Battery SOH estimation method based on constant voltage charging current curve

Abstract

The present invention discloses a battery SOH estimation method based on a constant-voltage charging current curve, comprising: extracting health factors HI1, HI2, and HI3 reflecting the battery aging data based on each round of the constant-voltage charging current curve during the full-cycle aging process of the battery and performing normalization processing; obtaining relevant parameters and a training data model based on an optimization algorithm according to the normalized values ​​of the extracted health factors HI1, HI2, and HI3 data; and inputting the normalized values ​​of the health factors HI1, HI2, and HI3 into the training data model to calculate the battery's SOH. According to the current curve based on the constant-voltage charging stage disclosed by the present invention, health factors reflecting battery aging information are collected, health factors with stronger applicability and mechanistic rationality are extracted, and an optimization algorithm and data model are constructed, which can accurately estimate the battery's health status and obtain more accurate battery health status results.

Description

Technical Field

[0001] The present invention belongs to the field of health status estimation of energy storage batteries. More specifically, it relates to a method for extracting battery aging information based on a constant voltage charging current curve and estimating the health status of energy storage batteries using an optimization algorithm and a data model. Background Art

[0002] Energy storage batteries are widely used in various portable devices, electric vehicles, and energy storage systems. The state of health (SOH) of energy storage batteries is one of their important parameters and serves as a key indicator for evaluating battery performance and lifespan. A decrease in SOH can lead to reduced discharge efficiency, increased internal resistance, and even safety hazards such as fire. Therefore, accurately estimating the SOH of energy storage batteries can effectively optimize battery usage and maintenance strategies, reduce replacement costs and environmental impact, and ensure efficient and stable battery operation.

[0003] Energy storage battery state estimation methods are mainly divided into model-based methods and data-driven methods. Traditional model-based methods mainly obtain battery aging characteristic parameters through empirical models, electrochemical models, and equivalent circuit models, and combine methods such as Kalman filtering and particle filtering to estimate the battery's state of health. The advantages of model-based health state estimation methods are strong physical interpretability and low data requirements. However, their modeling is complex, computational costs are high, model parameters are difficult to obtain, and adaptability to actual operating conditions is limited. In dynamic or extreme environments, this may lead to increased estimation errors.

[0004] In addition, existing data-driven methods are mainly divided into the extraction of the health indicator (HI) of energy storage batteries and the construction of SOH estimation models. By extracting parameters that can characterize battery aging from the physical external characteristic parameters of the energy storage battery, such as voltage, current, and temperature, and then using artificial intelligence algorithms such as support vector machines, Gaussian process regression, and artificial neural networks to obtain the mapping relationship between aging parameters and SOH, the battery's SOH is estimated. The data-driven method does not require a physical model, has strong adaptability, good real-time performance, and can mine potential patterns from data. It is currently the most promising SOH estimation method for actual production.

[0005] However, current data-driven methods all extract the health factor HI by analyzing the characteristics of the physical parameters of the entire battery charging and discharging process. However, the existing health factor HI extraction methods have certain limitations. For example, due to differences in users' battery usage habits, extracting the health factor HI only from the battery discharge phase is not universal. Secondly, differences in battery discharge cut-off voltage and capacity will also lead to differences in the initial state of charge (SOC) of the next cycle charge. Therefore, the method of extracting the health factor HI by analyzing the constant current phase or the complete charging process data is also limited. Summary of the Invention

[0006] In response to the defects of the existing technology and the need for improvement, the present invention discloses a method for collecting health factors reflecting battery aging information based on the current curve in the constant voltage charging stage, extracting health factors with stronger applicability and mechanistic rationality, and constructing optimization algorithms and data models to estimate the health status of energy storage batteries and obtain more accurate battery health status results.

[0007] The battery SOH estimation method based on the constant voltage charging current curve disclosed in the present invention includes:

[0008] Step S1, during the full cycle aging process of the battery, based on the constant voltage charging current curve of each round, extracting the health factor HI1, health factor HI2 and health factor HI3 reflecting the battery aging data and normalizing them respectively;

[0009] Step S2, according to the normalized values ​​of the extracted health factor HI1, the health factor HI2, and the health factor HI3 data, obtaining relevant parameters based on an optimization algorithm, and training a data model; and

[0010] Step S3: input the normalized values ​​of the health factors HI1, HI2, and HI3 into the training data model to calculate the SOH of the battery.

[0011] The step S1 specifically includes:

[0012] Step S11: Preprocess the capacity data of the battery to obtain the SOH data of the battery:

[0013]

[0014] Among them, Q n is the current capacity of the battery, Q o is the rated capacity of the battery;

[0015] Step S12: extracting the health factors HI1, HI2, and HI3, wherein the health factor HI1 is the time it takes for the current to change from a preset current to a target current during the constant voltage charging stage, the health factor HI2 is the charging current turning point information, and the health factor HI3 is the radius of the charging current curve fitting circle; and

[0016] Step S13: Normalize the health factors HI1, HI2 and HI3:

[0017]

[0018] Among them, x' i The normalized values ​​of the health factors HI1, HI2 and HI3 in the i-th cycle, x i are the values ​​of the health factors HI1, HI2 and HI3 in the i-th cycle, min(X) and max(X) are the minimum and maximum values ​​of the health factors HI1, HI2 and HI3 respectively.

[0019] The step S2 specifically includes:

[0020] Step S21: construct the training data model as a multi-core extreme learning machine model and introduce the kernel method:

[0021] f(x')=Hβ

[0022] Among them, x' is the input sample data, f(x') is the output result, H is the feature mapping matrix of the hidden layer, and β is the weight between the hidden layer and the output layer. Its expression is:

[0023]

[0024] Among them, I is the identity matrix, c is the regularization parameter, T is the target vector of the training sample data, and the kernel function matrix model is:

[0025] H*=HH T

[0026]

[0027] Among them, x i ' is a vector consisting of the normalized values ​​of the health factors HI1, HI2 and HI3 of the input features under the i-th cycle, x' j is the vector composed of the normalized values ​​of the health factors HI1, HI2 and HI3 of the input feature under the j-th cycle, H* is the similarity matrix, and the output result is:

[0028]

[0029] Wherein, x' is a vector composed of the normalized values ​​of the health factors HI1, HI2 and HI3 of the input feature under a specific cycle, x' n It is a vector consisting of the normalized values ​​of the health factors HI1, HI2 and HI3 of the input features in the nth cycle.

[0030] Step S22: Optimize using the optimization algorithm to obtain the optimal solution of the relevant parameters required in the multi-core extreme learning machine model.

[0031] Preferably, step S21 further includes combining the Poly kernel function and the RBF kernel function to obtain a hybrid kernel function:

[0032] K m =ωK RBF +(1-ω)K Poly

[0033] Where ω is the weight coefficient,

[0034] K Poly (x',x i ')=(x'·x i '+b) d

[0035]

[0036] Wherein, x' is a vector composed of the normalized values ​​of the health factors HI1, HI2 and HI3 of the input features under a specific cycle, x i ' is the vector consisting of the normalized values ​​of the health factors HI1, HI2 and HI3 of the input feature under the i-th cycle, b and d are the parameters of the Poly kernel function, and σ is the parameter of the RBF kernel function.

[0037] Optionally, the optimization algorithm in step S22 is an Osprey optimization algorithm, which specifically includes:

[0038] Step S31: Set the number of ospreys T, the maximum number of iterations N, and the parameter range to be optimized;

[0039] Step S32: Initialize the relevant parameters c, b, d, σ, ω;

[0040] Step S33: Evaluate the MSE of the fitness SOH training result;

[0041] Step S34: updating the relevant parameters c, b, d, σ, ω;

[0042] Step S35: Bounds check; and

[0043] Step S36: Obtain the optimal solution of the relevant parameters c, b, d, σ, and ω.

[0044] Optionally, step S3 specifically includes:

[0045] Step S41: inputting the normalized values ​​of the health factors HI1, HI2, and HI3;

[0046] Step S42: Obtaining the optimal solution of the relevant parameters calculated by the optimization algorithm;

[0047] Step S43: training the data model;

[0048] Step S44: testing the data model; and

[0049] Step S45: Output the SOH estimation result of the battery.

[0050] Optionally, the optimization algorithms include: Osprey optimization algorithm, particle swarm optimization algorithm, grey wolf optimization algorithm and whale optimization algorithm.

[0051] Optionally, the training data model includes: a relevance vector machine model, a support vector machine model, a Gaussian process regression model and a multi-core extreme learning machine model.

[0052] The present invention discloses a method for collecting health factors reflecting battery aging information based on the current curve in the constant voltage charging stage, extracting health factors with stronger applicability and mechanistic rationality, and constructing an optimization algorithm and data model to estimate the health status of the energy storage battery and obtain more accurate battery health status results. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1a Schematic diagram of battery health factor HI1 and health factor HI2 based on a constant voltage charging current curve according to one embodiment of the present invention;

[0054] Figure 1b A schematic diagram of a battery health factor HI3 based on a constant voltage charging current curve according to an embodiment of the present invention;

[0055] Figure 2 This is a flow chart of a method for estimating battery SOH based on a constant voltage charging current curve according to one embodiment of the present invention;

[0056] Figure 3 In the flowchart of a method for estimating battery SOH based on a constant voltage charging current curve according to an embodiment of the present invention, Figure 2 Detailed step diagram of step S1;

[0057] Figure 4 In the flowchart of a method for estimating battery SOH based on a constant voltage charging current curve according to an embodiment of the present invention, Figure 2Detailed step diagram of step S2;

[0058] Figure 5 In a flowchart of a method for estimating battery SOH based on a constant-voltage charging current curve according to an embodiment of the present invention, when the optimization algorithm is the Osprey optimization algorithm and the training data model is the multi-core extreme learning machine model, a flowchart of estimating battery SOH is provided;

[0059] Figure 6 A method for estimating battery SOH based on a constant voltage charging current curve according to an embodiment of the present invention, and an iterative process diagram of tuning a multi-core extreme learning machine model using the Osprey optimization algorithm; and

[0060] Figure 7 A method for estimating battery SOH based on a constant voltage charging current curve according to an embodiment of the present invention, and a comparison chart of the actual battery SOH value and the estimated battery SOH value. DETAILED DESCRIPTION

[0061] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0062] Figure 1a FIG. 1 is a schematic diagram of a battery health factor HI1 and a battery health factor HI2 based on a constant voltage charging current curve according to an embodiment of the present invention. Figure 1a As shown, using the constant-voltage charging current curves for battery cycles 1 and 161 as an example, as the number of cycles increases and the battery ages, the dynamic processes within the battery slow down, resulting in a gradual decrease in the current decay rate during the constant-voltage charging phase and a prolonged constant-voltage charging duration, which can characterize the battery aging process. Considering the possibility of incomplete charging, the intermediate current was selected for the experiment. In one embodiment of the present invention, during the full-cycle aging process of the battery, the health factor HI1 is the time it takes for the charging current to change from a preset current to a target current under the constant-voltage charging environment. For example, if the preset current is 1.5A and the target current is 0.5A, the time interval from the charging current of 1.5A to the target current of 0.5A is the health factor HI1. Furthermore, the starting point and end point of the charging current curve are connected to obtain a line segment L, for example, the starting point is the preset current point and the end point is the target current point. The distance from the point on the charging current curve to the line segment L is calculated, where the time corresponding to the point with the maximum distance is the health factor HI2, also known as the charging current turning point information. Those skilled in the art should understand that the values ​​of the preset current and target current herein are merely examples of the present invention and are not intended to limit the present invention.

[0063] Figure 1b This figure shows a battery health factor (HI3) based on a constant-voltage charging current curve according to an embodiment of the present invention. Using the constant-voltage charging current curves for one and 161 cycles as examples, a polynomial fitting method is used to fit the charging current curve to a circle, with the radius of the circle used as the health factor (HI3). As can be seen from the figure, the health factor (HI3) gradually increases as the battery ages.

[0064] Figure 2 This is a flow chart of a method for estimating battery state of health (SOH) based on a constant-voltage charging current curve according to one embodiment of the present invention. Specifically, the method includes the following steps: Step S1: During the full-cycle aging process of the battery, based on each round of the constant-voltage charging current curve, extract and normalize the health factors HI1, HI2, and HI3 reflecting the battery aging data; Step S2: Based on the extracted normalized data of health factors HI1, HI2, and HI3, obtain relevant parameters and a training data model based on an optimization algorithm; and Step S3: Input the normalized values ​​of health factors HI1, HI2, and HI3 into the training data model to calculate the battery's state of health.

[0065] Specifically, health factors HI1, HI2, and HI3 represent data reflecting battery aging information extracted from the constant-voltage charging current curve during each cycle of battery aging. Optimization algorithms include, but are not limited to, Osprey, Particle Swarm, Grey Wolf, and Whale. Training data models include, but are not limited to, Relevance Vector Machines, Support Vector Machines, Gaussian Process Regression, and Multi-Core Extreme Learning Machines. Those skilled in the art will appreciate that batteries include, but are not limited to, lithium batteries and liquid metal batteries.

[0066] Figure 3 This is a flow chart of a method for estimating battery SOH based on a constant voltage charging current curve according to an embodiment of the present invention. Figure 2 The detailed step diagram of step S1 in FIG. 1 specifically includes the following steps:

[0067] Step S11: Preprocess the battery capacity data to obtain the battery SOH data:

[0068]

[0069] Among them, Q n is the current capacity of the battery, Q o is the rated capacity of the battery.

[0070] Step S12: extracting health factors HI1, HI2, and HI3, where the health factor HI1 is the time it takes for the current to change from a preset current to a target current during the constant voltage charging phase, the health factor HI2 is the charging current turning point information, and the health factor HI3 is the radius of the charging current curve fitting circle; and

[0071] Step S13: Normalize the health factors HI1, HI2, and HI3:

[0072]

[0073] Among them, x' i The normalized values ​​of health factors HI1, HI2 and HI3 in the i-th cycle, x i are the values ​​of health factors HI1, HI2 and HI3 under the i-th cycle, min(X) and max(X) are the minimum and maximum values ​​of health factors HI1, HI2 and HI3 respectively.

[0074] Preferably, a correlation analysis between the health factors HI1, HI2, and HI3 and the battery SOH verifies that the health factors HI1, HI2, and HI3 are strongly correlated with the battery SOH, indicating that the health factors selected by the present invention can accurately reflect battery aging. Correlation analysis is a method well known to those skilled in the art and will not be further described here to avoid redundancy.

[0075] Figure 4 In the flowchart of a method for estimating battery SOH based on a constant voltage charging current curve according to an embodiment of the present invention, Figure 2 The detailed step diagram of step S2 specifically includes the following steps:

[0076] Step S21: construct the training data model as a multi-core extreme learning machine model and introduce the kernel method:

[0077] f(x')=Hβ

[0078] Among them, x' is the input sample data, f(x') is the output result, H is the feature mapping matrix of the hidden layer, and β is the weight between the hidden layer and the output layer. Its expression is:

[0079]

[0080] Among them, I is the identity matrix, c is the regularization parameter, T is the target vector of the training sample data, and the kernel function matrix model is:

[0081] H*=HH T

[0082]

[0083] Among them, x i ' is the vector composed of the normalized values ​​of the input characteristic health factors HI1, HI2 and HI3 under the i-th cycle, x' j is the vector composed of the normalized values ​​of the input characteristic health factors HI1, HI2 and HI3 under the j-th cycle, H* is the similarity matrix, and the output result is:

[0084]

[0085] Among them, x' is a vector composed of the normalized values ​​of the input characteristic health factors HI1, HI2 and HI3 under a specific cycle, x' n It is a vector consisting of the normalized values ​​of the input features HI1, HI2, and HI3 in the nth cycle.

[0086] Specifically, the Extreme Learning Machine (ELM) is a fast learning algorithm for single-hidden-layer feedforward neural networks. It offers significant advantages when processing large amounts of data. However, its randomly initialized hidden-layer parameters can lead to model instability, making it applicable only to certain nonlinear problems. This disclosure uses a multi-core extreme learning machine as an example to illustrate the present invention, and this is not intended to limit the present invention.

[0087] Step S22: Optimize using an optimization algorithm to obtain the optimal solution for the relevant parameters required in the multi-core extreme learning machine model.

[0088] In one embodiment, the Osprey optimization algorithm is selected to tune the multi-core extreme learning machine model to obtain the optimal solution for the relevant parameters. The Osprey optimization algorithm efficiently solves complex optimization problems by simulating the process of an osprey circling in the air to find prey and diving quickly to catch prey. The Osprey optimization algorithm combines the advantages of global search and local search, and is superior to traditional optimization algorithms in terms of convergence speed and solution accuracy. The global search process aims to explore the entire space to avoid falling into the local optimum, thereby increasing the possibility of finding the global optimal solution. The local search process further optimizes the solution based on the global search, fine-tunes the individual positions of the osprey to improve the accuracy of the solution, and speeds up the convergence speed, thereby finding a better solution to the problem.

[0089] Preferably, in one embodiment, Figure 4 Step S21 in the above also includes combining the Poly kernel function and the RBF kernel function to obtain a hybrid kernel function. The Poly kernel function characterizes the overall attenuation trend of the battery SOH, and the Poly kernel function characterizes the local fluctuation of the SOH. By combining the two, a multi-core extreme learning machine model is constructed to improve the accuracy of battery SOH estimation. The Poly kernel function and the RBF kernel function are respectively:

[0090] KPoly (x',x i ')=(x'·x i '+b) d

[0091]

[0092] Among them, x' is a vector composed of the normalized values ​​of the input characteristic health factors HI1, HI2 and HI3 under a specific cycle, x i ' is a vector consisting of the normalized values ​​of the input characteristic health factors HI1, HI2, and HI3 under the i-th cycle, b and d are the parameters of the Poly kernel function, and σ is the parameter of the RBF kernel function. The hybrid kernel function based on the Poly kernel function and the RBF kernel function is as follows:

[0093] K m =ωK RBF +(1-ω)K Poly

[0094] The mixed kernel function K m is a linear combination of the Poly kernel function and the RBF kernel function, and ω is a weight coefficient. In one embodiment of the present invention, the hybrid kernel function K m Substituting into the output equation f(x'), the output result is more accurate in estimating the battery SOH than that of a single kernel function.

[0095] Figure 5 In the flowchart of the method for estimating battery SOH based on the constant voltage charging current curve according to one embodiment of the present invention, when the optimization algorithm is the Osprey optimization algorithm and the training data model is the multi-core extreme learning machine model, the flowchart of estimating battery SOH is shown. Figure 5 The dotted box on the left is the Osprey optimization algorithm flow chart, and the dotted box on the right is the multi-core extreme learning machine model training flow chart. Figure 5 As shown, the Osprey optimization algorithm includes the following steps:

[0096] Step S31: Set the number of ospreys T, the maximum number of iterations N, and the parameter range to be optimized;

[0097] Step S32: Initialize relevant parameters c, b, d, σ, ω;

[0098] Step S33: Evaluate the MSE of the fitness SOH training result;

[0099] Step S34: Update relevant parameters c, b, d, σ, ω;

[0100] Step S35: Bounds check; and

[0101] Step S36: Obtain the optimal solution of relevant parameters c, b, d, σ, and ω.

[0102] The multi-core extreme learning machine model training and calculation process includes the following steps:

[0103] Step S41: input the normalized values ​​of health factors HI1, HI2, and HI3;

[0104] Step S42: Obtaining the optimal solution of the relevant parameters calculated by the optimization algorithm;

[0105] Step S43: training data model;

[0106] Step S44: testing the multi-core extreme learning machine model; and

[0107] Step S45: Output the battery SOH estimation result.

[0108] Specifically, the battery health factors HI1, HI2, and HI3 are normalized, and their corresponding battery SOH values ​​are divided into training, validation, and test sets. In one embodiment of the present invention, the ratios of the training, validation, and test sets were set to 48%, 12%, and 40%, respectively, for experimental verification. Those skilled in the art should understand that the ratios set here are merely for illustration purposes and are not intended to limit the present invention.

[0109] First, the parameters of the Osprey optimization algorithm are initialized. In this invention, the maximum number of iterations T of the Osprey optimization algorithm is set to 50 times, and the number of Osprey individuals N is 50. The search ranges of the parameters to be optimized b, σ, ω, and c are all set to [0.01, 100], and the search range of d is set to [-2, 2]. Each Osprey individual represents a set of parameter combinations, that is, there will be 50 sets of parameter combinations in one cycle, among which one set is the best parameter. Then, 50 sets of parameters enter the next iteration to find a better parameter combination, and there will be 50 sets of solutions. After 50 iterations, a total of 2500 sets of solutions are generated. The best combination of the last 50 is selected as the optimization result of the parameters b, σ, ω, c, and d.

[0110] Next, a fitness function is established. In one embodiment of the present invention, the mean square error (MSE) is used as the fitness function. The specific expression is as follows:

[0111]

[0112] Among them, y est and y real are the estimated and true values ​​of the battery SOH, respectively, and n is the number of cycles.

[0113] Then, the Osprey optimization algorithm is used to optimize the parameters of the multi-core extreme learning machine model. The model is trained and the fitness value of each test set is recorded until the maximum number of iterations T is reached. The training is stopped and the optimal combination of relevant parameters c, b, d, σ, and ω is recorded.

[0114] Finally, the model is trained based on the optimal combination of relevant parameters c, b, d, σ, and ω, and the battery SOH of the test set is estimated to verify the model performance.

[0115] Those skilled in the art should understand that the above-mentioned Osprey optimization algorithm and multi-core extreme learning machine model are only one embodiment of the present invention, and the purpose is to illustrate the present invention and verify the effect of the present invention. The concept of the present invention is not limited to the above embodiment.

[0116] Figure 6 A method for estimating battery SOH based on a constant-voltage charging current curve, according to one embodiment of the present invention, shows the iterative process of tuning a multi-core extreme learning machine model using the Osprey optimization algorithm. As shown, the iterative error is calculated as the root mean square error (RMS), which decreases continuously until convergence is achieved, yielding the optimal combination of relevant parameters.

[0117] Figure 7 A comparison chart of the actual battery SOH value and the estimated battery SOH value is shown in a method for estimating battery SOH based on a constant-voltage charging current curve according to one embodiment of the present invention. As can be seen, the curve of the estimated battery SOH value using the hybrid kernel function in the present invention almost coincides with the curve of the actual value, with a small error. In contrast, the curves of the estimated battery SOH value using the Poly kernel function and the RBF kernel function exhibit large errors.

[0118] Specifically, the error is evaluated using two evaluation indicators: mean absolute percentage error (MAPE) and root mean squared error (RMSE), which are expressed as follows:

[0119]

[0120]

[0121] Among them, y est and y real are the estimated and true values ​​of the battery SOH, respectively, and n is the number of cycles. When the MAPE and RMSE values ​​are smaller, it means that the error of the battery SOH estimation result is smaller and the performance of the proposed method is better. The specific errors are shown in the table:

[0122]

[0123] As shown in the table above, the battery SOH tested based on the hybrid kernel function is more accurate.

[0124] It can be seen that according to the current curve collection based on the constant voltage charging stage disclosed in the present invention, health factors reflecting battery aging information can be extracted to have stronger applicability and mechanistic rationality, and optimization algorithms and data models can be constructed to accurately estimate the health status of the battery and obtain more accurate battery health status results.

[0125] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A battery SOH estimation method based on a constant voltage charging current curve, characterized in that: include: Step S1, during the full cycle aging process of the battery, based on each round of the constant voltage charging current curve, extracting the health factor HI1, health factor HI2 and health factor HI3 reflecting the battery aging data and performing normalization processing; The step S1 specifically includes: Step S11: Preprocess the capacity data of the battery to obtain the SOH data of the battery: Among them, Q n is the current capacity of the battery, Q o is the rated capacity of the battery; Step S12: extracting the health factors HI1, HI2, and HI3, wherein the health factor HI1 is the time it takes for the current to change from a preset current to a target current during the constant voltage charging stage, the health factor HI2 is the charging current turning point information, and the health factor HI3 is the radius of the charging current curve fitting circle; and Step S13: Normalize the health factors HI1, HI2, and HI3: Among them, x' i is the normalized value of the health factors HI1, HI2 and HI3 in the i-th cycle, x i is the value of the health factors HI1, HI2 and HI3 under the i-th cycle, min(X) and max(X) are the minimum and maximum values ​​of the health factors HI1, HI2 and HI3 respectively; step S2, according to the normalized values ​​of the extracted health factors HI1, HI2 and HI3 data, obtaining relevant parameters based on the optimization algorithm and training data model; and Step S3: input the normalized values ​​of the health factors HI1, HI2, and HI3 into the training data model to calculate the SOH of the battery.

2. The battery SOH estimation method based on constant voltage charging current curve according to claim 1, characterized in that: The step S2 specifically includes: Step S21: construct the training data model as a multi-core extreme learning machine model and introduce the kernel method: f(x')=Hβ Among them, x' is the input sample data, f(x') is the output result, H is the feature mapping matrix of the hidden layer, and β is the weight between the hidden layer and the output layer. Its expression is: Among them, I is the identity matrix, c is the regularization parameter, T is the target vector of the training sample data, and the kernel function matrix model is: H*=HH T Among them, x' i is a vector consisting of the normalized values ​​of the health factors HI1, HI2, and HI3 of the input features under the i-th cycle, x' j is the vector composed of the normalized values ​​of the health factors HI1, HI2 and HI3 of the input feature under the j-th cycle, H* is the similarity matrix, and the output result is: Among them, x' is a vector composed of the normalized values ​​of the input features HI1, HI2 and HI3 in a specific cycle, x' n is a vector consisting of the normalized values ​​of the input features HI1, HI2, and HI3 in the nth cycle; Step S22: Optimize using the optimization algorithm to obtain the optimal solution of the relevant parameters required in the multi-core extreme learning machine model.

3. The battery SOH estimation method based on constant voltage charging current curve according to claim 2, characterized in that: The step S21 further includes combining the Poly kernel function and the RBF kernel function to obtain a hybrid kernel function: K m =ωK RBF (x',x' i )+(1-ω)K Poly (x',x' i ) Where ω is the weight coefficient, K Poly (x',x' i )=(x'·x' i +b) d Among them, x' is a vector composed of the normalized values ​​of the input features HI1, HI2 and HI3 in a specific cycle, x' i is the vector composed of the normalized values ​​of the input features HI1, HI2 and HI3 in the i-th cycle, b and d are the parameters of the Poly kernel function, and σ is the parameter of the RBF kernel function.

4. The battery SOH estimation method based on constant voltage charging current curve according to claim 2, characterized in that: The optimization algorithm in step S22 is the Osprey optimization algorithm, which specifically includes: Step S31: Set the number of ospreys T, the maximum number of iterations N, and the parameter range to be optimized; Step S32: Initialize the relevant parameters c, b, d, σ, ω; Step S33: Evaluate the MSE of the fitness SOH training result; Step S34: updating the relevant parameters c, b, d, σ, ω; Step S35: Bounds check; and Step S36: Obtain the optimal solution of the relevant parameters c, b, d, σ, and ω.

5. The battery SOH estimation method based on constant voltage charging current curve according to claim 1, characterized in that: The step S3 specifically includes: Step S41: inputting the normalized values ​​of the health factors HI1, HI2, and HI3; Step S42: Obtaining the optimal solution of the relevant parameters calculated by the optimization algorithm; Step S43: training the data model; Step S44: testing the data model; and Step S45: Output the SOH estimation result of the battery.

6. The battery SOH estimation method based on constant voltage charging current curve according to claim 1, characterized in that: The optimization algorithms include: Osprey optimization algorithm, particle swarm optimization algorithm, grey wolf optimization algorithm and whale optimization algorithm.

7. The battery SOH estimation method based on constant voltage charging current curve according to claim 1, characterized in that: The training data models include: a relevance vector machine model, a support vector machine model, a Gaussian process regression model and a multi-core extreme learning machine model.

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