Lithium ion battery actual capacity estimation method applied to incomplete constant voltage working condition

Abstract

The invention discloses a lithium ion battery actual capacity estimation method applied to an incomplete constant-voltage working condition. The method comprises the following steps: determining a battery model structure offline; identifying battery model parameters corresponding to different data lengths; further determining an effective data length interval, and determining and normalizing a characteristic variable representing battery capacity attenuation at the same time; constructing and storing actual battery capacity estimation models corresponding to different constant-voltage charging data lengths; recording and storing a constant-voltage charging time-current sequence of the battery in real time; after the charging process is finished, identifying to obtain a characteristic variable and carrying out normalization conversion; inquiring to obtain a battery actual capacity estimation model corresponding to the actual constant-voltage charging data; and calculating the actual capacity value of the battery. Compared with a battery actual capacity estimation method based on dynamic discharge data and constant current charge data, the estimation method provided by the invention has relatively high robustness.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and in particular relates to a method for estimating the actual capacity of a lithium-ion battery applied to a non-complete constant voltage working condition. Background Art

[0002] Efficient and accurate estimation of the state of health (SoH) of lithium-ion batteries is crucial to the safe and reliable operation of battery systems. Existing battery SoH estimation methods are mainly divided into two categories: model-based and data-driven estimation methods. Among them, model-based estimation methods mainly estimate the actual capacity of the battery based on electrochemical models, equivalent circuit models or empirical models, combined with relevant state observers or filters. The estimation effect of this type of method is closely related to the model structure adopted. Higher-order battery models can more accurately characterize battery chemical reactions and improve the accuracy of battery state estimation; but it also increases the computational burden.

[0003] According to the operating conditions of the battery, the data-driven estimation method can be further divided into estimation methods based on charging and discharging conditions. Compared with the discharging condition, the battery charging condition data is relatively stable and predictable. Therefore, the actual capacity estimation method of the battery based on the charging operation data has received widespread attention. Among them, the constant voltage charging condition has been increasingly used in the estimation of the actual capacity of the battery in recent years because it is insensitive to the initial state of charging and contains more information reflecting the battery capacity attenuation. For this type of method, the estimation accuracy of the actual capacity of the battery is closely related to the characteristic variables used, and the hyperparameters of the battery capacity estimation model and the integrity of the constant voltage charging data also have an important influence on the estimation results. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method for estimating the actual capacity of a lithium-ion battery applied to a non-complete constant voltage condition. After the constant voltage charging condition of the battery ends, the estimation method accurately estimates the actual capacity of the battery online according to a battery equivalent circuit model applied to the constant voltage condition, thereby realizing flexible and accurate estimation of the battery health status under the non-complete charging condition.

[0005] The present invention is achieved through the following technical solutions.

[0006] The present invention provides a method for estimating the actual capacity of a lithium-ion battery applied to a non-complete constant voltage working condition, the method comprising the following steps:

[0007] S1, determine the battery model structure according to the test data;

[0008] S2, identifying battery model parameters corresponding to different data lengths according to the current curve of the battery in the constant voltage charging stage;

[0009] S3, performing correlation analysis between battery model parameters and battery capacity corresponding to different data lengths over the entire life cycle, further determining the effective data length interval, and determining and normalizing characteristic variables that characterize battery capacity attenuation;

[0010] S4, combining the optimization algorithm with the machine learning algorithm, constructing and storing a battery actual capacity estimation model corresponding to different constant voltage charging data lengths;

[0011] S5, when the battery enters the constant voltage charging mode, recording and storing the battery constant voltage charging time-current sequence;

[0012] S6, when the charging process is completed, the characteristic variables determined in step S3 are identified based on the stored constant voltage charging time-current data and normalized;

[0013] S7, determine the actual constant voltage charging data length l d,act and the relationship between the constant voltage charging data length stored in step S4, querying to obtain a battery actual capacity estimation model corresponding to the actual constant voltage charging data;

[0014] S8, substituting the characteristic variables obtained in step S6 into the battery actual capacity estimation model corresponding to the actual constant voltage charging time obtained in step S7, to estimate the actual capacity value of the battery.

[0015] In the step S1, the input end of the multi-order network for characterizing the dynamic characteristics of the current is connected to the positive electrode of the voltage output end of the model end; the output end of the multi-order network for characterizing the dynamic characteristics of the current is connected to the negative electrode of the voltage output end of the model end; the multi-order network for characterizing the dynamic characteristics of the current includes a plurality of open circuit voltage equivalent voltage sources V connected in parallel in series. eoc,k , resistor R o,k and a resistor-capacitor network; the resistor-capacitor network includes a polarization resistor R connected in parallel p,k and polarization capacitance C p,k , where k = 1, 2, ... n; the mathematical equation for analyzing the battery model is:

[0016]

[0017] Where n is the number of parallel branches in the multi-order network that characterizes the dynamic characteristics of the current, that is, the order of the battery model in the constant voltage stage; t is the constant voltage charging time, I(t) represents the constant voltage charging current of the battery, and I k and d k They represent the branch current and dynamic characteristic parameters of the k-th order network characterizing the dynamic characteristics of current, and I k (0) is the initial current value of the branch, where d kis the slope k of the equivalent open-circuit voltage corresponding to the branch ocv,k , sampling frequency f s , resistor R o,k And the function of battery capacity Q, that is:

[0018] d k =f(k eocv,k ,f s ,R o,k ,Q)

[0019] In the step S1, the determination of the battery model structure includes the following steps:

[0020] S1.1, assign parameter k to 1;

[0021] S1.2, obtain the error e between the estimated current value and the measured current value I,k And the average execution time T of the parameter identification algorithm com,k ;

[0022] S1.3, increase the parameter k value by 1;

[0023] S1.4, judge e I,k-1 -e I,k The difference between the value and the error threshold Δe I and T com,k -T com,k-1 Value and time difference threshold ΔT com Relationship, that is, if e I,k-1 -e I,k <Δe I And T com,k -T com,k-1 >ΔT com , then the model order is determined to be k-1, otherwise execute step S1.2.

[0024] In step S2, the identification process of battery model parameters corresponding to different data lengths includes the following steps:

[0025] S2.1, extract the constant voltage charging curve of the battery in the previous cycle test as the reference curve, and select the data length corresponding to the current dropping to the cutoff value as the longest data length l required for the identification parameter d,max ;

[0026] S2.2, set the data length interval Δl d , and assign the interval number n to 0;

[0027] S2.3, identify the corresponding data length l d = l d,max -n*Δl d The battery model parameter set is stored and the battery model current estimation error e corresponding to the data length is obtained.I,n ;

[0028] S2.4, increase the interval number n by 1;

[0029] S2.5, determine the battery model current estimation error e I,n-1 With the error threshold e I,max and the interval numbers n and l d,max / Δl d Relationship, that is, if e I,n-1 ≥e I,max or n-1≥l d,max / Δl d , then determine the data length l d,max -(n-1)*Δl d The shortest data length l required for parameter identification d,min , otherwise execute step S2.3.

[0030] In step S3, the expression of the correlation coefficient in the correlation analysis is:

[0031]

[0032] Among them, P θ,Q (l d ) is the corresponding data length l d The correlation coefficient between the battery model parameter θ and the battery capacity Q, n cycle The number of battery RPT tests in offline testing;

[0033] In the step S3, extract θ,Q (l d )|>0.8, forming a high-correlation data length interval for each model parameter, counting the number of data points contained in the corresponding high-correlation data length interval for each model parameter, and selecting the battery model parameters whose corresponding data points are greater than the set threshold as the characteristic variable set characterizing the battery capacity attenuation;

[0034] In step S3, the normalized expression of the characteristic variable is:

[0035]

[0036] Among them, FoI i and FoI norm,i are the characteristic variable values ​​before and after normalization, FoI max and FoI min are the maximum and minimum values ​​of the feature variables before normalization.

[0037] In step S4, a support vector regression algorithm is used to construct a battery actual capacity estimation model corresponding to different constant voltage charging data lengths, and the gray wolf optimization algorithm is used to optimize the support vector regression algorithm parameters to obtain a more accurate battery actual capacity estimation model corresponding to different constant voltage charging data lengths. The method for constructing a battery health status estimation model combining the gray wolf optimization algorithm and the support vector regression algorithm is:

[0038] S4.1, Algorithm parameter initialization: Gray wolf population size N GW , the maximum number of iterations T GW ;

[0039] S4.2, Population initialization: Randomly generate N GW Gray wolf individuals x j ,j=1,2,...,N GW ; Train the model through support vector regression algorithm and calculate the fitness value of each individual;

[0040] S4.3, calculate and record the position vector x of the leader wolf σ, the deputy wolf β and the advisor wolf δ σ 、x β and x δ ;

[0041] S4.4, update the position of gray wolves in the gray wolf pack;

[0042] S4.5, determine whether the maximum number of iterations T set in S4.1 has been reached GW Or whether the termination condition is met; if T is not reached GW and the termination condition is not met, execute step S4.2; otherwise, execute step S4.6;

[0043] S4.6, output the optimal gray wolf position and obtain the optimal parameters of the support vector regression algorithm.

[0044] In step S7, the actual constant voltage charging data length is l d,act The judgment and query process of the constant voltage charging data length relationship stored in step S4 is as follows: d,act , when l d,act Greater than or equal to l d,max When, according to l d,max Select the corresponding battery actual capacity estimation model; otherwise, when l d,act Less than or equal to l d,min When, according to l d,min Select the corresponding battery actual capacity estimation model; otherwise, when l d,act Greater than l d,min And when l d,act Less than l d,max When, according to l d,actSelect the corresponding battery actual capacity estimation model.

[0045] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0046] 1. Compared with the traditional battery actual capacity estimation method based on constant voltage working condition, the battery actual capacity estimation method provided by the present invention is insensitive to the preset constant voltage working condition cutoff condition and has higher flexibility and robustness;

[0047] 2. The battery equivalent circuit model and structure determination method for constant voltage working conditions constructed by the present invention have low complexity while ensuring the accuracy of the model;

[0048] 3. The equivalent model parameters selected by the present invention can more accurately and effectively characterize the capacity decay of the battery;

[0049] 4. Compared with the battery actual capacity estimation method based on dynamic discharge data and constant current charging data, the estimation method provided by the present invention has higher robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The figure is a flow chart of the actual battery capacity estimation algorithm of the present invention.

[0051] Figure 2 The figure is a structural diagram of a battery equivalent circuit model applied to a constant voltage working condition according to the present invention.

[0052] Figure 3 A flow chart for determining the battery model structure of the present invention.

[0053] Figure 4 This is a flow chart of battery model parameter identification with different data lengths according to the present invention.

[0054] Figure 5 Comparison of current estimation error and parameter identification algorithm running time for different structural models.

[0055] Figure 6 This is a graph showing the evolution trend of the correlation coefficient of the battery model parameters obtained based on different constant voltage charging time data with respect to the actual battery capacity.

[0056] Figure 7 Comparison of battery capacity estimation and actual measurement under different constant voltage charging times. DETAILED DESCRIPTION

[0057] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0058] The embodiment of the present invention provides a method for estimating the actual capacity of a lithium-ion battery applied to a non-complete constant voltage working condition, thereby achieving efficient and safe management of an on-board power battery and an electric energy storage system.

[0059] A method for estimating the actual capacity of lithium-ion batteries applied to non-complete constant voltage conditions is as follows: Figure 1 As shown, the method is mainly divided into two parts; the first part is the offline analysis part, and the second part is the online estimation part; the two parts will be further explained below.

[0060] The offline analysis part includes the following steps:

[0061] 1) Determine the battery model structure based on the test data;

[0062] The battery model structure is as follows Figure 2 As shown. The input end of the multi-order network for characterizing the dynamic characteristics of the current is connected to the positive electrode of the voltage output end of the model end; the output end of the multi-order network for characterizing the dynamic characteristics of the current is connected to the negative electrode of the voltage output end of the model end; the multi-order network for characterizing the dynamic characteristics of the current includes a plurality of open circuit voltage equivalent voltage sources V connected in parallel in series. eoc,k , resistor R o,k and a resistor-capacitor network; the resistor-capacitor network includes a polarization resistor R connected in parallel p,k and polarization capacitance C p,k , where k = 1, 2, ... n; the mathematical equation for analyzing the battery model is:

[0063]

[0064] Where n is the number of parallel branches in the multi-order network that characterizes the dynamic characteristics of the current, that is, the order of the battery model in the constant voltage stage; t is the constant voltage charging time, I(t) represents the constant voltage charging current of the battery, and I k and d k They represent the branch current and dynamic characteristic parameters of the k-th order network characterizing the dynamic characteristics of current, and I k (0) is the initial current value of the branch, where d k is the slope k of the equivalent open-circuit voltage corresponding to the branch ocv,k , sampling frequency f s , resistor Ro,k And the function of battery capacity Q, that is:

[0065] d k =f(k eocv,k ,f s ,R o,k ,Q)

[0066] Among them, the process of determining the battery model structure is as follows Figure 3 As shown, the specific steps include:

[0067] 1.1) Assign parameter k to 1;

[0068] 1.2) Obtain the error e between the estimated current value and the measured current value I,k And the average execution time T of the parameter identification algorithm com,k ;

[0069] 1.3) Increase the parameter k value by 1;

[0070] 1.4) Determine e I,k-1 -e I,k The difference between the value and the error threshold Δe I and T com,k -T com,k-1 Value and time difference threshold ΔT com Relationship, that is, if e I,k-1 -e I,k <Δe I And T com,k -T com,k-1 >ΔT com , then determine the model order to be k-1, otherwise execute step 1.2);

[0071] 2) According to the current curve of the battery in the constant voltage charging stage, identify the battery model parameters corresponding to different data lengths;

[0072] Among them, the identification process of battery model parameters corresponding to different data lengths is as follows: Figure 4 As shown, the specific steps include:

[0073] 2.1) Extract the constant voltage charging curve of the battery in the previous cycle test as the reference curve, and select the data length corresponding to the current dropping to the cutoff value as the longest data length l required for the identification parameter d,max ;

[0074] 2.2) Set the data length interval Δl d , and assign the interval number n to 0;

[0075] 2.3) Identify the corresponding data length l d = l d,max -n*Δl dThe battery model parameter set is stored and the battery model current estimation error e corresponding to the data length is obtained. I,n ;

[0076] 2.4) Increase the interval number n by 1;

[0077] 2.5) Determine the battery model current estimation error e I,n-1 With the error threshold e I,max and the interval numbers n and l d,max / Δl d Relationship, that is, if e I,n-1 ≥e I,max or n-1≥l d,max / Δl d , then determine the data length l d,max -(n-1)*Δl d The shortest data length l required for parameter identification d,min , otherwise go to step 2.3);

[0078] 3) Carry out correlation analysis between battery model parameters and battery capacity corresponding to different data lengths over the entire life cycle, further determine the effective data length interval, and determine and normalize the characteristic variables that characterize battery capacity attenuation;

[0079] Among them, the expression of the correlation coefficient in the correlation analysis is:

[0080]

[0081] Among them, P θ,Q (l d ) is the corresponding data length l d The correlation coefficient between the battery model parameter θ and the battery capacity Q, n cycle The number of battery RPT tests in offline testing;

[0082] In the step 3), extract θ,Q (l d )|>0.8, forming a high-correlation data length interval for each model parameter, counting the number of data points contained in the corresponding high-correlation data length interval for each model parameter, and selecting the battery model parameters whose corresponding data points are greater than the set threshold as the characteristic variable set characterizing the battery capacity attenuation;

[0083] In the step 3), the normalized expression of the characteristic variable is:

[0084]

[0085] Among them, FoI i and FoI norm,iare the characteristic variable values ​​before and after normalization, FoI max and FoI min are the maximum and minimum values ​​of the characteristic variables before normalization;

[0086] 4) Combining the optimization algorithm with the machine learning algorithm, a battery actual capacity estimation model corresponding to different constant voltage charging data lengths is constructed and stored;

[0087] Among them, the support vector regression algorithm is used to construct a battery actual capacity estimation model corresponding to different constant voltage charging data lengths, and the gray wolf optimization algorithm is used to optimize the support vector regression algorithm parameters to obtain a more accurate battery actual capacity estimation model corresponding to different constant voltage charging data lengths. The above algorithm can be directly called in the corresponding algorithm module in MATLAB. The specific implementation steps of the battery health status estimation model construction method combining the gray wolf optimization algorithm and the support vector regression algorithm are:

[0088] S4.1, Algorithm parameter initialization: Gray wolf population size N GW , the maximum number of iterations T GW ;

[0089] S4.2, Population initialization: Randomly generate N GW Gray wolf individuals x j ,j=1,2,...,N GW ; Train the model through support vector regression algorithm and calculate the fitness value of each individual;

[0090] S4.3, calculate and record the position vector x of the leader wolf σ, the deputy wolf β and the advisor wolf δ σ 、x β and x δ ;

[0091] S4.4, update the position of gray wolves in the gray wolf pack;

[0092] S4.5, determine whether the maximum number of iterations T set in S4.1 has been reached GW Or whether the termination condition is met; if T is not reached GW and the termination condition is not met, execute step S4.2; otherwise, execute step S4.6;

[0093] S4.6, output the optimal gray wolf position and obtain the optimal parameters of the support vector regression algorithm.

[0094] The online estimation part includes the following steps:

[0095] 1) When the battery enters the constant voltage charging mode, the battery constant voltage charging time-current sequence is recorded and stored;

[0096] 2) When the charging process is completed, the offline analysis steps are obtained based on the stored constant voltage charging time-current data

[0097] 3) The characteristic variables determined in the experiment are normalized and transformed;

[0098] 3) Determine the actual constant voltage charging data length l d,act and offline analyzing the relationship between the constant voltage charging data length stored in step 4), querying and obtaining a battery actual capacity estimation model corresponding to the actual constant voltage charging data;

[0099] Among them, the actual constant voltage charging data length is l d,act The judgment and query process of the constant voltage charging data length relationship stored in the offline analysis step 4) is as follows: d,act , when l d,act Greater than or equal to l d,max When, according to l d,max Select the corresponding battery actual capacity estimation model; otherwise, when l d,act Less than or equal to l d,min When, according to l d,min Select the corresponding battery actual capacity estimation model; otherwise, when l d,act Greater than l d,min And when l d,act Less than l d,max When, according to l d,act Select the corresponding battery actual capacity estimation model.

[0100] 4) Substituting the characteristic variables obtained in the online estimation step 2) into the battery actual capacity estimation model corresponding to the actual constant voltage charging time obtained in the online estimation step 3) to estimate the actual capacity value of the battery.

[0101] In this embodiment, the implementation object is a ternary lithium-ion battery with a nominal capacity of 3.4Ah, but the actual application is not limited to this. The current estimation error and the running time of the parameter identification algorithm of different structural models are compared. Figure 5 As shown in the figure, it can be seen that with the increase of the model order, the corresponding parameter identification algorithm running time also increases. In comparison, compared with the first-order model, the current estimation accuracy of the second-order model has been greatly improved; but compared with the second-order model, the current estimation accuracy of the third-order model has not been significantly improved. Therefore, for the target battery, the model order is set to 2. The evolution trend of the correlation coefficient of the battery model parameters obtained based on different constant voltage charging time data with respect to the actual battery capacity is shown in the figure. Figure 6 As shown in the figure, it can be seen that when the constant voltage charging time is longer than 500s and 590s respectively, the absolute values ​​of the correlation coefficients of the model parameters d1 and I1(0) are both higher than 0.8. Therefore, for the target battery, d1 and I1(0) are selected as characteristic variables, and l d,minThe battery capacity estimation value obtained by the present invention under different constant voltage charging time is compared with the actual measured value. Figure 7 It can be found that the estimated battery capacity value can track the measured value well, so this method can well realize the online battery actual capacity estimation.

[0102] In summary, the present invention provides a method for estimating the actual capacity of a lithium-ion battery applied to a non-complete constant voltage working condition, comprising: determining a battery model structure offline; identifying battery model parameters corresponding to different data lengths; further determining a valid data length interval, and determining and normalizing characteristic variables characterizing battery capacity attenuation; constructing and storing a battery actual capacity estimation model corresponding to different constant voltage charging data lengths; recording and storing a battery constant voltage charging time-current sequence in real time; identifying characteristic variables after the charging process is completed and performing normalization conversion; querying and obtaining a battery actual capacity estimation model corresponding to actual constant voltage charging data; and calculating the actual capacity value of the battery. The estimation method proposed in the present invention has the following four advantages: (1) Compared with the traditional battery actual capacity estimation method based on constant voltage conditions, the battery actual capacity estimation method provided by the present invention is insensitive to the preset constant voltage condition cutoff condition and has higher flexibility and robustness; (2) The battery equivalent circuit model and structure determination method for constant voltage conditions constructed by the present invention have lower complexity while ensuring the accuracy of the model; (3) The equivalent model parameters selected by the present invention can more accurately and effectively characterize the capacity attenuation of the battery; (4) Compared with the battery actual capacity estimation method based on dynamic discharge data and constant current charging data, the estimation method provided by the present invention has higher robustness.

[0103] Those skilled in the art will appreciate that the accompanying drawing is only a schematic diagram of a preferred embodiment, and the serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0104] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0105] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for estimating the actual capacity of a lithium-ion battery under non-complete constant voltage conditions, characterized in that: The method comprises the following steps: S1, determine the battery model structure according to the test data; S2, identifying battery model parameters corresponding to different data lengths according to the current curve of the battery in the constant voltage charging stage; S3, performing correlation analysis between battery model parameters and battery capacity corresponding to different data lengths over the entire life cycle, further determining the effective data length interval, and determining and normalizing characteristic variables that characterize battery capacity attenuation; S4, combining the optimization algorithm with the machine learning algorithm, constructing and storing a battery actual capacity estimation model corresponding to different constant voltage charging data lengths; S5, when the battery enters the constant voltage charging mode, recording and storing the battery constant voltage charging time-current sequence; S6, when the charging process is completed, the characteristic variables determined in step S3 are identified based on the stored constant voltage charging time-current data and normalized; S7, determine the actual constant voltage charging data length l d,act and the relationship between the constant voltage charging data length stored in step S4, querying to obtain a battery actual capacity estimation model corresponding to the actual constant voltage charging data; S8, substituting the characteristic variables obtained in step S6 into the battery actual capacity estimation model corresponding to the actual constant voltage charging time obtained in step S7, to estimate the actual capacity value of the battery.

2. A method according to claim 1, characterized in that The step S1 determines that in the battery model structure, the input end of the multi-order network used to characterize the dynamic characteristics of the current is connected to the positive electrode of the voltage output end of the model end; the output end of the multi-order network used to characterize the dynamic characteristics of the current is connected to the negative electrode of the voltage output end of the model end; the multi-order network used to characterize the dynamic characteristics of the current includes a plurality of open circuit voltage equivalent voltage sources V connected in parallel in series. eoc,k , resistor R o,k and resistor-capacitor networks; The resistor-capacitor network includes polarization resistors R connected in parallel p,k and polarization capacitance C p,k , where k = 1, 2, ... n; the mathematical equation for analyzing the battery model is: Where n is the number of parallel branches in the multi-order network that characterizes the dynamic characteristics of the current, that is, the order of the battery model in the constant voltage stage; t is the constant voltage charging time, I(t) represents the constant voltage charging current of the battery, and I k and d k They represent the branch current and dynamic characteristic parameters of the k-th order network characterizing the dynamic characteristics of current, and I k (0) is the initial current value of the branch, where d k is the slope k of the equivalent open circuit voltage corresponding to the branch ocv,k , sampling frequency f s , resistor R o,k And the function of battery capacity Q, that is: d k =f(k eocv,k ,f s ,R o,k ,Q)。 3. A method according to claim 1, characterized in that In the step S1, the determination of the battery model structure includes the following steps: S1.1, assign parameter k to 1; S1.2, obtain the error e between the estimated current value and the measured current value I,k And the average execution time T of the parameter identification algorithm com,k ; S1.3, increase the parameter k value by 1; S1.4, judge e I,k-1 -e I,k The difference between the value and the error threshold Δe I and T com,k -T com,k-1 Value and time difference threshold ΔT com Relationship, that is, if e I,k-1 -e I,k <Δe I And T com,k -T com,k-1 >ΔT com , then the model order is determined to be k-1, otherwise execute step S1.

2.

4. A method according to claim 1, characterized in that In step S2, the identification process of battery model parameters corresponding to different data lengths includes the following steps: S2.1, extract the constant voltage charging curve of the battery in the previous cycle test as the reference curve, and select the data length corresponding to the current dropping to the cutoff value as the longest data length l required for the identification parameter d,max ; S2.2, set the data length interval Δl d , and assign the interval number n to 0; S2.3, identify the corresponding data length l d = l d,max -n*Δl d The battery model parameter set is stored and the battery model current estimation error e corresponding to the data length is obtained. I,n ; S2.4, increase the interval number n by 1; S2.5, determine the battery model current estimation error e I,n-1 With the error threshold e I,max and the interval numbers n and l d,max / Δl d Relationship, that is, if e I,n-1 ≥e I,max or n-1≥l d,max / Δl d , then determine the data length l d,max -(n-1)*Δl d The shortest data length l required for parameter identification d,min , otherwise execute step S2.

3.

5. A method according to claim 1, characterized in that: In step S3, the expression of the correlation coefficient in the correlation analysis is: Among them, θ i and Q i are the battery model parameters and battery capacity based on the i-th group of cycle data, P θ,Q (l d ) is the corresponding data length l d The correlation coefficient between the battery model parameter θ and the battery capacity Q, n cycle It is the number of battery RPT tests in offline test; In the step S3, extract θ,Q (l d )|>0.8, forming a high-correlation data length interval for each model parameter, counting the number of data points contained in the corresponding high-correlation data length interval for each model parameter, and selecting the battery model parameters whose corresponding data points are greater than the set threshold as the characteristic variable set characterizing the battery capacity attenuation; In step S3, the normalized expression of the characteristic variable is: Among them, FoI i and FoI norm,i are the characteristic variable values ​​before and after normalization, FoI max and FoI min are the maximum and minimum values ​​of the feature variables before normalization.

6. A method according to claim 1, characterized in that In the step S4, support vector regression is used to construct a battery actual capacity estimation model corresponding to different constant voltage charging data lengths, and the gray wolf optimization algorithm is used to optimize the support vector regression algorithm parameters to obtain a more accurate battery actual capacity estimation model corresponding to different constant voltage charging data lengths.

7. A method according to claim 1, characterized in that In step S7, the actual constant voltage charging data length is l d,act The judgment and query process of the constant voltage charging data length relationship stored in step S4 is as follows: d,act , when l d,act Greater than or equal to l d,max When, according to l d,max Select the corresponding battery actual capacity estimation model; otherwise, when l d,act Less than or equal to l d,min When, according to l d,min Select the corresponding battery actual capacity estimation model; otherwise, when l d,act Greater than l d,min And when l d,act Less than l d,max When, according to l d,act Select the corresponding battery actual capacity estimation model.

8. A method according to claim 6, characterized in that ,The method for building a battery health state estimation model combining the gray wolf optimization algorithm and the support vector regression algorithm is: S4.1, Algorithm parameter initialization: Gray wolf population size N GW , the maximum number of iterations T GW ; S4.2, Population initialization: Randomly generate N GW Gray wolf individuals x j ,j=1,2,...,N GW ; Train the model through support vector regression algorithm and calculate the fitness value of each individual; S4.3, calculate and record the position vector x of the leader wolf σ, the deputy wolf β and the advisor wolf δ σ 、x β and x δ ; S4.4, update the position of gray wolves in the gray wolf pack; S4.5, determine whether the maximum number of iterations T set in S4.1 has been reached GW Or whether the termination condition is met; if T is not reached GW If the termination condition is not met, execute step S4.2; Otherwise, execute step S4.6; S4.6, output the optimal gray wolf position and obtain the optimal parameters of the support vector regression algorithm.

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