix based on location and time. For all cells in the battery module at t m The heat source at all times The unit step size is defined as the distance between the a-th and b-th cells in the battery module when the distance between them is divided into N equal parts, where N ≥ 2. For unit time intervals, It is a constant; The method for obtaining the heat source of all cells in the battery module includes: Several sample modules are provided, wherein (K+L) cells in the sample modules are all sampling cells; The heat source of each cell is obtained based on the sampling temperature of each cell in the sample module, and the heat source ratio coefficient of each cell is obtained by fitting the heat source of each cell. The heat source of the corresponding sampled cell is obtained based on the sampling temperature of any sampled cell in the battery module, and the heat source of any non-sampled cell is obtained based on the heat source ratio coefficient, thereby obtaining the heat source of all cells in the battery module.

2. The method for predicting cell temperature in a battery module according to claim 1, characterized in that, The heat conduction equation satisfies: ; Its boundary conditions satisfy: , , ; in, Let be the first-order partial derivative of the temperature at position x in the battery module at time t. Let be the second partial derivative of the temperature at position x in the battery module at time t. This refers to the heat source at position x in the battery module at time t. Let x be the initial temperature at position x in the battery module at the start time. Let be the temperature of the a-th cell in the battery module at time t. Let be the temperature of the b-th cell in the battery module at time t, and let the a-th and b-th cells be the sampling cells.

3. The method for predicting cell temperature in a battery module according to claim 2, characterized in that, The methods for obtaining the recursive equation include: Discretize the positional distance and temperature transfer time between the a-th cell and the b-th cell in a planar coordinate system to obtain a temperature equation related to position and time; The first-order partial derivative of temperature is obtained by performing a one-way Taylor expansion on time in the temperature equation, and the second-order partial derivative of temperature is obtained by performing a two-way Taylor expansion on position in the temperature equation. Substituting the first-order and second-order partial derivatives of the temperature into the heat conduction equation and then performing a matrix transformation, the recursive equation is obtained.

4. The method for predicting cell temperature in a battery module according to claim 1, characterized in that, The method for predicting the temperature of non-sampling cells in the battery module based on the recursive equation includes: Obtain the initial temperature of all cells in the battery module at the start time; The initial temperature of all cells in the battery module at the start time is substituted into the recursive equation to predict the temperature of the non-sampled cells in the battery module. The method for obtaining the initial temperature of all cells in the battery module at the start time includes: During the battery module startup phase, the sampling temperature of each sampled cell in the battery module is obtained as the initial temperature of each sampled cell, and the initial temperature of each non-sampled cell is obtained based on the sampling temperature, thereby obtaining the initial temperature of all cells in the battery module at the start time.

5. The method for predicting cell temperature in a battery module according to claim 4, characterized in that, The average of the sampled temperatures is used as the initial temperature of each non-sampled cell.

6. The method for predicting cell temperature in a battery module according to claim 1, characterized in that, Before or after predicting the temperature of the non-sampled cells in the battery module, the prediction method further includes: The predicted temperature is obtained by predicting the temperature of the sampled cells in the battery module based on the recursive equation. The predicted temperature of the corresponding sampled cell is compared with its sampled temperature, and the sampling error of the battery module is determined based on the comparison result.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program stored in the memory to enable the electronic device to perform the method for predicting the cell temperature in a battery module as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for predicting the cell temperature in a battery module as described in any one of claims 1-6.

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