Battery cell characteristic analysis method, device and equipment

Abstract

The present application relates to the field of battery management technology, and specifically to a method, device, equipment and computer storage medium for analyzing battery characteristics. The battery characteristics analysis method uses an adaptive algorithm to identify the equivalent circuit model parameters of the battery online, dynamically updates the equivalent circuit model parameters, so that the initial parameters of the model obtained can accurately reflect the actual state of the battery, and further improves the accuracy of the battery characteristics analysis by obtaining the residual value between the initial parameters and the estimated parameters, and updating and iterating the performance parameters through the LM algorithm according to the residual value, so that the predicted performance parameters can more accurately reflect the actual state of the battery.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of battery management technology, and specifically to a method, device, equipment and computer storage medium for analyzing battery cell characteristics. Background Art

[0002] With the rapid development of electric vehicles, energy storage systems, etc., the management and monitoring of battery performance has become increasingly important. Existing battery management systems usually estimate the battery status through a preset model to determine the battery characteristics.

[0003] The inventors of this application found in their research that most existing battery models are based on ideal experimental conditions and cannot accurately reflect the changes in battery performance in actual use environments. Many battery parameters are preset and lack the ability to make adaptive adjustments based on actual use conditions. It is difficult to deal with the problem that battery performance changes over time and in the use environment, resulting in large errors in the estimation results. Existing battery management systems often cannot monitor and update these changes in real time, affecting the accurate estimation of battery status. Summary of the invention

[0004] In view of the above problems, the embodiments of the present application provide a battery cell characteristic analysis method, device, equipment and computer storage medium to solve the above technical problems existing in the prior art.

[0005] According to one aspect of an embodiment of the present application, a method for analyzing battery cell characteristics is provided, the method comprising:

[0006] S1: discharging the battery cell with a first discharge current I to obtain a first voltage value V1;

[0007] S2: Acquire a second voltage value V2 when the battery cell is discharged;

[0008] S3: Obtaining a third voltage value V3 after t1 seconds of the battery cell being discharged;

[0009] S4: Obtain the stable voltage value V4 after the discharge is completed;

[0010] S5: determining the polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I; determining the time constant τ of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2;

[0011] S6: determining the performance parameters, damping factor and damping factor update coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant τ; determining the discharge response function corresponding to the battery cell according to the performance parameters;

[0012] S7: obtaining a first real-time voltage value of the battery cell, and predicting the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record; obtaining a first residual value between the first real-time voltage value and the first voltage prediction record;

[0013] S8: updating the performance parameter by using the LM algorithm according to the first residual value and the damping factor;

[0014] S9: acquiring a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record;

[0015] S10: if the square value of the second residual value is less than or equal to the square value of the first residual value, the damping factor is reduced according to the damping factor update coefficient; if the square value of the second residual value is greater than the square value of the first residual value, the damping factor is amplified according to the damping factor update coefficient;

[0016] S11: According to the processed damping factor and the updated performance parameter, repeating steps S7 to S10 to iteratively update the performance parameter of the battery cell until the performance parameter meets a preset convergence condition.

[0017] Preferably, determining the discharge response function corresponding to the battery cell according to the performance parameter includes:

[0018] The discharge response function is determined by the following formula :

[0019] ;

[0020] Where b = IR1, .

[0021] Preferably, updating the performance parameter by using a LM algorithm according to the first residual value and the damping factor includes:

[0022] Calculating the partial derivative of the first residual value with respect to the performance parameter to form a Jacobian matrix;

[0023] Generate a Hessian matrix and a gradient vector according to the Jacobian matrix;

[0024] The performance parameter is updated according to the Hessian matrix, the gradient vector and the damping factor.

[0025] Preferably, the calculating the partial derivative of the first residual value with respect to the performance parameter to form a Jacobian matrix includes:

[0026] The Jacobian matrix is ​​determined by the following formula :

[0027] ;

[0028] in, , b = IR1, .

[0029] Preferably, updating the performance parameter according to the Hessian matrix, the gradient vector and the damping factor comprises:

[0030] The performance parameters are updated by the following formula:

[0031] ;

[0032] in is the current performance parameter vector, the Hessian matrix , the gradient vector r, wherein r is the first residual value, is the damping factor;

[0033] Current performance parameters: ;

[0034] Updated performance parameters: ;

[0035] Through matrix gradient operation, we get:

[0036] ;

[0037] The updated performance parameters are:

[0038] ;

[0039] .

[0040] Preferably, the damping factor is updated according to the damping factor update coefficient. Perform minification, including:

[0041] ;

[0042] The amplifying the damping factor according to the damping factor update coefficient includes:

[0043] ;

[0044] in, is the damping factor update coefficient, is a positive integer greater than 1.

[0045] Preferably, the preset convergence condition is: the first residual value and the second residual value are smaller than a preset residual threshold; or, the difference between the performance parameter and the updated performance parameter is smaller than a preset amplitude threshold.

[0046] According to one aspect of an embodiment of the present application, a battery cell characteristic analysis device is also provided, the device comprising:

[0047] Parameter acquisition module: used to discharge the battery cell with a first discharge current I to obtain a first voltage value V1; obtain a second voltage value V2 when the battery cell is discharged; obtain a third voltage value V3 t1 seconds after the discharge of the battery cell is completed; obtain a stable voltage value V4 after the discharge is completed; and also used to determine the polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I; determine the time constant τ of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2; and determine the performance parameters, damping factor and damping factor update coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant τ, and determine the discharge response function corresponding to the battery cell according to the performance parameters;

[0048] A first residual value determination module: used to obtain a first real-time voltage value of the battery cell, and predict the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record; obtain a first residual value between the first real-time voltage value and the first voltage prediction record;

[0049] A performance parameter updating module: used for updating the performance parameter by using the LM algorithm according to the first residual value and the damping factor;

[0050] A second residual value determination module: used to obtain a second real-time voltage value of the battery cell, determine a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determine a second residual value according to the second real-time voltage value and the second voltage prediction record;

[0051] Damping factor update module: used to determine whether the performance parameter meets the preset convergence condition. If the preset convergence condition is not met, when the square value of the second residual value is less than the square value of the first residual value, the damping factor is reduced according to the damping factor update coefficient; when the square value of the second residual value is greater than the square value of the first residual value, the damping factor is amplified according to the damping factor update coefficient.

[0052] According to a third aspect of an embodiment of the present application, a battery cell characteristic analysis device is also proposed, including: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0053] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the battery cell characteristic analysis method described in any one of the above embodiments.

[0054] According to the fourth aspect of the embodiments of the present application, a computer-readable storage medium is also proposed, wherein at least one executable instruction is stored in the storage medium. When the executable instruction is run on a battery cell characteristic analysis device, the battery cell characteristic analysis device executes the battery cell characteristic analysis method described in any one of the above embodiments.

[0055] As can be seen from the above, the embodiments of the present application propose a method, device and equipment for analyzing battery cell characteristics, which uses an adaptive algorithm to identify the equivalent circuit model parameters of the battery online, and dynamically updates the equivalent circuit model parameters, so that the initial parameters of the model obtained can accurately reflect the real state of the battery. At the same time, by obtaining the residual value between the initial parameters and the estimated parameters, and updating and iterating the performance parameters through the LM algorithm according to the residual value, the accuracy of the battery cell characteristic analysis is further improved, so that the predicted performance parameters can more accurately reflect the real state of the battery cell. Furthermore, in the process of analyzing the battery cell characteristics, the way to update the damping factor is determined by judging the size between the square value of the first residual value and the square value of the second residual value, so that the process of predicting the battery cell performance parameters using the prediction model is more targeted, and the prediction model can have a faster convergence speed and better calculation stability.

[0056] The above description is only an overview of the technical solution of the embodiment of the present application. In order to more clearly understand the technical means of the embodiment of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present application. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0058] Figure 1 A schematic diagram of a cell characteristics analysis method provided in an embodiment of the present application;

[0059] Figure 2 An equivalent circuit diagram of a battery cell provided in an embodiment of the present application;

[0060] Figure 3 A schematic diagram of the voltage response of a battery cell under an excitation current provided in an embodiment of the present application;

[0061] Figure 4 A functional block diagram of a battery cell characteristic analysis device provided in an embodiment of the present application;

[0062] Figure 5 A schematic diagram of the structure of a battery cell characteristic analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0064] The cell is the core part of the battery, and cell performance analysis plays a very important role in battery management. Cell performance analysis can ensure safe battery operation, extend battery life, optimize battery performance and efficiency, and achieve real-time monitoring and fault prediction. Cell performance analysis usually includes analyzing the battery's capacity, voltage, current, impedance, etc. to determine the health status of the cell, so that the battery management system can manage the battery according to the health status of the cell.

[0065] The applicant has found in the research that the existing battery cell parameter identification technology often adopts a static model method, which cannot adapt to the changes in battery cell parameters during actual use, such as aging effects and temperature changes, which will lead to insufficient accuracy of parameter identification results. Moreover, these methods are highly dependent on the initial model parameters, and improper initial parameter settings may lead to inaccurate estimation results.

[0066] In view of this, the embodiments of the present application propose a method, device and equipment for analyzing the characteristics of a battery cell. The method for analyzing the characteristics of a battery cell proposed in the embodiments of the present application adopts an adaptive algorithm to identify the equivalent circuit model parameters of the battery online, and dynamically updates the equivalent circuit model parameters, so that the initial parameters of the model obtained can accurately reflect the real state of the battery. At the same time, by obtaining the residual value between the initial parameter and the estimated parameter, and updating and iterating the performance parameters through the LM algorithm according to the residual value, the accuracy of the analysis of the characteristics of the battery cell is further improved, so that the predicted performance parameters can more accurately reflect the real state of the battery cell. Furthermore, in the process of analyzing the characteristics of the battery cell, the way to update the damping factor is determined by judging the size between the square value of the first residual value and the square value of the second residual value, so that the process of predicting the performance parameters of the battery cell using the prediction model is more targeted, and the prediction model can have a faster convergence speed and better calculation stability.

[0067] like Figure 1 FIG. 1 is a flow chart of a method for analyzing characteristics of a battery cell proposed in an embodiment of the present application, and the method comprises the following steps:

[0068] S1: discharging the battery cell with a first discharge current I to obtain a first voltage value V1;

[0069] S2: Acquire a second voltage value V2 when the battery cell is discharged;

[0070] S3: Obtaining a third voltage value V3 after t1 seconds of the battery cell being discharged;

[0071] S4: Obtain the stable voltage value V4 after the discharge is completed;

[0072] S5: determining the polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I; determining the time constant τ of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2;

[0073] In step S1 to step S5, the process of obtaining the initial parameters of the battery cell is shown. In the embodiment of the present application, in order to accurately obtain the initial parameters of the battery cell, an adaptive algorithm is used to online identify the parameters of the equivalent circuit model of the battery, such as Figure 2As shown, it is a first-order RC equivalent circuit of the battery cell shown in the embodiment of the present application, wherein R0 is the internal resistance of the battery cell, R1 is the polarization resistance, and C1 capacitor represents the capacitance of the battery cell. In this first-order RC equivalent circuit, when the battery cell is charged, the capacitor will charge, and the voltage will gradually increase until the capacitor is fully charged. At this time, the current in the circuit will gradually decrease because the capacitor no longer absorbs current. Therefore, at the beginning of the capacitor charging, the current in the circuit is large, and the current gradually decreases when the capacitor is charged to half, and the current is almost zero when the capacitor is fully charged. The voltage in the capacitor changes over time. In the first-order RC equivalent circuit, the time constant τ represents the constant of the time process of the transition reaction, which refers to the time required for the physical quantity to decay from the maximum value to 1 / e of the maximum value. Through this first-order RC equivalent circuit, it can be used to describe the charging and discharging process of the lithium battery, as well as to analyze the dynamic response and steady-state response of the lithium battery.

[0074] Figure 3 The response curve of the voltage in the first-order RC equivalent circuit is shown, and the change of the voltage in each stage during the discharge of the battery cell with the first discharge current I is shown. The embodiment of the present application obtains the corresponding voltage in each stage based on the first-order RC equivalent circuit as a model, so that the initial parameters of the battery cell can be obtained in real time and accurately.

[0075] exist Figure 2 and Figure 3 In the figure, V is the detection voltage, and I is the first discharge current, i.e., the excitation current. When the cell is discharged with the first discharge current I, the voltage of the cell will drop significantly at the beginning of the discharge. After the discharge lasts for N seconds, where N is preferably greater than 100 seconds, the capacitor C can be fully charged. The voltage V1 is recorded as the first voltage value, i.e., the first voltage V1 is the voltage before the discharge of the cell ends. Figure 3 It can be seen that V1 is the lowest voltage at this time.

[0076] Continue to refer Figure 3 After the discharge current ends, the capacitor C is fully charged and the voltage is rising. The voltage V2 is recorded. After the discharge of the battery cell ends t1 seconds later, preferably t1 is 100 seconds, the third voltage V3 is recorded. When the battery cell is in a stable state after the discharge ends, and the detected voltage does not change much, the stable voltage value V4 is recorded.

[0077] according to Figure 2 The first-order RC equivalent circuit, when the first discharge current I disappears, the voltage change full response function is:

[0078] (Formula 1);

[0079] (Formula 2);

[0080] (Formula 3);

[0081] (Formula 4);

[0082] From formula 3 and formula 4, we can get:

[0083] ,

[0084] That is, the time constant for:

[0085] (Formula 5);

[0086] Where t1 is the discharge end time.

[0087] The polarization resistance R1 is:

[0088] (Formula 6);

[0089] When the battery system runs the HPPC hybrid pulse power characteristic test condition, after each excitation pulse, all the parameters required for the first-order RC equivalent model can be obtained by obtaining voltage and current data according to the above steps.

[0090] The embodiment of the present application analyzes the voltage response curve and identifies the time constant of the battery cell online. In this way, the model parameters can be dynamically adjusted according to real-time data, the time constant can be dynamically updated, and the recognition efficiency of performance parameters can be improved.

[0091] S6: determining the performance parameters, damping factor and damping factor update coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant τ, and determining the discharge response function corresponding to the battery cell according to the performance parameters;

[0092] In the above steps S1-S5, after the polarization resistance R1 and the time constant τ are determined according to the first-order RC equivalent circuit, it is further necessary to determine the performance parameters, damping factor and damping factor update coefficient of the battery cell according to the polarization resistance R1 and the time constant τ.

[0093] Among them, the polarization resistance R1 and the time constant τ are all used as part of the battery performance parameters. In the embodiment of the present application, in order to facilitate the use of the above battery performance parameters in the subsequent model, it is necessary to convert the polarization resistance R1 and the time constant τ to obtain performance parameters a and b. According to formula 1, it can be known that:

[0094] RC discharge response function for:

[0095] (Formula 7);

[0096] in, , , t is the sampling time;

[0097] According to Formula 7, the discharge response function Transformed into:

[0098] (Formula 8);

[0099] The damping factor is an empirical value estimated based on the performance parameters measured by the first-order RC equivalent circuit. Since the damping factor will be updated in the subsequent prediction model, the initial damping factor can be estimated based on experience at this stage, which will not affect the accuracy of the subsequent estimation of the performance parameters. is a preset coefficient for updating the damping factor in the future. The determination of this coefficient will affect the number of iterations of the prediction model. Preferably, is a positive integer greater than 1. Preferably, the initial damping factor Set to 10, the damping factor update coefficient Set to 10.

[0100] S7: obtaining a first real-time voltage value of the battery cell, predicting the voltage value of the battery cell according to the discharge response function, and generating a first voltage prediction record; obtaining a first residual value between the first real-time voltage value and the first voltage prediction record;

[0101] In the above steps, after the initial performance parameters of the battery cell are determined by the first-order RC equivalent circuit model, according to the above formula 1, the data record of the first real-time voltage value of the acquisition system after the charging and discharging is completed, starting at an interval of 1 second, is shown in Table 1 below. The theoretical parameters τ=10, R1=0.0005, I=100A, b=IR1=0.02, a=-1 / τ=-0.01, and the actual voltage value obtained is:

[0102] Table 1

[0103]

[0104] At the same time, the voltage value of the battery cell is predicted according to Formula 8 to generate a first voltage prediction record: According to the first real-time voltage value and the first voltage prediction record in Table 1, the first residual value between the first real-time voltage value and the first voltage prediction record can be obtained: , for each data point i, the residual between the predicted value and the actual recorded value of the discharge response function is calculated according to the following formula 9.

[0105] (Formula 9);

[0106] in, The voltage change value at each time point recorded in Table 1 .

[0107] S8: updating the performance parameter by using the LM algorithm according to the first residual value and the damping factor;

[0108] The LM algorithm is an optimization algorithm for nonlinear least squares problems. By combining the advantages of the Gauss-Newton method and the gradient descent method, it can quickly and accurately find the optimal parameters that minimize the loss function. The LM algorithm controls the behavior between the Gauss-Newton method and the gradient descent method through the damping factor λ during the iteration process. When the damping factor is greater than a certain value, the LM algorithm is similar to the Gauss-Newton method; when the damping factor is less than a certain value, the LM algorithm tends to the gradient descent method. In the embodiment of the present application, by introducing the LM algorithm into the analysis of battery cell characteristics, the characteristics of the LM algorithm can be fully utilized, so that the analysis of battery cell characteristics can converge quickly and the stability of the calculation can be maintained.

[0109] From the above step S7, it can be seen that the first residual value is , the damping factor is , calculate the partial derivative of the first residual value with respect to the performance parameter to form a Jacobian matrix; for each data point t, calculate the partial derivative of the model with respect to the performance parameters a and b, and obtain the following Jacobian matrix:

[0110] ,in ;

[0111] Generate the Hessian matrix and gradient vector according to the Jacobian matrix to obtain the Hessian matrix and the gradient vector r, wherein r is the first residual value in the above step.

[0112] The performance parameter is updated according to the Hessian matrix, the gradient vector and the damping factor by the following formula 10:

[0113] (Formula 10);

[0114] in is the current performance parameter vector, the Hessian matrix , the gradient vector r, wherein r is the first residual value, is the damping factor;

[0115] Current performance parameters: ;

[0116] Updated performance parameters: ;

[0117] Through matrix gradient operation, we get:

[0118] ;

[0119] The updated performance parameters are:

[0120] ;

[0121] .

[0122] S9: acquiring a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record;

[0123] After obtaining the updated performance parameters, the second voltage prediction record is generated in the same manner as in step S7, and the residual determined according to the updated performance parameters is generated by formula nine according to the collected second real-time voltage value. ,in .

[0124] S10: if the square value of the second residual value is less than or equal to the square value of the first residual value, the damping factor is reduced according to the damping factor update coefficient; if the square value of the second residual value is greater than the square value of the first residual value, the damping factor is amplified according to the damping factor update coefficient;

[0125] like Figure 1 As shown, it is determined whether the square value of the second residual value is greater than the square value of the first residual value. When the square value of the second residual value is less than or equal to the square value of the first residual value, it means that the error is decreasing, and step S101 is executed, that is, the coefficient is updated according to the damping factor. The damping factor The method is reduced to make it close to Newton's method. , which reduces the damping factor times to reduce the iterative step size.

[0126] When the square value of the second residual value is greater than the square value of the first residual value, it means that the error is increasing, and then step S102 is executed, that is, the coefficient is updated according to the damping factor. The damping factor Amplify it to make it close to the gradient descent method. Preferably, in the embodiment of the present application, , that is, the damping factor is amplified times to increase the iterative step size.

[0127] The embodiment of the present application achieves efficient and stable parameter estimation in nonlinear least squares optimization by introducing a strategy for dynamically adjusting the damping factor λ according to the residual value. Compared with the traditional method, the LM algorithm provided in the embodiment of the present application provides better convergence speed and calculation stability.

[0128] S11: According to the processed damping factor and the updated performance parameter, repeating steps S7 to S10 to iteratively update the performance parameter of the battery cell until the performance parameter meets a preset convergence condition.

[0129] Continue to refer Figure 1 , determine whether the updated performance parameters meet the preset convergence conditions, wherein the preset convergence conditions may be that the first residual value and the second residual value are less than the preset residual threshold; when the first residual value or the second residual value is less than the residual threshold, it means that the actual voltage value and the predicted voltage value are close, and the difference between the two is not large, then step S12 may be executed, that is, the battery cell characteristic analysis process is terminated and the performance parameter update is stopped. Otherwise, continue to execute steps S7 to S10, and continue to iteratively update the performance parameters of the battery cell.

[0130] Optionally, the preset convergence condition may also be that the difference between the performance parameter and the updated performance parameter is less than a preset amplitude threshold. When the difference between the two is less than the preset amplitude threshold, it means that the performance parameter has not changed much, and step S12 may be executed, that is, the battery cell characteristic analysis process is terminated and the performance parameter update is stopped. Otherwise, steps S7 to S10 are continued to iteratively update the performance parameters of the battery cell.

[0131] As shown in Table 2 below, the performance parameters after multiple iterations based on the data in Table 1 are as follows:

[0132] Table 2

[0133]

[0134] It can be seen from Table 2 that after 6 iterations, the predicted performance parameters are a=-0.1010, b=0.0499. , The predicted time constant is τ=9.9 and the polarization resistance is R1=0.0004999. From Table 1, the theoretical parameters τ=10 and R1=0.0005 are very close to the predicted performance parameters and the theoretical performance parameters, which also proves the accuracy of the battery cell characteristic analysis method proposed in the embodiment of the present application.

[0135] In summary, the battery cell characteristic analysis method proposed in the embodiment of the present application adopts the first-order RC equivalent circuit model parameters and dynamically updates the equivalent circuit model parameters, so that the initial parameters of the obtained model can accurately reflect the real state of the battery. At the same time, by obtaining the residual value between the initial parameters and the estimated parameters, and updating and iterating the performance parameters through the LM algorithm according to the residual value, the accuracy of the battery cell characteristic analysis is further improved, so that the predicted performance parameters can more accurately reflect the real state of the battery cell. Furthermore, in the process of analyzing the battery cell characteristics, the way to update the damping factor is determined by judging the size between the square value of the first residual value and the square value of the second residual value, so that the process of predicting the battery cell performance parameters using the prediction model is more targeted, and the prediction model can have a faster convergence speed and better calculation stability.

[0136] According to another aspect of the embodiment of the present application, Figure 4 As shown, an embodiment of the present application also provides a battery cell characteristic analysis device 100, which is used to execute the battery cell characteristic analysis method proposed in any of the above embodiments. The battery cell characteristic analysis device 100 includes a parameter acquisition module 101, a first residual value determination module 102, a performance parameter update module 103, a second residual value determination module 104 and a damping factor update module 105.

[0137] Wherein, the parameter acquisition module 101 is used to discharge the battery cell with a first discharge current I to obtain a first voltage value V1; obtain a second voltage value V2 when the battery cell is discharged; obtain a third voltage value V3 t1 seconds after the discharge of the battery cell is completed; obtain a stable voltage value V4 after the discharge is completed; and is also used to determine the polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I; determine the time constant τ of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2; and determine the performance parameters, damping factor and damping factor update coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant τ, and determine the discharge response function corresponding to the battery cell according to the performance parameters;

[0138] The first residual value determination module 102 is used to obtain a first real-time voltage value of the battery cell, and predict the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record; obtain a first residual value between the first real-time voltage value and the first voltage prediction record;

[0139] The performance parameter updating module 103 is used to update the performance parameter by using the LM algorithm according to the first residual value and the damping factor;

[0140] The second residual value determination module 104 is used to obtain a second real-time voltage value of the battery cell, determine a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determine a second residual value according to the third voltage value and the second voltage prediction record;

[0141] The damping factor updating module 105 is used to determine whether the performance parameter meets the preset convergence condition. If the preset convergence condition is not met, when the square value of the second residual value is smaller than the square value of the first residual value, the damping factor is reduced according to the damping factor updating coefficient; when the square value of the second residual value is larger than the square value of the first residual value, the damping factor is amplified according to the damping factor updating coefficient.

[0142] The present application also provides a battery cell characteristic analysis device. Figure 5 , Figure 5 A schematic structural diagram of a battery cell characteristic analysis device is shown.

[0143] like Figure 5 As shown, the target detection and determination device may include: a processor 602 , a memory 606 , a communication interface 604 and a communication bus 608 .

[0144] The processor 602, the memory 606 and the communication interface 604 communicate with each other via a communication bus 608. The memory 606 is used to store at least one program 610, and the program 610 enables the processor 602 to execute the relevant steps in the above-mentioned embodiment of the battery cell characteristic analysis method.

[0145] Specifically, the program 610 may include program code including computer executable instructions.

[0146] The processor 602 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the battery cell characteristic analysis device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0147] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0148] The program 610 can be specifically called by the processor 602 to enable the battery cell characteristic analysis device to perform any operation method provided in the above battery cell characteristic analysis method embodiment, specifically including the following operations:

[0149] S1: discharging the battery cell with a first discharge current I to obtain a first voltage value V1;

[0150] S2: Acquire a second voltage value V2 when the battery cell is discharged;

[0151] S3: Obtaining a third voltage value V3 after t1 seconds of the battery cell being discharged;

[0152] S4: Obtain the stable voltage value V4 after the discharge is completed;

[0153] S5: determining the polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I; determining the time constant τ of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2;

[0154] S6: determining the performance parameters, damping factor and damping factor update coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant τ; determining the discharge response function corresponding to the battery cell according to the performance parameters;

[0155] S7: obtaining a first real-time voltage value of the battery cell, and predicting the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record; obtaining a first residual value between the first real-time voltage value and the first voltage prediction record;

[0156] S8: updating the performance parameter by using the LM algorithm according to the first residual value and the damping factor;

[0157] S9: acquiring a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record;

[0158] S10: if the square value of the second residual value is less than or equal to the square value of the first residual value, the damping factor is reduced according to the damping factor update coefficient; if the square value of the second residual value is greater than the square value of the first residual value, the damping factor is amplified according to the damping factor update coefficient;

[0159] S11: According to the processed damping factor and the updated performance parameter, repeating steps S7 to S10 to iteratively update the performance parameter of the battery cell until the performance parameter meets a preset convergence condition.

[0160] An embodiment of the present application further provides a computer-readable storage medium, in which executable instructions are stored. When the executable instructions are executed on a battery cell characteristic analysis device, the battery cell characteristic analysis device executes the battery cell characteristic analysis method provided in any of the above embodiments.

[0161] An embodiment of the present application also provides a battery cell characteristic analysis program, which is used to execute the battery cell characteristic analysis method provided in the above embodiment.

[0162] The algorithm or display provided here are not inherently related to any specific computer, virtual system or other equipment. Various general systems can also be used together with the teaching based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is to disclose the best mode of implementation of the present application.

[0163] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0164] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various aspects of the invention, in the above description of exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof.

[0165] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying abstract and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying abstract and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0166] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and those skilled in the art may design alternative embodiments without departing from the scope. The steps in the above embodiments, unless otherwise specified, should not be understood as limiting the execution order.

Claims

1. A method for analyzing battery cell characteristics, characterized in that: include: S1: discharging the battery cell with a first discharge current I to obtain a first voltage value V1; S2: Acquire a second voltage value V2 when the battery cell is discharged; S3: Obtaining a third voltage value V3 after t1 seconds of the battery cell being discharged; S4: Obtain the stable voltage value V4 after the discharge is completed; S5: determining the polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I; determining the time constant τ of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2; S6: determining the performance parameters, damping factor and damping factor update coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant τ; determining the discharge response function corresponding to the battery cell according to the performance parameters; S7: obtaining a first real-time voltage value of the battery cell, and predicting the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record; obtaining a first residual value between the first real-time voltage value and the first voltage prediction record; S8: updating the performance parameter by using the LM algorithm according to the first residual value and the damping factor; S9: acquiring a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record; S10: if the square value of the second residual value is less than or equal to the square value of the first residual value, reducing the damping factor according to the damping factor update coefficient; If the square value of the second residual value is greater than the square value of the first residual value, amplifying the damping factor according to the damping factor update coefficient; S11: According to the processed damping factor and the updated performance parameter, repeating steps S7 to S10 to iteratively update the performance parameter of the battery cell until the performance parameter meets a preset convergence condition.

2. The method for analyzing battery cell characteristics according to claim 1, characterized in that: Determining the discharge response function corresponding to the battery cell according to the performance parameter includes: The discharge response function is determined by the following formula : ； Where b = IR1, , t is the sampling time.

3. The method for analyzing battery cell characteristics according to claim 1, characterized in that: The updating of the performance parameter by using the LM algorithm according to the first residual value and the damping factor includes: Calculating the partial derivative of the first residual value with respect to the performance parameter to form a Jacobian matrix; Generate a Hessian matrix and a gradient vector according to the Jacobian matrix; The performance parameter is updated according to the Hessian matrix, the gradient vector and the damping factor.

4. The method for analyzing battery cell characteristics according to claim 3, characterized in that: The calculating the partial derivative of the first residual value with respect to the performance parameter to form a Jacobian matrix includes: The Jacobian matrix is ​​determined by the following formula : ； in, , b = IR1, , t is the sampling time.

5. The method for analyzing battery cell characteristics according to claim 4, characterized in that: Updating the performance parameter according to the Hessian matrix, the gradient vector and the damping factor includes: The performance parameters are updated by the following formula: ； in is the current performance parameter vector, the Hessian matrix , the gradient vector r, wherein r is the first residual value, is the damping factor; Current performance parameters: ; Updated performance parameters: ; Through matrix gradient operation, we get: ； The updated performance parameters are: ； 。 6. The method for analyzing battery cell characteristics according to claim 5, characterized in that: The damping factor is updated according to the damping factor update coefficient. Perform minification, including: ； The amplifying the damping factor according to the damping factor update coefficient includes: ； in, is the damping factor update coefficient, is a positive integer greater than 1.

7. The method for analyzing battery cell characteristics according to claim 1, characterized in that: The preset convergence condition is: the first residual value and the second residual value are less than a preset residual threshold; or the difference between the performance parameter and the updated performance parameter is less than a preset amplitude threshold.

8. A battery cell characteristic analysis device, characterized in that: include: Parameter acquisition module: used to discharge the battery cell with a first discharge current I to obtain a first voltage value V1; obtain a second voltage value V2 when the battery cell is discharged; obtain a third voltage value V3 t1 seconds after the discharge of the battery cell is completed; and obtain a stable voltage value V4 after the discharge is completed; It is also used to determine the polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I; determine the time constant τ of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2; and determine the performance parameters, damping factor and damping factor update coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant τ, and determine the discharge response function corresponding to the battery cell according to the performance parameters; A first residual value determination module: used to obtain a first real-time voltage value of the battery cell, and predict the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record; obtain a first residual value between the first real-time voltage value and the first voltage prediction record; A performance parameter updating module: used for updating the performance parameter by using the LM algorithm according to the first residual value and the damping factor; A second residual value determination module: used to obtain a second real-time voltage value of the battery cell, determine a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determine a second residual value according to the second real-time voltage value and the second voltage prediction record; Damping factor updating module: used for judging whether the performance parameter satisfies a preset convergence condition. If the preset convergence condition is not satisfied, when the square value of the second residual value is less than the square value of the first residual value, the damping factor is reduced according to the damping factor updating coefficient; When the square value of the second residual value is greater than the square value of the first residual value, the damping factor is amplified according to the damping factor update coefficient.

9. A battery cell characteristic analysis device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the battery cell characteristic analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one executable instruction. When the executable instruction is executed on the battery cell characteristic analysis device, the battery cell characteristic analysis device executes the battery cell characteristic analysis method according to any one of claims 1 to 7.

Images