Lithium ion battery health state estimation method based on open-circuit voltage curve reconstruction and computer readable storage medium

Abstract

The invention relates to the technical field of batteries, in particular to a lithium ion battery health state estimation method based on open-circuit voltage curve reconstruction and a computer readable storage medium, and the method comprises the following steps: extracting a first feature point related to the aging of a template lithium battery from an incremental capacity curve, finding out a second feature point corresponding to the first feature point from the open-circuit voltage curve; obtaining a charging curve of the template lithium battery, dividing the charging curve into data fragments with fixed lengths, and further constructing an open-circuit voltage reconstruction model of the template lithium battery; optimizing parameters of the open-circuit voltage reconstruction model; and obtaining a charging data fragment of the target lithium battery, inputting the charging data fragment into the open-circuit voltage reconstruction model to obtain an aging feature point corresponding to the target lithium battery, and performing reconstruction in combination with the aging feature point to obtain an open-circuit voltage capacity curve so as to complete state-of-health estimation of the target lithium battery. According to the invention, accurate estimation of the lithium battery health state is realized.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a lithium-ion battery health state estimation method based on open circuit voltage curve reconstruction and a computer-readable storage medium. Background Art

[0002] As a key component of modern energy technology, lithium batteries have strong support for their application in many fields, such as green transportation (such as electric vehicles), energy storage (electric ships and smart homes), communication equipment (wireless devices), and aerospace industries due to their fast charging and remarkable service life. However, with the frequent charging and discharging of batteries, their electrochemical properties will gradually degrade, which may lead to increased internal resistance and reduced battery endurance, and even potential risks of safety accidents. Therefore, accurate prediction of lithium battery capacity is a crucial link. This technology not only helps to optimize the battery management system, but also can identify serious capacity decay in advance through real-time monitoring of battery capacity. The early warning mechanism of the battery management system can effectively avoid emergency operations before the loss of key performance, thereby ensuring the stable operation of the energy storage system and the safety of users. Through regular capacity maintenance or timely replacement, we can extend the service life of the battery, reduce operating costs, and minimize the safety hazards caused by battery failure.

[0003] Battery capacity prediction is one of the core tasks of battery management system (BMS) and has been widely studied. At present, model-based methods such as electrochemical models use the internal chemical reaction principles of batteries to calculate and predict. This method has a good theoretical basis, but in practical applications, its effect will be significantly affected by external factors such as current characteristics, ambient temperature and material properties, and the accuracy and robustness of the model are limited. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a method for estimating the health state of a lithium-ion battery based on open circuit voltage curve reconstruction, which reconstructs the open circuit voltage curve by partial charging segment data of the lithium battery, thereby realizing rapid estimation of the health state of the lithium battery and improving the accuracy and robustness of the prediction.

[0005] The present invention provides a method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction, comprising the following steps: S1, performing an open circuit battery test on the template lithium battery to obtain an open circuit voltage curve, and then calculating an incremental capacity curve of the template lithium battery through the open circuit voltage curve; S2, extracting a first characteristic point related to the aging of the template lithium battery from the incremental capacity curve, and finding a second characteristic point corresponding to the first characteristic point from the open circuit voltage curve; S3, obtaining a set amount of the first characteristic points and the second characteristic points of the template lithium battery as a training data set, obtaining a charging curve of the template lithium battery, dividing the charging curve into data segments of fixed length, and then constructing an open circuit voltage reconstruction model of the template lithium battery; S4, optimizing the parameters of the open circuit voltage reconstruction model, and determining the mapping relationship between the data segment and the aging characteristic point reflecting the health status of the template lithium battery; S5, obtaining charging data segments of the target lithium battery, inputting the charging data segments into the open circuit voltage reconstruction model to obtain aging characteristic points corresponding to the target lithium battery, and reconstructing the open circuit voltage capacity curve in combination with the aging characteristic points to complete the health status estimation of the target lithium battery.

[0006] The further improvement of the lithium-ion battery health status estimation method based on open circuit voltage curve reconstruction of the present invention is that step S2 is specifically, The first coordinates corresponding to two troughs and one peak in the incremental capacity curve are extracted, and the first coordinates are defined as the first characteristic point. Then, the voltage value and the charging capacity value corresponding to the open circuit voltage curve are found according to the first coordinates, and the voltage value and the charging capacity value are defined as the second characteristic point.

[0007] A further improvement of the lithium-ion battery health status estimation method based on open circuit voltage curve reconstruction of the present invention is that in step S3, dividing the charging curve into data segments of fixed length comprises the following steps: Define the voltage range corresponding to the fixed length And the sampling step , and obtain the sampling set ,in, , Combined with the ampere-hour integration method, the charge corresponding to the sampled data is calculated. , Get the input vector based on several charges , and then constructing the open circuit voltage reconstruction model according to the input vector; in, Indicates the starting voltage, Indicates the termination voltage, represents the number of sampling points, Indicates the charging current of the template lithium battery, The expression representing the charging voltage versus time is, represents the sampling point sequence, Indicates The charge corresponding to each sampling point.

[0008] A further improvement of the lithium-ion battery health status estimation method based on open circuit voltage curve reconstruction of the present invention is that optimizing the open circuit voltage reconstruction model in step S4 includes: S41, calculate the gradient during iteration , first-order moment estimation , second-order moment estimation , in, represents the first decay rate, represents the second decay rate, represents the gradient of the objective function being optimized, Indicates the corresponding time step The parameter values ​​evaluated in the iteration at time, Indicates the time step The first moment estimate of time, Indicates the time step Second moment estimation of the moment; S42, calculate the bias-corrected first-order moment estimate , bias-corrected second-order moment estimate ; S43, updating the parameters of the open circuit voltage reconstruction model , in, represents the initial learning rate, Indicates setting parameters. Indicates the corresponding time step The parameter value evaluated in the iteration at time.

[0009] The further improvement of the lithium-ion battery health state estimation method based on open circuit voltage curve reconstruction of the present invention is that step S5 combines the aging characteristic point reconstruction to obtain the open circuit voltage capacity curve to complete the health state estimation of the target lithium battery, and the specific steps are: S51, an initial reference data set with aging feature points is set in the open circuit voltage reconstruction model, the initial reference data set includes L open circuit voltages and L charging capacity values, and the L open circuit voltages and L charging capacity values ​​are arranged in chronological order to obtain a reference data set ; S52, matching the aging characteristic points in the reference data set with the aging characteristic points of the target lithium battery, and selecting the voltage offset coefficient , Capacity offset coefficient , Curve expansion coefficient And structure is the loss coefficient, and the particle swarm algorithm is combined with the particle swarm optimization algorithm to minimize the loss coefficient and obtain the optimal matching coefficient; S53, combining the optimal matching coefficient, transforming the data of the aging characteristic points of the reference data set to obtain the open circuit voltage capacity curve of the target lithium battery, in, Indicates the charge amount of the aging characteristic point of the target lithium battery corresponding to the i-th sampling point, express The corresponding open circuit voltage, Indicates the open circuit voltage of the aging characteristic point of the target lithium battery corresponding to the i-th sampling point in the reference data set; S54, the capacity of the open circuit voltage capacity curve corresponding to the upper limit cut-off voltage of the target lithium battery is the estimated capacity of the target lithium battery, combined with The health status of the target lithium battery can be obtained, where: Indicates the estimated capacity, Indicates the rated capacity, Indicates the health status of the target lithium battery.

[0010] A further improvement of the lithium-ion battery health status estimation method based on open circuit voltage curve reconstruction of the present invention is that the loss coefficient is expressed as .

[0011] A further improvement of the lithium-ion battery health status estimation method based on open circuit voltage curve reconstruction of the present invention is that step S1 comprises: S11: Discharge the template lithium battery to a cut-off voltage at a rate of 0.5C; S12: After the template lithium battery is left to stand for 1 hour, it is charged in a constant current and constant voltage mode at a rate of 0.5C. When the charging current drops to the set value, the complete charging capacity is recorded as the template lithium battery capacity; S13: After the template lithium battery is left to stand for 15 minutes, it is discharged at a constant current rate of 0.05C until it is discharged to a cut-off voltage, thereby obtaining an open circuit voltage curve of the template lithium battery; S14: Combined with the open circuit voltage curve, the incremental capacity curve is calculated. in, Indicates the incremental capacity of the target lithium battery, Indicates the preset voltage interval, Indicates the capacity increment change value of the target lithium battery within the preset voltage interval. Indicates the capacity change value of the target lithium battery within the preset voltage interval. Indicates the preset voltage change value.

[0012] The present invention also provides a computer-readable storage medium, which includes a lithium-ion battery health status estimation method program based on open circuit voltage curve reconstruction. When the lithium-ion battery health status estimation method program based on open circuit voltage curve reconstruction is executed by a processor, a lithium-ion battery health status estimation method based on open circuit voltage curve reconstruction as described above is implemented.

[0013] The above one or more technical solutions in the embodiments of the present invention have wider applicability and can adaptively capture and predict battery performance changes; an open circuit voltage reconstruction model is established through the open circuit voltage curves of several template lithium batteries, and the aging characteristic points of the target lithium battery can be predicted based on the open circuit voltage reconstruction model. The open circuit voltage capacity curve of the target lithium battery is reconstructed based on the aging characteristic points, and the health status of the target lithium battery can be intuitively derived based on the changes in the open circuit voltage capacity curve.

[0014] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction provided by an embodiment of the present invention.

[0017] Figure 2 It is the incremental capacity curve corresponding to the template lithium battery in different aging states in the embodiment of the present invention.

[0018] Figure 3 It is a structural diagram of a CNN-Transformer according to an embodiment of the present invention.

[0019] Figure 4 It is a fitting diagram of the OCV curve of the target lithium battery of the embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0021] Combine the following Figure 1 A method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction of the present invention is described, comprising the following steps: S1, performing an open circuit battery test on the template lithium battery to obtain an open circuit voltage curve, and then calculating an incremental capacity curve of the template lithium battery through the open circuit voltage curve; S2, extracting a first characteristic point related to the aging of the template lithium battery from the incremental capacity curve, and finding a second characteristic point corresponding to the first characteristic point from the open circuit voltage curve; S3, obtaining a set amount of first characteristic points and second characteristic points of the template lithium battery as a training data set, obtaining a charging curve of the template lithium battery, dividing the charging curve into data segments of fixed length, and then constructing an open circuit voltage reconstruction model of the template lithium battery; S4, optimizing the parameters of the open circuit voltage reconstruction model, and determining the mapping relationship between the data segments and the aging characteristic points reflecting the health status of the template lithium battery; S5, obtaining charging data segments of the target lithium battery, inputting the charging data segments into an open circuit voltage reconstruction model to obtain aging characteristic points corresponding to the target lithium battery, and reconstructing the open circuit voltage capacity curve in combination with the aging characteristic points to complete the health status estimation of the target lithium battery.

[0022] In a preferred implementation case of the lithium-ion battery health status estimation method based on open circuit voltage curve reconstruction of the present invention, step S2 is specifically, The first coordinates corresponding to two troughs and one peak in the incremental capacity curve are extracted, and the first coordinates are defined as the first characteristic point. Then, the voltage value and the charging capacity value corresponding to the open circuit voltage curve are found according to the first coordinates, and the voltage value and the charging capacity value are defined as the second characteristic point.

[0023] By extracting the first coordinates corresponding to two troughs and one peak, a value within a smaller period can be extracted, thereby improving the accuracy of data extraction.

[0024] In a specific implementation case, Figure 3As shown, the open circuit voltage reconstruction model includes a convolution layer, a pooling layer, a convolution layer, a pooling layer, a fully connected layer, a position encoding layer, a multi-head self-attention mechanism layer, a residual connection or a normalization layer, a feedforward connection layer, a residual connection or a normalization layer, a fully connected layer, and an output layer.

[0025] Furthermore, step S3 constructs an open circuit voltage reconstruction model of the template lithium battery, and the specific steps are: S31, construct a dual-channel convolution layer and a maximum pooling layer. Each convolution layer is connected to a pooling layer to reduce the risk of overfitting. S32, first convolution layer: for the input vector H, apply one-dimensional convolution operation and maximum pooling operation, in is the weight of the convolution kernel, is the bias term of the convolution kernel, * represents the convolution operation, and Is an activation function used to increase nonlinearity. This layer extracts the features of time series data by applying convolution operations. is the pooling operation, is the output of the first layer of convolution; S33, the second convolution layer is immediately followed by the first convolution layer, and the output of the first convolution layer As the input of the second convolution layer: in, is the weight of the second layer convolution kernel, is the bias of the second convolution kernel, is the output of the second convolution layer; To construct the convolutional neural network (CNN) part of the open circuit voltage reconstruction model; S34, build the Transformer part. The model uses 4 attention heads, which are composed of encoder, multi-head self-attention mechanism, feedforward neural network, residual connection and layer normalization. The self-attention mechanism can be expressed as: in, matrices representing queries, keys, and values, respectively, is the dimension of the key matrix, used to scale the dot product results to prevent gradient vanishing, Represents a transposed calculation.

[0026] The core calculation process of the self-attention mechanism is as follows: is the attention head of each self-attention mechanism; in, is the specific transformation weight that concatenates the results of the multi-head self-attention mechanism, , , is the weight of the self-attention operation; In order to enhance the model's ability to fit complex states, two fully connected layers are added to enhance the model's expressiveness. The essence of the feedforward neural network is a two-layer fully connected layer. The activation function of the first layer is Relu, and the second layer does not use an activation function. The implementation process is shown in the following formula: For each sub-layer (such as the self-attention layer and the feed-forward network), residual connections and layer normalization are applied, as shown in the following formula: ; This completes the CNN-Transformer structure of the open circuit voltage reconstruction model.

[0027] Furthermore, dividing the charging curve into data segments of fixed length in step S3 includes the following steps: Define the voltage range corresponding to the fixed length And the sampling step , and obtain the sampling set ,in, , Combined with the ampere-hour integration method, the charge corresponding to the sampled data is calculated. , Get the input vector based on several charge quantities , and then construct an open circuit voltage reconstruction model according to the input vector; in, represents the starting voltage, Indicates the termination voltage, represents the number of sampling points, Indicates the charging current of the template lithium battery, The expression representing the charging voltage versus time is, represents the sampling point sequence, Indicates The charge corresponding to each sampling point.

[0028] Furthermore, in step S4, optimizing the open circuit voltage reconstruction model includes: S41, calculate the gradient during iteration , first-order moment estimation , second-order moment estimation , in, represents the first decay rate, , represents the second decay rate, , represents the gradient of the objective function being optimized, Indicates the corresponding time step The parameter values ​​evaluated in the iteration at time, Indicates the time step The first moment estimate of time, Indicates the time step Second moment estimation of the moment; S42, calculate the bias-corrected first-order moment estimate , bias-corrected second-order moment estimate ; S43, updating the parameters of the open circuit voltage reconstruction model , in, represents the initial learning rate. In a specific embodiment, Set to 0.001, represents a setting parameter. In a specific embodiment, Set as , Indicates the corresponding time step The parameter value evaluated in the iteration at time.

[0029] Specifically, it also includes using the mean absolute percentage error as the basic loss function, and using the mean absolute percentage error to calculate the average percentage error between the predicted value and the true value, thereby ensuring that the model can effectively adjust parameters during the training process to minimize the prediction error and prevent the occurrence of overfitting.

[0030] Furthermore, step S5 combines the aging characteristic points to reconstruct an open circuit voltage capacity curve to complete the health status estimation of the target lithium battery. The specific steps are: S51, an initial reference data set with aging feature points is set in the open circuit voltage reconstruction model, the initial reference data set includes L open circuit voltages and L charging capacity values, and the L open circuit voltages and L charging capacity values ​​are arranged in chronological order to obtain a reference data set , S52, matching the aging characteristic points in the reference data set with the aging characteristic points of the target lithium battery, and selecting the voltage offset coefficient , Capacity offset coefficient , Curve expansion coefficient And structure is the loss coefficient, and the particle swarm algorithm is combined with the particle swarm optimization algorithm to minimize the loss coefficient and obtain the optimal matching coefficient; S53, combining the optimal matching coefficient, transforming the data of the aging characteristic points of the reference data set to obtain the open circuit voltage capacity curve of the target lithium battery, in, Indicates the charge amount of the aging characteristic point of the target lithium battery corresponding to the i-th sampling point, express The corresponding open circuit voltage, Indicates the open circuit voltage of the aging characteristic point of the target lithium battery corresponding to the i-th sampling point in the reference data set; S54, the capacity of the open circuit voltage capacity curve corresponding to the upper limit cut-off voltage of the target lithium battery is the estimated capacity of the target lithium battery, combined with The health status of the target lithium battery can be obtained, where: Indicates the estimated capacity, Indicates the rated capacity, Indicates the health status of the target lithium battery.

[0031] In a specific embodiment, the upper cut-off voltage of the target lithium battery is obtained by measurement, and then the corresponding estimated capacity can be obtained according to the upper cut-off voltage, that is, the corresponding , and then we can find the corresponding , thus calculating .

[0032] Specifically, the loss coefficient is expressed as .

[0033] In a specific implementation case, Figure 4 As shown, Figure 4 To predict the health status of the target lithium battery, the reference OCV curve is the capacity-voltage curve generated by the reference data set in the open circuit voltage reconstruction model, the real OCV curve is the capacity-voltage curve of the target lithium battery, and the estimated OCV curve is the capacity-voltage curve of the target lithium battery after being processed by the open circuit voltage reconstruction model. According to the capacity-voltage curve and the current voltage of the lithium battery, the current capacity of the target lithium battery can be estimated, and thus the health status of the target lithium battery can be estimated.

[0034] Further, step S1 includes: S11: Discharge the template lithium battery to a cut-off voltage at a rate of 0.5C; S12: After the template lithium battery is left to stand for 1 hour, it is charged in a constant current and constant voltage mode at a rate of 0.5C. When the charging current drops to a set value, in a specific embodiment, the set value is 0.02mAh, the complete charging capacity is recorded as the template lithium battery capacity; S13: After the template lithium battery is left to stand for 15 minutes, it is discharged at a constant current rate of 0.05C until it is discharged to a cut-off voltage, thereby obtaining an open circuit voltage curve of the template lithium battery; S14: Combined with the open circuit voltage curve, the incremental capacity curve is calculated. in, Indicates the incremental capacity of the target lithium battery, Indicates the preset voltage interval, Indicates the capacity increment change value of the target lithium battery within the preset voltage interval. Indicates the capacity change value of the target lithium battery within the preset voltage interval. Indicates the preset voltage change value.

[0035] In a specific implementation case, Figure 2 As shown, by obtaining the incremental capacity curves of lithium batteries with different templates, the lithium batteries with different templates are cycled 60 times, 146 times, 232 times, 332 times and 420 times respectively, and the incremental capacity curves are calculated through the OCV curves.

[0036] The present invention also provides a computer-readable storage medium, which includes a lithium-ion battery health status estimation method program based on open circuit voltage curve reconstruction. When the lithium-ion battery health status estimation method program based on open circuit voltage curve reconstruction is executed by a processor, the above-mentioned lithium-ion battery health status estimation method based on open circuit voltage curve reconstruction is implemented.

[0037] The above one or more technical solutions in the embodiments of the present invention have wider applicability and can adaptively capture and predict battery performance changes; an open circuit voltage reconstruction model is established through the open circuit voltage curves of several template lithium batteries, and the aging characteristic points of the target lithium battery can be predicted based on the open circuit voltage reconstruction model. The open circuit voltage capacity curve of the target lithium battery is reconstructed based on the aging characteristic points, and the health status of the target lithium battery can be intuitively derived based on the changes in the open circuit voltage capacity curve.

[0038] 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 embodiments of the present invention.

Claims

1. A method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction, characterized in that: The steps include: S1, performing an open circuit battery test on the template lithium battery to obtain an open circuit voltage curve, and then calculating an incremental capacity curve of the template lithium battery through the open circuit voltage curve; S2, extracting a first characteristic point related to the aging of the template lithium battery from the incremental capacity curve, and finding a second characteristic point corresponding to the first characteristic point from the open circuit voltage curve; S3, obtaining a set amount of the first characteristic points and the second characteristic points of the template lithium battery as a training data set, obtaining a charging curve of the template lithium battery, dividing the charging curve into data segments of fixed length, and then constructing an open circuit voltage reconstruction model of the template lithium battery; S4, optimizing the parameters of the open circuit voltage reconstruction model, and determining the mapping relationship between the data segment and the aging characteristic point reflecting the health status of the template lithium battery; S5, obtaining charging data segments of the target lithium battery, inputting the charging data segments into the open circuit voltage reconstruction model to obtain aging characteristic points corresponding to the target lithium battery, and reconstructing the open circuit voltage capacity curve in combination with the aging characteristic points to complete the health status estimation of the target lithium battery.

2. The method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction according to claim 1, characterized in that: Step S2 specifically includes: The first coordinates corresponding to two troughs and one peak in the incremental capacity curve are extracted, and the first coordinates are defined as the first characteristic point. Then, the voltage value and the charging capacity value corresponding to the open circuit voltage curve are found according to the first coordinates, and the voltage value and the charging capacity value are defined as the second characteristic point.

3. The method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction according to claim 1, characterized in that: Dividing the charging curve into data segments of fixed length in step S3 includes the following steps: Define the voltage range corresponding to the fixed length And the sampling step , and obtain the sampling set ,in, , Combined with the ampere-hour integration method, the charge corresponding to the sampled data is calculated. ， Get the input vector based on several charge quantities , and then constructing the open circuit voltage reconstruction model according to the input vector; in, Indicates the starting voltage, Indicates the termination voltage, represents the number of sampling points, Indicates the charging current of the template lithium battery, The expression representing the charging voltage versus time is, represents the sampling point sequence, Indicates The charge corresponding to each sampling point.

4. The method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction according to claim 3, characterized in that: Optimizing the open circuit voltage reconstruction model in step S4 includes: S41, calculate the gradient during iteration , first-order moment estimation , second-order moment estimation , in, represents the first decay rate, represents the second decay rate, represents the gradient of the objective function being optimized, Indicates the corresponding time step The parameter values ​​evaluated in the iteration at time, Indicates the time step The first moment estimate of time, Indicates the time step Second moment estimation of the moment; S42, calculate the bias-corrected first-order moment estimate , bias-corrected second-order moment estimate ; S43, updating the parameters of the open circuit voltage reconstruction model, in, represents the initial learning rate, Indicates setting parameters. Indicates the corresponding time step The parameter value evaluated in the iteration at time.

5. The method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction according to claim 4, characterized in that: Step S5 combines the aging characteristic points to reconstruct and obtain an open circuit voltage capacity curve to complete the health status estimation of the target lithium battery. The specific steps are: S51, an initial reference data set with aging feature points is set in the open circuit voltage reconstruction model, the initial reference data set includes L open circuit voltages and L charging capacity values, and the L open circuit voltages and L charging capacity values ​​are arranged in chronological order to obtain a reference data set ; S52, matching the aging characteristic points in the reference data set with the aging characteristic points of the target lithium battery, and selecting the voltage offset coefficient , Capacity offset coefficient , Curve expansion coefficient And structure is the loss coefficient, and the particle swarm algorithm is combined with the particle swarm optimization algorithm to minimize the loss coefficient and obtain the optimal matching coefficient; S53, combining the optimal matching coefficient, transforming the data of the aging characteristic points of the reference data set to obtain the open circuit voltage capacity curve of the target lithium battery, in, Indicates the charge amount of the aging characteristic point of the target lithium battery corresponding to the i-th sampling point, express The corresponding open circuit voltage, Indicates the open circuit voltage of the aging characteristic point of the target lithium battery corresponding to the i-th sampling point in the reference data set; S54, the capacity of the open circuit voltage capacity curve corresponding to the upper limit cut-off voltage of the target lithium battery is the estimated capacity of the target lithium battery, combined with The health status of the target lithium battery can be obtained, where: Indicates the estimated capacity, Indicates the rated capacity, Indicates the health status of the target lithium battery.

6. The method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction according to claim 5, characterized in that: The loss coefficient is expressed as .

7. The method for estimating the health status of a lithium-ion battery based on open circuit voltage curve reconstruction according to claim 1, characterized in that: Step S1 includes: S11: Discharge the template lithium battery to a cut-off voltage at a rate of 0.5C; S12: After the template lithium battery is left to stand for 1 hour, it is charged in a constant current and constant voltage mode at a rate of 0.5C. When the charging current drops to the set value, the complete charging capacity is recorded as the template lithium battery capacity; S13: After the template lithium battery is left to stand for 15 minutes, it is discharged at a constant current rate of 0.05C until it is discharged to a cut-off voltage, thereby obtaining an open circuit voltage curve of the template lithium battery; S14: Combined with the open circuit voltage curve, the incremental capacity curve is calculated. in, Indicates the incremental capacity of the target lithium battery, Indicates the preset voltage interval, Indicates the capacity increment change value of the target lithium battery within the preset voltage interval. Indicates the capacity change value of the target lithium battery within the preset voltage interval. Indicates the preset voltage change value.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a lithium-ion battery health state estimation method program based on open circuit voltage curve reconstruction. When the lithium-ion battery health state estimation method program based on open circuit voltage curve reconstruction is executed by a processor, a lithium-ion battery health state estimation method based on open circuit voltage curve reconstruction as described in any one of claims 1 to 7 is implemented.

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