A prediction method and a prediction device for the increase of internal resistance of an electric cell

Abstract

An embodiment of the present invention discloses a method and a device for predicting the internal resistance growth of a battery cell. The method for predicting the internal resistance growth of a battery cell includes: testing the storage temperature and the remaining state of charge of the battery cell when stored, which affect the internal resistance growth of the battery cell, and obtaining the growth data of the internal resistance of the battery cell at preset time intervals; constructing a function expression of the remaining state of charge of the battery cell when stored, a function expression of the storage temperature, and a function expression of the remaining state of charge and the storage temperature of the battery cell when stored according to the growth data of the internal resistance of the battery cell, constructing an internal resistance growth model of the battery cell according to the function expression of the remaining state of charge of the battery cell when stored, the function expression of the storage temperature, the function expression of the remaining state of charge and the storage temperature of the battery cell when stored, and the storage days of the battery cell, and predicting the growth of the internal resistance of the battery cell according to the internal resistance growth model. The parameter processing of the internal resistance growth model in the present invention is simple and convenient, and the method for predicting the internal resistance growth of the battery cell is easy to promote and use.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of power batteries, and in particular, to a method and device for predicting the growth of the internal resistance of a battery cell. Background Art

[0002] Currently, there is little research on related models for predicting the DC internal resistance of battery cells in hybrid electric vehicles, and the commonly used electrochemical models have many parameters and complex calculations, making it difficult to widely promote and apply. Summary of the Invention

[0003] The present invention provides a method and device for predicting the growth of the internal resistance of a battery cell, so as to simplify the parameter processing of the internal resistance growth model of the battery cell and make the prediction method of the internal resistance growth of the battery cell easy to promote and use.

[0004] According to one aspect of the present invention, there is provided a method for predicting the growth of the internal resistance of a battery cell, the method for predicting the growth of the internal resistance of the battery cell comprising: testing the storage temperature and the remaining state of charge of the battery cell during storage that affect the growth of the internal resistance of the battery cell, and obtaining the growth data of the internal resistance of the battery cell at preset time intervals;

[0005] Constructing a function expression of the remaining state of charge of the battery cell during storage, a function expression of the storage temperature, and a function expression of the remaining state of charge of the battery cell during storage and the storage temperature according to the growth data of the internal resistance of the battery cell, constructing an internal resistance growth model of the battery cell according to the function expression of the remaining state of charge of the battery cell during storage, the function expression of the storage temperature, the function expression of the remaining state of charge of the battery cell during storage and the storage temperature, and the storage days of the battery cell, and predicting the growth of the internal resistance of the battery cell according to the internal resistance growth model.

[0006] Optionally, the expression of the internal resistance growth model is as follows:

[0007] R

[0007] , increase , B(T) ,

[0009] ,

[0008] , , Z(SOC,T) , , increase , =A(SOC)*e B(T) *t Z(SOC,T)

[0008] wherein, R increase is the internal resistance growth value of the battery cell, t is the storage days of the battery cell, A(SOC) represents a function expression related to the remaining state of charge of the battery cell during storage, B(T) represents a function expression related to the storage temperature, Z(SOC,T) represents a function expression related to the remaining state of charge of the battery cell during storage and the storage temperature, SOC represents the remaining state of charge of the battery cell during storage, and T represents the storage temperature.

[0009] Optionally, constructing the internal resistance growth model of the battery cell based on the function expression of the remaining state of charge of the battery cell during storage, the function expression of the storage temperature, the function expression of the remaining state of charge and the storage temperature of the battery cell during storage, and the storage days of the battery cell includes:

[0010] Converting the expression of the internal resistance growth model of the battery cell into the form of natural logarithm, and obtaining the function relation expression based on the function relation in the rectangular coordinate system through fitting. The function relation expression is as follows:

[0011] lnR increase = lnA(SOC) + B(T) + Z(SOC,T)lnt

[0012] When the storage temperature and the remaining state of charge of the battery cell during storage are determined, lnA(SOC) + B(T) = a, and Z(SOC,T) = b; where a is the vertical intercept in the rectangular coordinate system, b is the slope in the rectangular coordinate system, and both a and b are constants;

[0013] Using the method of plane fitting to fit the equation expressions of a, b with the storage temperature and the remaining state of charge of the battery cell during storage;

[0014] Substituting the equation expressions into the internal resistance growth model of the battery cell to obtain the final expression of the internal resistance growth model of the battery cell.

[0015] Optionally, the function expression of A(SOC) is as follows:

[0016]

[0017] where z1 is a constant, c1 is a constant, d1 is a constant, and SOC is the remaining state of charge of the battery cell during storage.

[0018] Optionally, the function expression of B(T) is as follows:

[0019] B(T) = z2 + c2 / T + d2 / T 2

[0020] where z2 is a constant, c2 is a constant, d2 is a constant, and T is the storage temperature.

[0021] Optionally, the function expression of Z(SOC,T) is as follows:

[0022] Z(SOC,T) = z3 + c3*SOC + d3*SOC 2 + c4*T + d4*T 2

[0023] Wherein, z3 is a constant, c3 is a constant, d3 is a constant, c4 is a constant, d4 is a constant, SOC is the state of charge remaining in the battery cell during storage, and T is the storage temperature.

[0024] Optionally, the final expression of the internal resistance growth model of the battery cell is as follows:

[0025]

[0026] Wherein, R increase is the internal resistance growth value of the battery cell, z4 is a constant, c1 is a constant, d1 is a constant, c2 is a constant, d2 is a constant, z3 is a constant, c3 is a constant, d3 is a constant, c4 is a constant, d4 is a constant, SOC is the state of charge remaining in the battery cell during storage, T is the storage temperature, and t represents the number of storage days of the battery cell.

[0027] Optionally, the testing of the storage temperature and the state of charge remaining in the battery cell that affect the internal resistance growth of the battery cell and obtaining the growth data of the internal resistance of the battery cell at preset time intervals includes:

[0028] Obtaining the growth data of the internal resistance of the battery cell through the variable testing of the storage temperature and the variable testing of the state of charge remaining in the battery cell at preset time intervals.

[0029] Optionally, the number of variables of the storage temperature is not less than three, and the change gradient range of the storage temperature is 10°C - 20°C; the number of variables of the state of charge remaining in the battery cell during storage is not less than three, and the gradient change range of the state of charge remaining in the battery cell during storage is 10% - 20%.

[0030] According to another aspect of the present invention, there is provided a prediction device for the internal resistance growth of a battery cell, and the prediction device for the internal resistance growth of the battery cell includes:

[0031] A data acquisition module, configured to test the storage temperature and the state of charge remaining in the battery cell that affect the internal resistance growth of the battery cell and obtain the growth data of the internal resistance of the battery cell at preset time intervals;

[0032] A model construction module, configured to construct a function expression of the state of charge remaining in the battery cell during storage, a function expression of the storage temperature, a function expression of the state of charge remaining in the battery cell during storage and the storage temperature according to the growth data of the internal resistance of the battery cell, construct the internal resistance growth model of the battery cell according to the function expression of the state of charge remaining in the battery cell during storage, the function expression of the storage temperature, the function expression of the state of charge remaining in the battery cell during storage and the storage temperature, and the number of storage days of the battery cell, and predict the growth of the internal resistance of the battery cell according to the internal resistance growth model.

[0033] In the technical solution of the embodiment of the present invention, by testing the storage temperature that affects the growth of the internal resistance of the battery cell and the remaining state of charge of the battery cell during storage and obtaining the growth data of the internal resistance of the battery cell at preset time intervals; constructing a function expression of the remaining state of charge of the battery cell during storage, a function expression of the storage temperature, and a function expression of the remaining state of charge and storage temperature of the battery cell during storage according to the growth data of the internal resistance of the battery cell, and constructing a model for predicting the growth of the internal resistance of the battery cell according to the function expression of the remaining state of charge of the battery cell during storage, the function expression of the storage temperature, the function expression of the remaining state of charge and storage temperature of the battery cell during storage, and the storage days of the battery cell. The embodiment of the present invention provides a method for establishing a prediction model for the calendar internal resistance growth of battery cells in a hybrid electric vehicle, constructs a model for predicting the growth of the internal resistance of the battery cell, the parameter processing of this model is simple and convenient, the prediction method for the growth of the internal resistance of the battery cell is easy to promote and use, and the effectiveness of the model is verified by actual measurement data. In summary, the embodiment of the present invention solves the problem in the prior art that the electrochemical model has many parameters and complex calculations, making it difficult to be widely promoted and applied.

[0034] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0036] Figure 1 is a flowchart of a method for predicting the growth of the internal resistance of a battery cell according to an embodiment of the present invention;

[0037] Figure 2 is a schematic diagram of the distribution of experimental data according to an embodiment of the present invention;

[0038] Figure 3 is a three-dimensional fitting schematic diagram with an intercept of a according to an embodiment of the present invention;

[0039] Figure 4 is a three-dimensional fitting schematic diagram with a slope of b according to an embodiment of the present invention;

[0040] Figure 5 is a schematic diagram of the structure of a device for predicting the growth of the internal resistance of a battery cell according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0043] Figure 1 is a flowchart of a method for predicting the internal resistance growth of a battery cell provided according to an embodiment of the present invention. Refer to Figure 1 , an embodiment of the present invention provides a method for predicting the internal resistance growth of a battery cell. The method for predicting the internal resistance growth of a battery cell includes:

[0044] S110. Test the storage temperature and the remaining state of charge of the battery cell during storage that affect the internal resistance growth of the battery cell, and obtain the growth data of the internal resistance of the battery cell at preset time intervals;

[0045] Specifically, by analyzing the operating conditions of the battery cell, the influencing factors affecting the calendar internal resistance growth of the battery cell are determined. The influencing factors include the storage temperature T, the remaining state of charge SOC of the battery cell during storage, and the storage days t. Both the storage temperature T and the remaining state of charge SOC of the battery cell during storage are variables, and the storage days t are constant constants. Arrange tests for the determined influencing factors, including different temperatures and different initial SOCs. For example, the number of variables for temperature testing is not less than three, and the temperature gradient is between 10°C and 20°C; the number of variables for SOC testing is not less than three, and the SOC change gradient is between 10% and 20%. Measure the internal resistance growth rate of the battery cell every seven days.

[0046] S120. Construct function expressions for the state of charge remaining during cell storage, function expressions for the storage temperature, and function expressions for the state of charge remaining during cell storage and the storage temperature based on the growth data of the internal resistance of the cell. Construct a cell internal resistance growth model based on the function expressions for the state of charge remaining during cell storage, the function expressions for the storage temperature, the function expressions for the state of charge remaining during cell storage and the storage temperature, and the storage days of the cell. Predict the growth of the cell internal resistance according to the cell internal resistance growth model.

[0047] Specifically, first collect the growth data of the internal resistance of calendar-decaying cells. Then establish a relationship between the storage temperature T, the state of charge SOC remaining in the cell during storage, the storage days t, and the growth value of the cell internal resistance. This relationship is the cell internal resistance growth model, and the model adopts the form of the Arrhenius equation. According to the cell internal resistance growth model, it can be predicted whether the storage performance of the cell meets the requirements.

[0048] The technical solution of the embodiment of the present invention is to test the storage temperature and the state of charge remaining during cell storage that affect the growth of the cell internal resistance and obtain the growth data of the cell internal resistance at preset time intervals; construct function expressions for the state of charge remaining during cell storage, function expressions for the storage temperature, and function expressions for the state of charge remaining during cell storage and the storage temperature based on the growth data of the cell internal resistance. Construct a cell internal resistance growth model based on the function expressions for the state of charge remaining during cell storage, the function expressions for the storage temperature, the function expressions for the state of charge remaining during cell storage and the storage temperature, and the storage days of the cell. Predict the growth of the cell internal resistance according to the cell internal resistance growth model. The embodiment of the present invention proposes a method for establishing a prediction of the calendar internal resistance growth of cells in a hybrid electric vehicle, constructs a cell internal resistance growth model, the parameter processing of this model is simple and convenient, the prediction method of the cell internal resistance growth is easy to promote and use, and the effectiveness of the model is verified by measured data. To sum up, the embodiment of the present invention solves the problem in the prior art that the electrochemical model has many parameters and complex calculations, making it difficult to be widely promoted and applied.

[0049] Optionally, the expression of the cell internal resistance growth model is as follows:

[0050] R increase = A(SOC)*e B(T) *t Z(SOC,T)

[0051] Wherein, R increaseis the growth value of the internal resistance of the battery cell, t is the storage days of the battery cell, A(SOC) represents the function expression related to the remaining state of charge of the battery cell during storage, B(T) represents the function expression related to the storage temperature, Z(SOC,T) represents the function expression related to the remaining state of charge and storage temperature of the battery cell during storage, SOC represents the remaining state of charge of the battery cell during storage, and T represents the storage temperature.

[0052] Figure 2 is a schematic diagram of the distribution of experimental data provided by an embodiment of the present invention, Figure 3 is a three-dimensional fitting schematic diagram with an intercept of a provided by an embodiment of the present invention, Figure 4 is a three-dimensional fitting schematic diagram with a slope of b provided by an embodiment of the present invention, refer to Figure 2 、 Figure 3 and Figure 4 , optionally, constructing a battery cell internal resistance growth model according to the function expression of the remaining state of charge of the battery cell during storage, the function expression of the storage temperature, the function expression of the remaining state of charge and storage temperature of the battery cell during storage, and the storage days of the battery cell includes:

[0053] Convert the expression of the battery cell internal resistance growth model into the form of natural logarithm, and obtain the function relationship expression based on the function relationship in the rectangular coordinate system through fitting. The function relationship expression is as follows:

[0054] lnR increase =lnA(SOC)+B(T)+Z(SOC,T)lnt

[0055] When the storage temperature and the remaining state of charge of the battery cell during storage are determined, lnA(SOC)+B(T)=a, Z(SOC,T)=b; where a is the vertical intercept in the rectangular coordinate system, b is the slope in the rectangular coordinate system, and both a and b are constants;

[0056] Use the method of plane fitting to fit the equation expressions of a, b with the storage temperature and the remaining state of charge of the battery cell during storage;

[0057] Substitute the equation expressions into the battery cell internal resistance growth model to obtain the final expression of the battery cell internal resistance growth model.

[0058] Specifically, convert the growth value R of the internal resistance of the battery cell corresponding to the collected storage temperature T and the remaining state of charge SOC of the battery cell during storage increase into the form of natural logarithm, and then use lnR increase as the ordinate and lnt as the abscissa to plot a graph in the rectangular coordinate system, specifically as Figure 2 shown. Through linear fitting, obtain the function relationship of the above scatter points in the rectangular coordinate system: lnR increase= lnA(SOC) + B(T) + Z(SOC,T)lnt. When the storage temperature T and the state of charge (SOC) of the battery cell at the time of storage are determined, lnA(SOC) + B(T) = a, and Z(SOC,T) = b. By using the data measured at different storage temperatures T and different SOC values, the y-intercept a and the slope b of different linear functions can be obtained. Then, in a three-dimensional rectangular coordinate system, with the storage temperature T as the x-axis, the state of charge (SOC) of the battery cell at the time of storage as the y-axis, and the value of a / b as the z-axis, a scatter plot in the three-dimensional rectangular coordinate system can be created, specifically as shown in Figure 3 and Figure 4 shown. Determine the form of the fitting equation, and use the plane fitting method to fit the equation expression between a / b and the storage temperature T and the state of charge (SOC) of the battery cell at the time of storage. Finally, substitute the obtained equation expression into the battery cell internal resistance growth model to obtain the final expression of the model for predicting the internal resistance growth of the battery cell under calendar storage.

[0059] Optionally, the functional expression of A(SOC) is as follows:

[0060]

[0061] where z1 is a constant, c1 is a constant, d1 is a constant, and SOC is the state of charge of the battery cell at the time of storage.

[0062] Optionally, the functional expression of B(T) is as follows:

[0063] B(T) = z2 + c2 / T + d2 / T 2

[0064] where z2 is a constant, c2 is a constant, d2 is a constant, and T is the storage temperature.

[0065] Optionally, the functional expression of Z(SOC,T) is as follows:

[0066] Z(SOC,T) = z3 + c3*SOC + d3*SOC 2 + c4*T + d4*T 2

[0067] where z3 is a constant, c3 is a constant, d3 is a constant, c4 is a constant, d4 is a constant, SOC is the state of charge of the battery cell at the time of storage, and T is the storage temperature.

[0068] Optionally, the final expression of the battery cell internal resistance growth model is as follows:

[0069]

[0070] where R increaseis the growth value of the internal resistance of the battery cell, z4 is a constant, c1 is a constant, d1 is a constant, c2 is a constant, d2 is a constant, z3 is a constant, c3 is a constant, d3 is a constant, c4 is a constant, d4 is a constant, SOC is the state of charge remaining during the storage of the battery cell, T is the storage temperature, and t represents the number of days of storage of the battery cell.

[0071] Specifically, the Arrhenius equation: k = Ae -Ea / RT The relationship between temperature given in it is 1 / T, so the part about T in the formula of this embodiment uses 1 / T. Substituting the obtained function expressions of A(SOC), B(T), and Z(SOC,T) into the internal resistance growth model of the battery cell can obtain the final expression. Among them, z4 = z1 + z2, and z, c, and d in all formulas of this example are constants, which can be obtained by fitting the measured data. It should be noted that the second-order equation in this embodiment is selected considering the comprehensive accuracy and calculation complexity, and a third-order equation or a fourth-order equation can also be used. The specific order of the equation can be adjusted according to the measured data.

[0072] Optionally, testing the storage temperature and the state of charge remaining during the storage of the battery cell that affect the growth of the internal resistance of the battery cell and obtaining the growth data of the internal resistance of the battery cell at preset time intervals includes: obtaining the growth data of the internal resistance of the battery cell through variable tests of the storage temperature and variable tests of the state of charge remaining during the storage of the battery cell at preset time intervals.

[0073] Optionally, there are no less than three variables of the storage temperature, and the change gradient range of the storage temperature is 10°C - 20°C; there are no less than three variables of the state of charge remaining during the storage of the battery cell, and the gradient change range of the state of charge remaining during the storage of the battery cell is 10% - 20%.

[0074] Specifically, arranging tests for the determined influencing factors, the tests include variable tests of the storage temperature and variable tests of the state of charge remaining during the storage of the battery cell. The variable tests of the storage temperature include different temperatures, and the variable tests of the state of charge remaining during the storage of the battery cell include different initial SOCs. There are no less than three variables in the storage temperature test, and the temperature gradient is between 10°C and 20°C; there are no less than three variables in the SOC test, and the SOC change gradient is between 10% and 20%. The internal resistance growth rate of the battery cell is measured once every seven days.

[0075] Figure 5 is a schematic structural diagram of a prediction device for the growth of the internal resistance of a battery cell provided by an embodiment of the present invention. Refer to Figure 5 , an embodiment of the present invention also provides a prediction device for the growth of the internal resistance of a battery cell. The prediction device for the growth of the internal resistance of a battery cell includes:

[0076] The data acquisition module 201 is configured to test the storage temperature that affects the increase in the internal resistance of the battery cell and the remaining state of charge of the battery cell during storage, and obtain the growth data of the internal resistance of the battery cell at preset time intervals.

[0077] The model construction module 202 is configured to construct a function expression of the remaining state of charge of the battery cell during storage, a function expression of the storage temperature, and a function expression of the remaining state of charge and the storage temperature of the battery cell during storage according to the growth data of the internal resistance of the battery cell, and construct an internal resistance growth model of the battery cell according to the function expression of the remaining state of charge of the battery cell during storage, the function expression of the storage temperature, the function expression of the remaining state of charge and the storage temperature of the battery cell during storage, and the storage days of the battery cell, and predict the increase in the internal resistance of the battery cell according to the internal resistance growth model.

[0078] The above-mentioned prediction device for the increase in the internal resistance of the battery cell can execute the prediction method for the increase in the internal resistance of the battery cell provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the prediction method for the increase in the internal resistance of the battery cell.

[0079] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for predicting the increase in internal resistance of an electric cell, characterized in that, Including: Testing the storage temperature and the state of charge remaining in the battery cell during storage that affect the increase in the internal resistance of the battery cell, and obtaining the growth data of the internal resistance of the battery cell at preset time intervals; Constructing a function expression of the state of charge remaining in the battery cell during storage, a function expression of the storage temperature, and a function expression of the state of charge remaining in the battery cell and the storage temperature according to the growth data of the internal resistance of the battery cell. Constructing a battery cell internal resistance growth model based on the function expression of the state of charge remaining in the battery cell during storage, the function expression of the storage temperature, the function expression of the state of charge remaining in the battery cell and the storage temperature, and the storage days of the battery cell, and predicting the growth of the internal resistance of the battery cell according to the battery cell internal resistance growth model; Among them, constructing the battery cell internal resistance growth model according to the function expression of the state of charge remaining in the battery cell during storage, the function expression of the storage temperature, the function expression of the state of charge remaining in the battery cell and the storage temperature, and the storage days of the battery cell includes: Converting the expression of the battery cell internal resistance growth model into the form of natural logarithm, and obtaining a function relationship expression based on the function relationship in the rectangular coordinate system through fitting. The function relationship expression is as follows: lnR increase = lnA(SOC) + B(T) + Z(SOC,T)lnt wherein, R increase is the growth value of the internal resistance of the battery cell, t is the number of storage days of the battery cell, A(SOC) represents a function expression related to the remaining state of charge during the storage of the battery cell, B(T) represents a function expression related to the storage temperature, Z(SOC,T) represents a function expression related to the remaining state of charge and the storage temperature during the storage of the battery cell, SOC represents the remaining state of charge during the storage of the battery cell, and T represents the storage temperature; When the storage temperature and the state of charge remaining in the battery cell during storage are determined, lnA(SOC)+B(T) = a, Z(SOC,T) = b; where a is the vertical intercept in the rectangular coordinate system, b is the slope in the rectangular coordinate system, and both a and b are constants; Using the method of plane fitting to fit the equation expressions of a, b, the storage temperature, and the state of charge remaining in the battery cell during storage; Substituting the equation expression into the battery cell internal resistance growth model to obtain the final expression of the battery cell internal resistance growth model.

2. The method according to claim 1, wherein The expression of the battery cell internal resistance growth model is as follows: R increase = A(SOC) * e B(T) * t Z(SOC,T) 。 3. The method according to claim 2, characterized in that, The function expression of A(SOC) is as follows: Among them, z1 is a constant, c1 is a constant, d1 is a constant, and SOC is the state of charge remaining in the battery cell during storage.

4. The method according to claim 2, characterized in that, The function expression of B(T) is as follows: B(T) = z2 + c2 / T + d2 / T 2 Among them, z2 is a constant, c2 is a constant, d2 is a constant, and T is the storage temperature.

5. The method according to claim 2, wherein The function expression of Z(SOC,T) is as follows: Z(SOC,T) = z3 + c3*SOC + d3*SOC 2 + c4*T + d4*T 2 Among them, z3 is a constant, c3 is a constant, d3 is a constant, c4 is a constant, d4 is a constant, SOC is the state of charge remaining in the battery cell during storage, and T is the storage temperature.

6. The method according to claim 1, characterized in that, The final expression of the battery cell internal resistance growth model is as follows: Among them, R increase is the internal resistance growth value of the battery cell, z4 is a constant, c1 is a constant, d1 is a constant, c2 is a constant, d2 is a constant, z3 is a constant, c3 is a constant, d3 is a constant, c4 is a constant, d4 is a constant, SOC is the remaining state of charge of the battery cell during storage, T is the storage temperature, and t represents the storage days of the battery cell.

7. The method according to claim 1, characterized in that, The testing the storage temperature and the state of charge remaining in the battery cell during storage that affect the increase in the internal resistance of the battery cell, and obtaining the growth data of the internal resistance of the battery cell at preset time intervals includes: Obtaining the growth data of the internal resistance of the battery cell through variable testing of the storage temperature and variable testing of the state of charge remaining in the battery cell during storage at preset time intervals.

8. The method according to claim 7, wherein The number of variables of the storage temperature is not less than three, and the change gradient interval of the storage temperature is 10°C - 20°C; the number of variables of the state of charge remaining in the battery cell during storage is not less than three, and the gradient change interval of the state of charge remaining in the battery cell during storage is 10% - 20%.

9. A prediction device for the internal resistance growth of an electric cell, characterized in that, Including: A data acquisition module, configured to test the storage temperature affecting the growth of the internal resistance of the battery cell and the remaining state of charge of the battery cell during storage, and obtain the growth data of the internal resistance of the battery cell at preset time intervals; A model construction module, configured to construct a function expression of the remaining state of charge of the battery cell during storage, a function expression of the storage temperature, a function expression of the remaining state of charge of the battery cell during storage and the storage temperature according to the growth data of the internal resistance of the battery cell, and construct a battery cell internal resistance growth model according to the function expression of the remaining state of charge of the battery cell during storage, the function expression of the storage temperature, the function expression of the remaining state of charge of the battery cell during storage and the storage temperature, and the storage days of the battery cell, and predict the growth of the internal resistance of the battery cell according to the battery cell internal resistance growth model; Wherein, the constructing the battery cell internal resistance growth model according to the function expression of the remaining state of charge of the battery cell during storage, the function expression of the storage temperature, the function expression of the remaining state of charge of the battery cell during storage and the storage temperature, and the storage days of the battery cell includes: Converting the expression of the battery cell internal resistance growth model into the form of natural logarithm, and obtaining a function relation expression based on the function relation in the rectangular coordinate system by fitting. The function relation expression is as follows: lnR increase = lnA(SOC) + B(T) + Z(SOC,T)lnt wherein, R increase is the growth value of the internal resistance of the battery cell, t is the number of storage days of the battery cell, A(SOC) represents a function expression related to the remaining state of charge during the storage of the battery cell, B(T) represents a function expression related to the storage temperature, Z(SOC,T) represents a function expression related to the remaining state of charge and the storage temperature during the storage of the battery cell, SOC represents the remaining state of charge during the storage of the battery cell, and T represents the storage temperature; When the storage temperature and the remaining state of charge of the battery cell during storage are determined, lnA(SOC)+B(T)=a, Z(SOC,T)=b; where a is the vertical intercept in the rectangular coordinate system, b is the slope in the rectangular coordinate system, and both a and b are constants; Adopting a plane fitting method to fit the equation expressions of a, b with the storage temperature and the remaining state of charge of the battery cell during storage; Substituting the equation expressions into the battery cell internal resistance growth model to obtain the final expression of the battery cell internal resistance growth model.

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