A method for reconstructing open circuit voltage curve of vehicle-mounted lithium-ion batteries

Abstract

The application provides a kind of open-circuit voltage curve reconstruction method for vehicle-mounted lithium ion battery, belongs to the technical field of battery management system, including collecting and storing the multistage constant current charging data of vehicle-mounted lithium ion battery, and the data is segmented processing;Establish the first-order equivalent circuit model of battery, and the optimal parameters of the model are identified by using the white whale optimization algorithm enhanced by random reset strategy;According to the model parameters, the open-circuit voltage curve in each segment is solved;The open-circuit voltage curve combination is reconstructed.The application can complete the reconstruction of the open-circuit voltage curve of the battery only by using the data of single charging of the vehicle.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery management system, and particularly relates to an open-circuit voltage curve reconstruction method for a vehicle-mounted lithium ion battery. BACKGROUND

[0002] The open-circuit voltage curve is an important feature of a lithium ion battery, and can be used for estimation of the state of charge (SOC) and diagnosis of the state of health, and plays an important role in the vehicle-mounted battery management system. However, as the battery ages, the shape and position of the open-circuit voltage curve will change, and if the curve is not updated, the performance of the battery management system will decrease, such as an increase in SOC error.

[0003] Current research on open-circuit voltage curve reconstruction mainly includes the following categories: 1) test-based method, which reduces the standing time by applying different current excitations to the battery, thereby reducing the test time. However, testing requires special equipment and cannot be performed in the daily use of vehicle users. 2) model-based method, which establishes a battery model, such as an equivalent circuit model, and uses a parameter identification algorithm to identify the model parameters, thereby solving the open-circuit voltage. However, the stability of the identification result is greatly affected by the operating conditions of the battery. 3) data-driven method, which uses Gaussian process regression, back propagation neural network, deep neural network, etc. to predict the open-circuit voltage of the battery, but this method requires a large amount of data support and is difficult to apply in practice.

[0004] Multi-stage constant current charging is a charging method commonly used in electric vehicles, in which the charging current decreases step by step, which can effectively control the charging temperature rise and prolong the service life of the battery. Unlike constant current and constant voltage charging, the multi-stage constant current charging process includes a constant current stabilization process and a dynamic process during the constant current stage switching, which can be used to reconstruct the open-circuit voltage curve. Using the charging data of the battery to reconstruct the open-circuit voltage curve can greatly reduce the time cost required for testing, and is also very convenient for users. However, few studies focus on using multi-stage constant current charging data of the battery to reconstruct the open-circuit voltage curve.

[0005] Application No. 202311360664.4, a lithium ion battery health diagnosis method based on open-circuit voltage curve reconstruction, discloses using battery management system data of a cloud platform-based energy storage system, using the battery open-circuit voltage data set collected and stored by the BMS, analyzing the curve characteristics of the open-circuit voltage data points and reconstructing the incomplete curve of the state of charge-open-circuit voltage relationship; based on the offline complete open-circuit voltage curve in the preliminary experiment, the complete open-circuit voltage curve is reconstructed using the voltage curve similarity matching method; based on the curve change characteristics of the high state of charge region of the open-circuit voltage curve after the battery ages, the battery health diagnosis is realized.

[0006] Application number CN202110615632.9, invention title: A data-driven method for reconstructing the open-circuit voltage curve of a lithium-ion battery. The invention discloses collecting and storing a battery's open-circuit voltage data set through a battery management system (BMS), preprocessing the stored open-circuit voltage data set, and finally reconstructing the open-circuit voltage curve using the preprocessed open-circuit voltage data. This invention solves the problem of interference with the open-circuit voltage curve caused by SOC estimation errors in traditional open-circuit voltage curve acquisition methods, and avoids the problem of traditional open-circuit voltage curve acquisition methods relying on low-current operating conditions.

[0007] The invention, titled "A Method for Reconstructing Open-Circuit Voltage Curves for Automotive Lithium-Ion Batteries," utilizes multi-stage constant-current charging data from automotive lithium-ion batteries, processes the data in segments, establishes a battery model, and utilizes a Beluga optimization algorithm enhanced with a random reset strategy to identify the optimal parameters of the model. The method then solves for the open-circuit voltage curve within each segment. This invention significantly reduces the time cost of acquiring open-circuit voltage curves while maintaining high accuracy, providing an efficient solution for reconstructing open-circuit voltage curves using multi-stage constant-current charging data.

[0008] Therefore, there is an urgent need to develop a method to reconstruct the battery open circuit voltage curve using charging data. Summary of the Invention

[0009] The purpose of the present invention is to solve the defects of the above-mentioned prior art and provide a method for reconstructing the open circuit voltage curve of a vehicle-mounted lithium-ion battery, so that the open circuit voltage curve of the lithium-ion battery can be reconstructed using the charging data of the vehicle.

[0010] The present invention adopts the following technical solutions:

[0011] A method for reconstructing an open circuit voltage curve of an on-board lithium-ion battery, comprising:

[0012] S1. The vehicle battery management system collects and stores battery status data of lithium-ion batteries during multi-stage constant current charging, including current, voltage, state of charge (SOC), and temperature data;

[0013] S2. Segment the data according to the current changes during the multi-stage constant current charging process;

[0014] S3. Establish a first-order RC equivalent circuit model of the battery for each segment in S2;

[0015] S4. Use the White Whale optimization algorithm enhanced by the random reset strategy to optimize the model parameters;

[0016] S5. solving the open circuit voltage curve of each segment according to the optimal model parameters;

[0017] S6. Combine all segmented open circuit voltage curves, smooth the curves, and use a polynomial model to fit the open circuit voltage-state of charge (SOC) curve to complete the reconstruction.

[0018] Furthermore, in step S1, the multi-stage constant current charging process should include at least three constant current stages, and the entire charging process starts when the SOC is lower than 10% and ends when the SOC is higher than 90%.

[0019] Furthermore, the step 2 is specifically as follows:

[0020] S2.1. Intercept all the segments where the current suddenly changes when charging starts and switching between the constant current phase, and record them as A, A = [A0, A1, ..., A n ], where A i Represents the i-th segment, each segment length is m, m can be set to a different time length, such as 300 seconds;

[0021] S2.2. After the entire charging process is cut off from segment A, all the remaining segments are recorded as B, B = [B0, B1, ..., B n ], where B j represents the jth segment.

[0022] Furthermore, in step S3, the first-order RC equivalent circuit model is only for segment A. i Modeling is performed, the open circuit voltage source U in the model ocv Use a linear model instead, that is, Where k is the sampling time point, k = 0, 1, 2, ..., Δt is the sampling time interval, μ and λ are parameters. The parameter set to be solved for the entire model is [μ, λ, R o , R p , C p ], where R o ,R p and C p They represent ohmic internal resistance, polarization internal resistance and polarization capacitance respectively.

[0023] Furthermore, in step S4, the White Whale optimization algorithm enhanced by the random reset strategy is specifically as follows: after the White Whale optimization algorithm updates the parameter position, it checks whether the new parameter position exceeds the set parameter boundary. If it exceeds, the random reset strategy is used to adjust the parameter position. The adjustment formula is:

[0024]

[0025] in, represents the new position of the i-th individual in the j-th dimension obtained by the white whale algorithm, T represents the current number of iterations, Represents the position after random reset strategy adjustment, lb and ub represent the lower and upper bounds of the parameter respectively, rand() is a function that generates random numbers between the interval [0,1], and d is an adjustment factor, which is set to 0.3 by default.

[0026] Furthermore, in step S4, when optimizing the model parameters, the loss function is defined as the root mean square error between the actual terminal voltage of the battery and the terminal voltage estimated by the model, and the calculation formula is:

[0027]

[0028] Where n is the number of data points, U t is the real terminal voltage, U est Estimate the terminal voltages for the model.

[0029] Furthermore, step S5 includes the following steps:

[0030] S5.1. According to the first-order RC equivalent circuit model, U ocv The linear model is used to solve the open circuit voltage curves of all segments in A;

[0031] S5.2. Solve for the open-circuit voltage curves of all segments in B. The calculation formula is:

[0032] U ocv =U t -I(R o +R p )

[0033] Where I is the current value, which is positive during charging. If the maximum temperature difference during charging does not exceed 10°C, then R o and R p Take the mean of all fragment identification results in A. If the maximum temperature difference during charging exceeds 10°C, use a linear model to fit the temperature and R o and R p The relationship between R and o and R p value.

[0034] Furthermore, step S6 includes the following steps:

[0035] S6.1. Connect the open-circuit voltage curves corresponding to all segments in A and B according to their positional relationships, and smooth the combined open-circuit voltage curves using a Savitzky-Golay filter.

[0036] S6.2. Obtain a curve of open circuit voltage versus SOC based on the SOC data recorded by the battery management system;

[0037] S6.3. The relationship between open-circuit voltage and SOC is fitted using a p-order polynomial model, p is at least 6 orders, which can be adjusted according to the fitting effect.

[0038] The beneficial effects of the present application are:

[0039] The present application can directly use the multi-stage constant current charging data of a vehicle to reconstruct the open-circuit voltage curve of the battery pack, which has good precision on the basis of saving time cost.

[0040] The present application can update the open-circuit voltage curve of the battery in the daily use of the vehicle without other special tests on the battery, thereby improving the reliability of the battery management system and playing an important role in the safe management of the vehicle battery. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 is the overall flowchart of the method of the present application;

[0042] Figure 2 is the data curve of the multi-stage constant current charging process in the method of the present application; (a) represents the current curve; (b) represents the voltage curve; (c) represents the SOC curve; (d) represents the temperature curve.

[0043] Figure 3 is the result of segmenting the charging data in the method of the present application.

[0044] Figure 4 is a schematic diagram of the first-order equivalent current model in the method of the present application.

[0045] Figure 5 is the flowchart of the white whale optimization algorithm enhanced by the random reset strategy in the method of the present application.

[0046] Figure 6 is the reconstructed open-circuit voltage curve result in the preferred example of the present application; (a) represents the comparison of the reconstructed open-circuit voltage curve and the true open-circuit voltage curve; (b) represents the absolute error of the reconstructed open-circuit voltage curve. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the present application is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0048] EMBODIMENT

[0049] As Figure 1As shown, a method for reconstructing an open circuit voltage curve of a vehicle-mounted lithium-ion battery of the present invention comprises the following steps:

[0050] S1. The on-board battery management system collects and stores battery status data of lithium-ion batteries during multi-stage constant current charging, including current, voltage, state of charge (SOC) and temperature data.

[0051] S2. Segment the data according to the current changes during the multi-stage constant current charging process;

[0052] S3. Establish a first-order RC equivalent circuit model of the battery for each segment in S2;

[0053] S4. Use the White Whale optimization algorithm enhanced by the random reset strategy to optimize the model parameters of S3;

[0054] S5. solving the open circuit voltage curve of each segment according to the optimal model parameters;

[0055] S6. Combine all segmented open circuit voltage curves, smooth the curves, and use a polynomial model to fit the open circuit voltage-state of charge (SOC) curve to complete the reconstruction.

[0056] In a preferred embodiment of the present invention, in step S1, the multi-stage constant current charging process should include at least 3 constant current stages, and the entire charging process starts when the SOC is lower than 10% and ends when the SOC is higher than 90%.

[0057] In this embodiment: the experimental battery is a ternary lithium-ion battery with a rated capacity of 6.9Ah. Before the experiment, the battery is discharged to an SOC of less than 10%. The battery is charged with a multi-stage constant current using charging and discharging equipment: specifically, in the first stage, the battery is charged with a current of 1C until the battery voltage reaches 3.8V; in the second stage, the battery is charged with a current of 0.75C until the battery voltage reaches 3.95V; in the third stage, the battery is charged with a current of 0.5C until the battery voltage reaches 4.1V; in the fourth stage, the battery is charged with a current of 0.25C until the battery voltage reaches 4.2V, and the charging is completed. Record the data (current, voltage, SOC, temperature) during the charging process, such as Figure 2 shown.

[0058] like Figure 3 As shown, in a preferred embodiment of the present invention, step S2 includes:

[0059] S2.1. Intercept all the segments where the current suddenly changes when charging starts and switching between the constant current phase, and record them as A, A = [A0, A1, ..., A n ], where A i Represents the i-th segment, each segment length is m, m can be set to different time lengths, such as 300 seconds;

[0060] S2.2. After the entire charging process is cut off from segment A, all the remaining segments are recorded as B, B = [B0, B1, ..., B n ], where B j represents the jth segment.

[0061] For multi-stage constant current charging, there are several constant current stages corresponding to the number of A i and B i In the charging data of this embodiment, a 4-stage constant current charging is used, so there are 4 A i and 4 Bs i Fragments. The specific segmentation method is:

[0062] (1). Intercept A i Fragment, according to the setting A i The length of the segment is m. A1 corresponds to intercepting a segment of length m from the start of charging. A2 corresponds to switching from the first constant current stage to the second constant current stage, taking two data sampling points forward of the switching point as the starting point and intercepting a segment of length m backward. A3 corresponds to switching from the second constant current stage to the third constant current stage, taking two data sampling points forward of the switching point as the starting point and intercepting a segment of length m backward. A4 corresponds to switching from the third constant current stage to the fourth constant current stage, taking two data sampling points forward of the switching point as the starting point and intercepting a segment of length m backward.

[0063] (2) Determine B i Segments, B1 corresponds to the data segment from the end point of segment A1 to the start point of segment A2. B2 corresponds to the data segment from the end point of segment A2 to the start point of segment A3. B3 corresponds to the data segment from the end point of segment A3 to the start point of segment A4. B4 corresponds to the data segment from the end point of segment A4 to the end of charging. In this way, all A1 and B are obtained. i fragment.

[0064] like Figure 4 As shown, in a preferred embodiment of the present invention, in step S3, the first-order RC equivalent circuit model is only for segment A i Modeling is performed, the open circuit voltage source U in the model ocv Use a linear model instead, that is, Where k is the sampling time point, k = 0, 1, 2, ..., Δt is the sampling time interval, μ and λ are parameters. The parameter set to be solved for the entire model is [μ, λ, R o , R p , C p ], where R o ,R p and C pThey represent ohmic internal resistance, polarization internal resistance and polarization capacitance respectively.

[0065] The specific formula of the first-order RC equivalent circuit model in this embodiment is as follows:

[0066] U t =U ocv +IR o +U p (1)

[0067]

[0068] U p represents the polarization voltage, represents the derivative of polarization voltage with respect to time, U ocv represents the open circuit voltage OCV, and I is the current (positive during charging and negative during discharge). Writing the above equations (1) and (2) in discrete form, we get:

[0069] U t (k+1)=U ocv (k+1)+R0I(k+1)+U p (k+1) (3)

[0070]

[0071] k is the discrete time step, Δt is the sampling time interval, and in the charging data used for verification, the sampling time interval is 1s. Substituting formula (4) into formula (3), eliminating U p , we can get:

[0072]

[0073] Since the open circuit voltage OCV changes slowly, it can be considered that the open circuit voltages in adjacent time steps are equal, that is, U ocv (k+1)=U ocv (k), and further Formula (5) can be simplified as:

[0074] U t (k)=(1-a)U ocv (k)+aU t (k-1)+cR p -a(R o +R p ))I(k-1)+R o I(k) (6)

[0075] At this time, the linear model of OCV is as follows:

[0076]

[0077] Substituting equation (6) into equation (8), equation (8) can be obtained:

[0078]

[0079] From equation (8), it can be seen that:

[0080] U t is the terminal voltage, corresponding to the voltage in the charging data, which is known; Δt is the sampling time interval, which is 1 s; I is the charging current, which is known in the charging data:

[0081] Therefore, the unknown parameters θ = [μ, λ, R o , R p , C p ]

[0082] As shown in the preferred embodiment of the present application, in step S4, after the white whale optimization algorithm updates the parameter position, it is checked whether the new parameter position exceeds the set parameter boundary. If it exceeds, the parameter position is adjusted using a random reset strategy, and the adjustment formula is: Figure 5

[0083]

[0084] wherein xi,j represents the new position of the ith individual in the jth dimension obtained by the white whale optimization algorithm, T represents the current iteration number, xi,j represents the position adjusted by the random reset strategy, lb and ub represent the lower and upper bounds of the parameter respectively, rand() is a function that generates a random number in the interval [0, 1], and d is an adjustment factor, which is set to 0.3 by default. In a preferred embodiment of the present application, in step S4, when optimizing the model parameters, the loss function is defined as the root mean square error of the battery true terminal voltage and the model estimated terminal voltage, and the calculation formula is:

[0085]

[0086]

[0087] wherein n is the number of data points, U t is the true terminal voltage, and U est is the model estimated terminal voltage.

[0088] In this embodiment:

[0089] After the parameters to be optimized are determined, the following specific numerical values are substituted for calculation:

[0090] Define the input data X(k) = [U t ​​(k-1), I(k-1), I(k), k], k=1,2,3,…, Substitute the voltage and current data collected during the charging process into X(k). The right side of the equal sign of formula (8) is actually a function of the input data X(k) and the parameter set θ, denoted as f(X(k),θ). Now we can get U t (k) = f(X(k),θ), in this formula, only θ is unknown.

[0091] The following uses the White Whale optimization algorithm enhanced by the random reset strategy to identify the optimal parameter θ of the model. The optimal θ is the voltage value calculated by f(X(k),θ) and the actual voltage value U t The error between (k) is the smallest, and the root mean square error is used here to measure it. The calculation formula is:

[0092]

[0093] U est (k) corresponds to the voltage value calculated by f(X(k), θ). This formula calculates the error between the voltage estimated by the battery model and the actual voltage.

[0094] Among them, the specific steps of the white whale optimization algorithm enhanced by the random reset strategy are: (1). Set the population size and number of iterations, randomly initialize the position of the white whale population, and the position of each white whale corresponds to a candidate solution θ. According to the loss function, find the white whale individual with the smallest current loss function value, that is, the optimal individual.

[0095] (2) According to the balance factor B f Determine whether the algorithm is in the exploration stage or the development stage. f When it is greater than 0.5, it is in the exploration stage, and when it is less than or equal to 0.5, it is in the development stage. Different White Whale position update strategies are implemented in the two stages.

[0096] (3) Further judgement of B f Is it less than the whale fall probability W? f If the condition is met, the white whale position will be further updated by whale fall.

[0097] (4) Check whether the updated white whale position exceeds the parameter boundary. For each parameter in θ, its normal upper and lower parameters are set. If it exceeds, the parameter position is adjusted using a random reset strategy. The adjustment formula is:

[0098]

[0099] in, represents the new position of the i-th individual in the j-th dimension obtained by the White Whale optimization algorithm, T represents the current number of iterations, Represents the position after random reset strategy adjustment, lb and ub represent the lower and upper bounds of the parameter respectively, rand() is a function that generates random numbers between the interval [0,1], and d is an adjustment factor, which is set to 0.3 by default.

[0100] In this embodiment, the number of iterations is set to 1000, the population size is 200, and the four A i The parameter optimization results on the fragment are:

[0101] snippet <![CDATA[R o (mΩ)]]> <![CDATA[R p (mΩ)]]> a μ(V) λ Temperature (℃) [A1] 28.6 1.5 0.98 3.31 1401.1 24.16 <![CDATA[A2]]> 26.1 0.8 0.91 3.61 11944.4 23.53 <![CDATA[A3]]> 25.7 1.2 0.94 3.81 9925.7 23.94 <![CDATA[A4]]> 25.5 0.8 0.94 4.01 16046.1 23.16

[0102] In a preferred embodiment of the present invention, step S5 includes the following steps:

[0103] S5.1. According to the first-order RC equivalent current model U ocv The linear model is used to solve the open circuit voltage curves of all segments in A;

[0104] S5.2. Solve for the open-circuit voltage curves of all segments in B. The calculation formula is:

[0105] U OCV =U t -I(R o +R p )

[0106] Where I is the current value, which is positive during charging. If the maximum temperature difference during charging does not exceed 10°C, then R o and R p Take the mean of all fragment identification results in A. If the maximum temperature difference during charging exceeds 10°C, use a linear model to fit the temperature and R o and R p The relationship between R and o and R p The value of .

[0107] In a preferred embodiment of the present invention, step S6 includes the following steps:

[0108] S6.1. Connect the open-circuit voltage curves corresponding to all segments in A and B according to their positional relationships, and smooth the combined open-circuit voltage curves using a Savitzky-Gol ay filter.

[0109] S6.2. Obtain a curve of open circuit voltage with respect to SOC based on the SOC data recorded by the battery management system.

[0110] S6.3. Use a p-order polynomial model to fit the relationship between open-circuit voltage and SOC. p is at least 6th order and can be adjusted based on the fitting effect.

[0111] In this embodiment, the object of implementation is a ternary lithium-ion battery, and an 8th-order polynomial model is used to fit the relationship between open circuit voltage and SOC. The comparison between the reconstructed OCV curve and the real curve is as follows: Figure 6 As shown in the figure, it can be seen that the open circuit voltage curve reconstructed by the present invention is very close to the real open circuit voltage curve, with a maximum error of no more than 15mV, and has high accuracy. i R in the fragment o and R p Mean, determine R o is 26.5mΩ, R p is 1.1 mΩ, which is used to calculate the open circuit voltage curves of all segments in B. The calculation formula is:

[0112] U ocv =U t -I(R o +R p )

[0113] Where U t and I are voltage and current respectively, which are known in the charging data.

[0114] Now we have obtained the OCV of all A1-A4 and B1-B4 segments, spliced ​​them according to their positional relationship, and then used 8th order polynomial fitting to obtain the attached Figure 6 The open circuit voltage reconstruction results in .

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for reconstructing the open circuit voltage curve of a vehicle-mounted lithium-ion battery, characterized in that ,include: S1. The vehicle battery management system collects and stores battery status data of lithium-ion batteries during multi-stage constant current charging, including current, voltage, state of charge, and temperature data; S2. Segment the data according to the current changes during the multi-stage constant current charging process; S2.

1. Intercept all the segments where the current suddenly changes when charging starts and switching between the constant current phase, and record them as A, A = [A0, A1, ..., A n ], where A i represents the i-th segment, each segment has a length of m, and m is set to a different time length; S2.

2. After the entire charging process is cut off from segment A, all the remaining segments are recorded as B, B = [B0, B1, ..., B n ], where B j represents the jth segment; S3. Establish a first-order RC equivalent circuit model of the battery for each segment in S2; S4. Use the White Whale optimization algorithm enhanced by the random reset strategy to optimize the model parameters; S5. solving the open circuit voltage curve of each segment according to the optimal model parameters; S6. Combine all segmented open circuit voltage curves, smooth the curves, and use a polynomial model to fit the open circuit voltage-state of charge curve to complete the reconstruction.

2. The method according to claim 1, characterized in that In step S1, the multi-stage constant current charging process should include at least three constant current stages, and the entire charging process starts when the state of charge is lower than 10% and ends when the state of charge is higher than 90%.

3. The method according to claim 1, characterized in that In step S3, the first-order RC equivalent circuit model is only for segment A. i Modeling is performed, the open circuit voltage source U in the model ocv Use a linear model instead, that is, Where k is the sampling time point, k = 0, 1, 2, ..., Δt is the sampling time interval, μ and λ are parameters, and the parameter set to be solved for the entire model is [μ, λ, R o , R p , C p ], where R o , R p and C p They represent ohmic internal resistance, polarization internal resistance and polarization capacitance respectively.

4. The method according to claim 1, wherein In step S4, the White Whale optimization algorithm enhanced by the random reset strategy is specifically as follows: after the White Whale optimization algorithm updates the parameter position, it checks whether the new parameter position exceeds the set parameter boundary. If it exceeds, the random reset strategy is used to adjust the parameter position. The adjustment formula is: in, represents the new position of the i-th individual in the j-th dimension obtained by the white whale algorithm, T represents the current number of iterations, It represents the position after random reset strategy adjustment, lb and ub represent the lower and upper bounds of the parameter respectively, rand() is a function that generates random numbers between the interval [0, 1], and d is an adjustment factor.

5. The method according to claim 4, characterized in that In step S4, when optimizing the model parameters, the loss function is defined as the root mean square error between the actual terminal voltage of the battery and the terminal voltage estimated by the model, and the calculation formula is: Where n is the number of data points, U t is the real terminal voltage, U est Estimate the terminal voltages for the model.

6. The method according to claim 1, characterized in that The step S5 comprises the following steps: S5.

1. According to the first-order RC equivalent circuit model, U ocv The linear model is used to solve the open circuit voltage curves of all segments in A; S5.

2. Solve for the open-circuit voltage curves of all segments in B. The calculation formula is: U ocv =U t -I(R o +R p ) Where I is the current value, which is positive during charging. If the maximum temperature difference during charging does not exceed 10°C, then R o and R p Take the mean of all fragment identification results in A. If the maximum temperature difference during charging exceeds 10°C, use a linear model to fit the temperature and R o and R p The relationship between R and o and R p The value of .

7. The method according to claim 1, characterized in that The step S6 comprises the following steps: S6.

1. Connect the open-circuit voltage curves corresponding to all segments in A and B according to their positional relationships, and smooth the combined open-circuit voltage curves using a Savitzky-Golay filter. S6.

2. Obtain a curve of open circuit voltage with respect to state of charge based on the state of charge data recorded by the battery management system; S6.

3. Use a p-order polynomial model to fit the relationship between open-circuit voltage and state of charge, where p is at least 6.

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