Battery pack health state prediction method, device, equipment, medium and program product

Abstract

The invention provides a battery pack health state prediction method, device and equipment, a medium and a program product, and relates to the technical field of data processing. Comprising the following steps: acquiring battery pack temperature data, battery pack current data and battery pack voltage data of at least two battery packs in a preset time period; calculating the temperature difference of the battery packs at each time point based on the battery pack temperature data, and calculating the temperature similarity between the battery packs according to the temperature difference; calculating current similarity between the battery packs based on the frequency domain representation of the battery pack current data; calculating a covariance matrix of voltage change based on the voltage data of the battery packs, and calculating the voltage similarity between the battery packs according to the covariance matrix; constructing a temperature adjacency diagram, a current adjacency diagram and a voltage adjacency diagram based on the temperature similarity, the current similarity and the voltage similarity; and predicting the health state of the battery pack based on the temperature adjacency diagram, the current adjacency diagram and the voltage adjacency diagram through a preset health state prediction model. According to the invention, the accuracy of the SOH prediction result can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing, and particularly to a method, device, equipment, medium and program product for predicting the state of health of a battery pack. Background Art

[0002] In the related art, the prediction of the state of health (SOH) of a battery pack is crucial for the efficient operation of an energy storage power station. At present, the prediction of the SOH of a battery pack mostly relies on traditional physical models or statistical models. Among them, physical models are usually constructed based on the electrochemical principles and physical characteristics of batteries. Since the chemical reactions and physical characteristics inside the batteries are difficult to describe and simulate with accurate physical equations, the constructed physical models are relatively simplified, thus affecting the prediction accuracy of the SOH of the battery pack. Statistical models usually analyze historical data to find the correlation laws between the battery health state and various factors. However, due to the characteristics of high dimensionality, non-linearity, and strong noise of the data generated during the operation of the battery pack, statistical models have obvious deficiencies in processing such complex data, resulting in low accuracy when dealing with complex data. Summary of the Invention

[0003] The present disclosure provides a method, device, equipment, medium and program product for predicting the state of health of a battery pack. The technical solution of the present disclosure is as follows: In a first aspect, the present disclosure provides a method for predicting the state of health of a battery pack, including: Obtaining battery pack operation data of at least two battery packs within a preset time period; wherein, the battery pack operation data includes battery pack temperature data, battery pack current data, and battery pack voltage data, and the battery pack operation data is represented in the form of a time series; Based on the battery pack temperature data, calculating the temperature difference of the battery pack at each time point, and based on the temperature difference at each time point, calculating the temperature similarity between the battery packs; Based on the frequency domain representation of the battery pack current data, calculating the current similarity between the battery packs; Based on the battery pack voltage data, calculating the covariance matrix of the voltage change, and based on the covariance matrix, calculating the voltage similarity between the battery packs; Based on the temperature similarity, current similarity, and voltage similarity between the battery packs, constructing a temperature adjacency graph, a current adjacency graph, and a voltage adjacency graph; Predicting the state of health of each battery pack through a preset state of health prediction model based on the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph; wherein, the preset state of health prediction model is constructed based on a graph convolutional network.

[0004] In a possible implementation manner, calculating the current similarity between the battery packs based on the frequency-domain representation of the battery pack current data includes: Performing Fourier transform on the battery pack current data of each battery pack to obtain the frequency-domain representation of the battery pack current data of each battery pack; Calculating the current similarity between the battery packs based on the frequency-domain representation of the battery pack current data of each battery pack; Calculating the covariance matrix of the voltage change based on the battery pack voltage data includes: Calculating the covariance matrix of the voltage change based on the battery pack voltage data of each battery pack at each time point.

[0005] In a possible implementation manner, when the battery pack includes battery pack i and battery pack j In this case ， The calculation formula for the temperature similarity between the battery packs is:

[0006] where α The weighting factor of the temperature difference, n is an exponential parameter for adjusting the influence of temperature change on the adjacency relationship, △T ij (t ) is the temperature difference between the battery packs at each time point t , T i (t), T j (t) respectively represent the temperatures of battery pack i and battery pack j at time point t , T is the length of the time series, S temp (i, j) is the temperature similarity between the battery packs, i represents battery pack i , j represents battery pack j .

[0007] In a possible implementation manner, the calculation formula for the current similarity between the battery packs is:

[0008] where I i (f) represents the frequency-domain current signal of battery pack i ,I j (f) Represents the battery pack j of the frequency-domain current signal I i (t) Represents the battery pack i at the time point t of the frequency-domain current signal i Represents the battery pack i , j Represents the battery pack j , S current (i, j) Represents the current similarity between battery packs

[0009] In a possible implementation, the calculation formula for the voltage similarity between the battery packs is:

[0010] Wherein, and are respectively the average values of the voltages of the battery pack i and the battery pack j The mean value of the voltage, V i (t) is the voltage of the battery pack i at the time point t The voltage, V j (t) is the voltage of the battery pack j at the time point t The voltage, T is the length of the time series, Represents the battery pack i and the battery pack j The voltage similarity between

[0011] In a possible implementation, predicting the battery pack health state of each of the battery packs based on the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph through the preset health state prediction model includes: Inputting the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph into the preset health state prediction model, and processing the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph through the preset health state prediction model to obtain the graph convolution processing results of the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph; Processing the graph convolution processing results based on a time series model to obtain the long-term and short-term dependencies and the long-term time series patterns of the battery capacity change of the battery pack; wherein, the time series model includes a long short-term memory network and a Transformer; Predict the state of health of each battery pack based on the long-term dependence and long-term temporal pattern of the change in the battery capacity of the battery pack.

[0012] In a second aspect, the present disclosure provides a device for predicting the state of health of a battery pack, including: A data acquisition module, configured to acquire the battery pack operation data of at least two battery packs within a preset time period; wherein, the battery pack operation data includes battery pack temperature data, battery pack current data, and battery pack voltage data, and the battery pack operation data is represented in the form of a time series; A temperature similarity calculation module, configured to calculate the temperature difference of the battery packs at each time point based on the battery pack temperature data, and calculate the temperature similarity between the battery packs based on the temperature difference at each time point; A current similarity calculation module, configured to calculate the current similarity between the battery packs based on the frequency domain representation of the battery pack current data; A voltage similarity calculation module, configured to calculate the covariance matrix of the voltage change based on the battery pack voltage data, and calculate the voltage similarity between the battery packs based on the covariance matrix; A graph construction module, configured to construct a temperature adjacency graph, a current adjacency graph, and a voltage adjacency graph based on the temperature similarity, current similarity, and voltage similarity between the battery packs; A prediction module, configured to predict the state of health of each battery pack through a preset state of health prediction model based on the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph; wherein, the preset state of health prediction model is constructed based on a graph convolutional network.

[0013] In a third aspect, the present disclosure provides an electronic device, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the instructions to implement the method for predicting the state of health of a battery pack according to the first aspect.

[0014] In a fourth aspect, the present disclosure provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for predicting the state of health of a battery pack according to the first aspect is implemented.

[0015] In a fifth aspect, the present disclosure provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the method for predicting the state of health of a battery pack according to the first aspect is implemented.

[0016] The technical solution of the present disclosure at least brings the following beneficial effects: In an embodiment of the present disclosure, battery pack operation data of at least two battery packs within a preset time period is obtained; wherein, the battery pack operation data includes battery pack temperature data, battery pack current data, and battery pack voltage data, and the battery pack operation data is represented in the form of a time series; based on the battery pack temperature data, the temperature difference of the battery pack at each time point is calculated, and based on the temperature difference at each time point, the temperature similarity between the battery packs is calculated; based on the frequency domain representation of the battery pack current data, the current similarity between the battery packs is calculated; based on the battery pack voltage data, the covariance matrix of the voltage change is calculated, and based on the covariance matrix, the voltage similarity between the battery packs is calculated; based on the temperature similarity, current similarity, and voltage similarity between the battery packs, a temperature adjacency graph, a current adjacency graph, and a voltage adjacency graph are constructed; through a preset health state prediction model, based on the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph, the battery pack health state of each battery pack is predicted; wherein, the preset health state prediction model is constructed based on a graph convolutional network. In this way, by capturing multi-dimensional correlation features (temperature, current, voltage) between battery packs, multiple graphs (temperature adjacency graph, current adjacency graph, voltage adjacency graph, etc.) are constructed, and the state of health (SOH) of the battery packs is predicted by combining the graph convolutional network. In this way, the multi-dimensional spatio-temporal characteristics of the battery packs can be fused, the mutual influence between the battery packs and the multi-dimensional characteristics of the battery packs can be considered more comprehensively, and the accurate prediction of the SOH of the battery packs can be realized; at the same time, by combining the graph convolutional network to predict the SOH of the battery packs, the spatial dependence relationship and the time dependence relationship of the battery packs can be better processed, so that the SOH prediction result is more accurate.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present disclosure. Brief Description of the Drawings

[0018] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0019] Figure 1 It is a schematic flowchart of a method for predicting the health state of a battery pack provided by an embodiment of the present disclosure; Figure 2 It is a schematic flowchart of another method for predicting the health state of a battery pack provided by an embodiment of the present disclosure; Figure 3 It is a schematic structural diagram of a device for predicting the health state of a battery pack provided by an embodiment of the present disclosure; Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. Detailed Description of the Embodiments

[0020] To enable those of ordinary skill in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0021] It should be noted that in the embodiments of the present disclosure, there may be some existing industry solutions for certain software, components, models, etc. These should be regarded as exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solutions of the present disclosure, but it does not mean that the applicant has already or necessarily used this solution.

[0022] In the related art, the prediction of the state of health of the battery pack is crucial for the efficient operation of the energy storage power station. The battery pack capacity is the core indicator of battery health. As the battery is used, the capacity gradually decays, and the state of health (SOH) of the battery pack is usually measured by the change in capacity. With the changes in factors such as the charge-discharge cycle times, temperature, current, and voltage of the battery, the health state of the battery will show complex fluctuations, which makes the prediction of SOH challenging. Currently, most of the prediction methods for the SOH of the battery pack rely on traditional physical models or statistical models, and these methods may have problems of low accuracy and poor generalization ability when dealing with complex data. In the related art, deep learning and machine learning methods have also been widely applied to the field of battery health prediction. In particular, methods such as the long short-term memory network (LSTM) and convolutional neural network (CNN) based on time series data have achieved certain results. However, these methods usually focus on single features or information in the time dimension, ignoring the spatial correlation between battery packs and the comprehensive influence of multi-dimensional features. Especially in the energy storage power station, since the battery packs are physically related, their health states are not only affected by their own characteristics but also by the interaction with other battery packs. Therefore, traditional models are difficult to capture the spatial relationship between battery packs, thereby affecting the accuracy and reliability of SOH prediction.

[0023] To this end, the embodiments of the present disclosure provide a method, device, equipment, medium and program product for predicting the state of health of a battery pack. The method for predicting the state of health of a battery pack provided by the embodiments of the present disclosure is a method for predicting the state of health of a battery pack that integrates multi-dimensional spatio-temporal characteristics and multi-model integration. It can comprehensively consider the spatio-temporal characteristics of the battery pack, and by capturing multi-dimensional correlation features (such as capacity, temperature, current, voltage, etc.) between battery packs, achieve accurate prediction of the state of health (SOH) of the battery pack. Exemplarily, the method for predicting the state of health of a battery pack provided by the embodiments of the present disclosure can construct multiple graphs (such as a temperature adjacency graph, a current adjacency graph, a voltage adjacency graph, etc.), and combine a graph convolutional network (Graph Convolutional Network, GCN) to predict the SOH of the battery pack. In this way, it can more comprehensively consider the mutual influence between battery packs and the multi-dimensional characteristics of the battery pack; moreover, it is also an SOH prediction method that combines a multi-graph convolutional network with a time series model LSTM and a Transformer model architecture. By combining GCN, LSTM and Transformer to process the spatial and temporal dependence relationships of the battery pack, the SOH prediction result can be made more accurate. At the same time, the method for predicting the SOH of the entire battery pack that comprehensively considers the mutual influence between battery packs can optimize the SOH prediction of multiple battery packs as a whole, consider the cooperative working state and mutual influence of each battery pack in the energy storage power station, and provide a more accurate overall state of health prediction.

[0024] The technical solutions provided by the embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0025] Figure 1 The following is a flowchart of a method for predicting the state of health of a battery pack provided by the embodiments of the present disclosure. This method can be applied to a server, such as a single server or a server cluster. As Figure 1 shown, the method for predicting the state of health of a battery pack may include the following steps: S101, obtain the battery pack operation data of at least two battery packs within a preset time period.

[0026] Among them, the battery pack operation data includes battery pack temperature data, battery pack current data, and battery pack voltage data, and the battery pack operation data is represented in the form of a time series. In an embodiment of the present disclosure, when predicting the state of health of a battery pack, the operating data of the battery pack within a preset time period (e.g., a past period of time) can be obtained first. Exemplarily, the operating data of at least two battery packs within the preset time period can be obtained, and the operating data of each battery pack can include data such as battery pack temperature data, battery pack current data, and battery pack voltage data. Moreover, for each type of battery pack operating data, it can be represented in the form of a dynamic time series. As a specific example, considering that in the actual operation of an energy storage power station, the operating parameters of the battery pack have significant dynamic characteristics and multi-dimensional influences, the battery pack operating data can be processed into the following form: Capacity time series: the historical capacity change of each battery pack C i (t) , where t is time, i is the number of the battery pack.

[0027] Environmental and operating conditions characteristics: including the battery pack temperature data, battery pack current data, and battery pack voltage data of the battery pack, which are respectively represented as the temperature T i (t) of the battery pack, current I i (t) voltage V i (t) and other time series.

[0028] Each type of data is represented as a dynamic time series as follows:

[0029] where X i is the input feature matrix of the battery pack i , containing the multi-dimensional data of the time step t 1 ，t 2 ，……，t T .

[0030] S102. Based on the battery pack temperature data, calculate the temperature difference of the battery pack at each time point, and based on the temperature difference at each time point, calculate the temperature similarity between battery packs.

[0031] In an embodiment of the present disclosure, after obtaining the battery pack operation data of the battery pack within a preset period, the similarity between battery packs can be calculated, including temperature similarity. Exemplarily, based on the obtained battery pack temperature data, the temperature difference between battery packs at each time point can be calculated. As an example, considering that temperature is a key factor affecting battery performance, different battery packs may experience similar degradation patterns under similar temperature conditions. To accurately capture the temperature correlation between battery packs, a high-order similarity metric based on temperature change can be adopted. Based on this, when determining the temperature similarity between battery packs, the high-order temperature difference can be defined as follows first, and the non-linear relationship of temperature difference can be processed through a weighted exponential function to calculate the temperature difference (high-order temperature difference) between battery packs at each time point. △T ij (t) 。

[0032]

[0033] Wherein, is the weighting factor of the temperature difference, n is the exponential parameter that adjusts the influence of temperature change on the adjacency relationship, controlling the attenuation rate of temperature difference on similarity, and the battery packs i and j at the time point t have temperatures of and respectively.

[0034] After obtaining the temperature difference at each time point, based on the temperature difference at each time point, the weighted similarity between the battery packs i and j can be calculated to obtain the overall temperature similarity between the battery packs i and j as follows:

[0035] Wherein, α is the weighting factor of the temperature difference, n is the exponential parameter that adjusts the influence of temperature change on the adjacency relationship, △T ij (t ) is the temperature difference between the battery packs at each time point t , T i (t), T j (t) respectively represent the temperatures of the battery pack i and the battery pack j at the time point t , T is the length of the time series.S temp (i, j) is the temperature similarity between battery packs, i indicating a battery pack i , j indicating a battery pack j .

[0036] It can be understood that the temperature similarity between battery packs can be used to evaluate the similarity between the temperature data of different battery packs.

[0037] S103. Calculate the current similarity between battery packs based on the frequency-domain representation of the battery pack current data.

[0038] In an embodiment of the present disclosure, the current similarity between battery packs can also be calculated. Considering that during the charging and discharging process of the battery, the current fluctuation affects the battery health state. In order to accurately capture the similarity of the current fluctuations between battery packs, a dynamic frequency-domain similarity metric can be adopted to effectively reflect the periodic fluctuations of the current between battery packs. Exemplarily, the frequency-domain feature extraction of the battery pack current data can be performed first, and then the current similarity between battery packs can be calculated based on the frequency-domain representation of each battery pack current signal. It can be understood that the current similarity between battery packs can be used to evaluate the similarity between the current data of different battery packs.

[0039] S104. Calculate the covariance matrix of the voltage change based on the battery pack voltage data, and calculate the voltage similarity between battery packs based on the covariance matrix.

[0040] In an embodiment of the present disclosure, the voltage similarity between battery packs can also be calculated. Considering that the voltage fluctuation of the battery directly affects the working efficiency and capacity of the battery. In order to fully consider the correlation of the voltage changes between battery packs, a voltage change modeling based on a high-order covariance matrix can be adopted to calculate the voltage similarity. Exemplarily, the covariance matrix characterizing the voltage change of the battery pack can be calculated first based on the battery pack voltage data of the battery pack. Then, the voltage similarity between battery packs can be calculated based on the covariance matrix characterizing the voltage change of the battery pack. It can be understood that the voltage similarity between battery packs can be used to evaluate the similarity between the voltage data of different battery packs. As an example, taking the example of including battery pack 1 and battery pack 2, the temperature similarity between battery pack 1 and battery pack 2 can be calculated, the current similarity between battery pack 1 and battery pack 2 can be calculated, and the voltage similarity between battery pack 1 and battery pack 2 can be calculated.

[0041] S105. Construct a temperature adjacency graph, a current adjacency graph, and a voltage adjacency graph based on the temperature similarity, current similarity, and voltage similarity between battery packs.

[0042] In an embodiment of the present disclosure, after calculating the temperature similarity, current similarity, and voltage similarity between battery packs, multiple adjacency graphs can be constructed based on these similarity evaluation results between battery packs (including temperature similarity, current similarity, and voltage similarity between battery packs), and each adjacency graph corresponds to a feature (such as temperature similarity, current similarity, voltage similarity) to capture the multi-dimensional relationships between battery packs. In this way, the spatial dependence, temporal dependence, and steady-state dependence between battery packs can be comprehensively modeled from different levels. Exemplarily, a temperature adjacency graph associated with the thermal environment can be constructed based on the temperature similarity between battery packs. For example, the quantization result of the battery pack temperature similarity can be mapped to the adjacency graph structure to capture the thermal environment dependence relationship between battery packs, and the temperature adjacency matrix (temperature adjacency graph) is defined as follows:

[0043] where S temp (i, j) is the temperature similarity between battery packs i, j .

[0044] Exemplarily, the dynamic similarity of current fluctuations (current similarity) can be used to reflect the dynamic dependence characteristics of battery packs during charge and discharge, and the elements of the current adjacency matrix (current adjacency graph) are defined as follows:

[0045] where is the current similarity calculated through the frequency-domain similarity metric, is the time alignment error of the current fluctuations between battery packs measured by dynamic time warping (DTW).

[0046] Exemplarily, the covariance of the steady-state voltage characteristics can be quantified, that is, a long-term steady-state association graph between battery packs is constructed based on voltage similarity. The elements of the voltage adjacency matrix (voltage adjacency graph) are defined as:

[0047] where represents the voltage similarity between battery pack i and battery pack j .

[0048] In this way, by designing multi-dimensional similarity metrics based on temperature similarity, current similarity, and voltage similarity, the spatial dependence, temporal dependence, and steady-state dependence between battery packs can be comprehensively modeled from different levels.

[0049] S106. Based on the temperature adjacency graph, current adjacency graph, and voltage adjacency graph, predict the battery pack health state of each battery pack through a preset health state prediction model.

[0050] Among them, the preset health state prediction model is constructed based on a graph convolutional network.

[0051] In an embodiment of the present disclosure, after obtaining the temperature adjacency graph, current adjacency graph, and voltage adjacency graph, the battery pack health state of the battery pack can be predicted through a preset health state prediction model. Exemplarily, the temperature adjacency graph, current adjacency graph, and voltage adjacency graph can be input into the preset health state prediction model, and the preset health state prediction model processes the temperature adjacency graph, current adjacency graph, and voltage adjacency graph to predict the battery pack health state of each battery pack. As an example, the preset health state prediction model can be pre-constructed based on a graph convolutional network. The graph convolutional network can process graph-structured data, extract features by learning the connection patterns between nodes, and thus perform node-level predictions. In the prediction of the battery pack health state, the battery pack can be regarded as a node in the graph, and parameters such as temperature, current, and voltage can be used as the features of the node to predict the battery pack health state. In this way, based on the graph convolutional network, the complex relationships within the battery pack can be fully utilized, thereby improving the accuracy and reliability of the health state prediction.

[0052] In an embodiment of the present disclosure, obtain the battery pack operation data of at least two battery packs within a preset time period; wherein, the battery pack operation data includes battery pack temperature data, battery pack current data, and battery pack voltage data, and the battery pack operation data is represented in the form of a time series; based on the battery pack temperature data, battery pack current data, and battery pack voltage data of each battery pack, respectively determine the temperature similarity, current similarity, and voltage similarity between the battery packs; based on the temperature similarity, current similarity, and voltage similarity between the battery packs, construct a temperature adjacency graph, a current adjacency graph, and a voltage adjacency graph; through a preset health state prediction model, based on the temperature adjacency graph, current adjacency graph, and voltage adjacency graph, predict the battery pack health state of each battery pack; wherein, the preset health state prediction model is constructed based on a graph convolutional network. In this way, by capturing the multi-dimensional correlation features (temperature, current, voltage) between battery packs, constructing multiple graphs (temperature adjacency graph, current adjacency graph, voltage adjacency graph, etc.), and combining the graph convolutional network to predict the battery pack SOH. In this way, the multi-dimensional spatio-temporal characteristics of the battery pack can be fused, the mutual influence between battery packs and the multi-dimensional characteristics of the battery pack can be considered more comprehensively, and the accurate prediction of the battery pack SOH can be realized; at the same time, combining the graph convolutional network to predict the battery pack SOH can better handle the spatial and temporal dependence relationships of the battery pack, so that the SOH prediction result is more accurate.

[0053] In some possible embodiments, calculating the current similarity between battery packs based on the frequency-domain representation of the battery pack current data includes: Performing a Fourier transform on the battery pack current data of each battery pack to obtain the frequency-domain representation of the battery pack current data of each battery pack; Calculating the current similarity between battery packs based on the frequency-domain representation of the battery pack current data of each battery pack; Calculating the covariance matrix of voltage changes based on the battery pack voltage data, including: Calculating the covariance matrix of voltage changes based on the battery pack voltage data of each battery pack at each time point.

[0054] In the embodiments of the present disclosure, when calculating the current similarity between battery packs based on the frequency-domain representation of the battery pack current data, frequency-domain feature extraction of current fluctuations can be performed first. First, perform a Fourier transform on the current time series of the battery pack current data of the battery pack to obtain the frequency-domain representation of each battery pack current signal, as follows:

[0055] Battery pack i and j The current similarity (current fluctuation similarity) between can be represented by the cross-correlation function in the frequency domain, defined as follows:

[0056] Wherein, I i (f) represents the frequency-domain current signal of battery pack i , I j (f) represents the frequency-domain current signal of battery pack j , I i (t) represents the frequency-domain current signal of battery pack i at time point t , i represents battery pack i , j represents battery pack j , S current (i, j) represents the current similarity between battery packs.

[0057] When calculating the covariance matrix of voltage changes based on the battery pack voltage data, it can be assumed that the voltages of battery packs i and j at time point t are respectively and the covariance matrix of the voltage change can be calculated, and the correlation of the voltage changes between battery packs is reflected through the high-order covariance matrix. The covariance matrix of the voltage change is defined as follows:

[0058] wherein, and are respectively the mean values of the voltages of battery pack i and battery pack j ; V i (t) is the voltage of battery pack i at time point t ; V j (t) is the voltage of battery pack j at time point t ; T is the length of the time series.

[0059] Then, the voltage similarity between battery packs can be defined as follows:

[0060] wherein, and are respectively the mean values of the voltages of battery pack i and battery pack j ; V i (t) is the voltage of battery pack i at time point t ; V j (t) is the voltage of battery pack j at time point t ; T is the length of the time series, represents the voltage similarity between battery pack i and battery pack j .

[0061] In a possible implementation manner, based on the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph, the battery pack health state of each battery pack is predicted through a preset health state prediction model, including: Input the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph into the preset health state prediction model, and process the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph through the preset health state prediction model to obtain the graph convolution processing results of the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph; Process the graph convolution processing results based on a time series model to obtain the long - short - term dependence and long - term time series pattern of the battery capacity change of the battery pack; wherein, the time series model includes a long short - term memory network and a Transformer; Predict the state of health (SOH) of each battery pack based on the long - term dependence and long - term time series pattern of the battery capacity change of the battery pack.

[0062] In an embodiment of the present disclosure, when predicting the SOH of each battery pack through a preset SOH prediction model, a temperature adjacency graph, a current adjacency graph, and a voltage adjacency graph can be input into the preset SOH prediction model. These graphs are processed by a graph convolutional network to obtain graph convolution processing results. The graph convolutional network can process graph - structured data, extract features by learning the connection patterns between nodes, and thus perform node - level predictions. Then, the graph convolution processing results can be input into a time series model. Based on the time series model, the graph convolution processing results are processed to obtain the long - short - term dependence and long - term time series pattern of the battery capacity change of the battery pack. Exemplarily, the time series model includes a long short - term memory network (LSTM) and a Transformer. These models can effectively capture short - term patterns and long - term dependence problems in time - series data. For example, the long short - term memory network can control the flow of information through gating mechanisms (input gate, forget gate, output gate), model local time series patterns, and capture the short - term dynamic changes of the sequence. After that, based on the long - term dependence and long - term time series pattern of the battery capacity change of the battery pack, the SOH of each battery pack can be predicted. For example, the output of the time series model can be used for a classification or regression task, and machine learning or deep - learning algorithms can be used to predict the SOH of the battery pack.

[0063] It can be understood that, first of all, the embodiments of the present disclosure can construct a state of health (SOH) prediction model of the battery based on a multi - graph convolutional network (GCN). Through layer - by - layer information transfer, graph convolution operations, and multi - graph feature fusion, the complex spatial relationship between battery packs can be effectively learned, and key features helpful for SOH prediction can be extracted from multi - dimensional features. The steps include graph convolution operations, fusion of multiple graphs, and non - linear activation and other processes. Specifically, the specific implementation of this process can include: 1. Graph convolution operation and dynamic update of node features: The graph convolution operation is the core of the GCN. By propagating node information in the graph structure, the features of each node are updated. For each node of the battery pack , its feature will be updated according to the features of neighboring nodes. Specifically, it includes: 1.1 Processing of the adjacency matrix: Suppose there is an adjacency graph of a battery pack G = (V, E) , where V is the set of battery pack nodes and E is the set of edges between nodes. The dependence relationship between nodes is represented by the adjacency matrixA It is represented by. In the graph convolution operation, the role of the adjacency matrix is to define the relationships between nodes and use their weights for feature update.

[0064] 1.2 Multidimensional graph convolution update formula for fusing heterogeneous features: The graph convolution operation updates by aggregating and weighting the features of neighboring nodes. Assume that the feature of node i is , and the feature of neighboring node j is . Then, the node feature can be updated by the weighted aggregation method as follows:

[0065] Among them, is the updated feature of node i at the l th layer, is an element of the adjacency matrix, representing the connection weight between node i and node j , and are the degrees of nodes i and node j respectively (i.e., the number of adjacent nodes), is the weight matrix of the l th layer, is the bias term, is the non-linear activation function, represents the set of all nodes directly connected to node i .

[0066] 2. Multi-graph convolution feature fusion and global dependence enhancement mechanism: In the prediction of the health state of a battery pack, different adjacency graphs represent different dependence relationships between battery packs (such as temperature adjacency graph, current adjacency graph, voltage adjacency graph, etc.). To comprehensively consider these multi-dimensional features, the graph convolution layers can be fused by fusing the results of multiple graph convolution operations to improve the prediction ability of the model as follows:

[0067] Among them, is the element of the l-th layer adjacency matrix, l = 1: (temperature adjacency matrix); l = 2: (current adjacency matrix); l = 3: (voltage adjacency matrix).

[0068] Then, considering that the SOH of the battery pack has strong time dependence, especially the periodic fluctuations during the capacity attenuation process. Therefore, a time series modeling and adaptive optimization mechanism can be constructed by combining the long short-term memory network (LSTM) and the Transformer time series model to further improve the accuracy of SOH prediction. Specifically, the specific implementation of this process can include: 1. Long-term time series dependence modeling and dynamic learning based on LSTM: LSTM can effectively capture the long-term dependence of battery capacity changes and learn the dynamic characteristics of the battery pack's health state over time. The key update steps of LSTM are as follows:

[0069]

[0070]

[0071]

[0072] Among them, is the input gate, is the forget gate, is the output gate, is the cell state, is the hidden state. is the input feature at time t (i.e., the node feature output by GCN), is the hidden state of the previous time step.

[0073] 2. Long-term time series pattern recognition and adaptive attention mechanism based on Transformer: By using the biased cosine similarity and weighted fusion strategy, the self-attention mechanism becomes more flexible and takes into account the dynamic weights of different features.

[0074] Introduce the attention weights of the weighted kernel function and the bias term:

[0075]

[0076] Among them, is the learned bias term, indicating the preference for certain elements. At this time, the attention matrix will consider this additional learned information to adjust the similarity scores of different features. The symbol ⊙ represents element-wise multiplication, which can make the attention mechanism not only a weighted sum, but also emphasize which features are more important and which part of the information may be "ignored" or have a higher weight.

[0077] For the multi-head attention mechanism, considering that each head may capture different health patterns of the battery under different operating conditions (such as voltage, current, temperature, etc.), a "weighted fusion" strategy can be introduced to dynamically adjust the contribution of each head in order to better capture the temporal dependencies of the battery in different states. The weighted fusion method of the multi-head self-attention mechanism is as follows:

[0078] where, is the learnable weight of each attention head, representing the contribution of that head in the final calculation, h is the number of heads.

[0079] Secondly, spatio-temporal data fusion and multi-model integration can be carried out to predict the health state of the battery pack: Based on the spatial features extracted by the graph convolutional network, the temporal features of the battery capacity are further processed by combining the LSTM and Transformer modules to improve the adaptability of SOH prediction to time changes. Specifically, the specific implementation of this process can include: 1. GCN feature space extraction: The input graph data is processed through the GCN network to obtain the graph structure feature representation of the nodes. The GCN layer will capture the spatial relationships between the nodes.

[0080]

[0081] where, X is the node feature, A is the adjacency matrix, is the node feature representation output by the GCN.

[0082] 2. Parallel input of LSTM and Transformer and multi-level temporal feature fusion: LSTM will capture the long-term and short-term dependencies between time steps, and Transformer captures the longer-range dependencies through the self-attention mechanism, as follows:

[0083]

[0084] where, H LSTM represents the long-term and short-term dependencies, H Transformer represents the long-term temporal pattern.

[0085] 3. Adaptive spatio-temporal feature fusion and weighted decision-making mechanism: After the parallel operation of LSTM and Transformer, the outputs of the two can be fused. Through a weighted fusion mechanism, the outputs of the two are weighted and combined through a non-linear transformation, as follows:

[0086] Among them, H LSTM and H Transformer are the outputs from LSTM and Transformer, is a learnable weight matrix, is a bias term.

[0087] This approach can provide a more flexible non - linear transformation for the final output, allowing the model to dynamically adjust the contributions of LSTM and Transformer at each time step.

[0088] Finally, SOH prediction and calculation are performed, including: Based on the fused features by the trained model H final , predict the current capacity of each battery pack. The SOH of a battery pack is usually calculated by the ratio of its current capacity to the initial capacity, as follows:

[0089] Among them, is the predicted capacity of the battery pack, is the initial capacity of the battery pack.

[0090] Based on this, the battery pack health state prediction method provided by the embodiments of the present disclosure uses a graph convolutional network (GCN) to simultaneously process graph-structured data from multiple dimensions (such as temperature, current, voltage, etc.), which can effectively capture the spatial dependence relationships between battery packs. By constructing multiple adjacency graphs to represent different mutual relationships between battery packs, the prediction ability for complex battery systems is improved. The battery pack health state prediction method provided by the embodiments of the present disclosure comprehensively considers multi-dimensional features of the battery pack (such as capacity, current, voltage, temperature, etc.), and performs in-depth modeling of the battery pack health state based on spatio-temporal fusion. It learns the spatial relationships between battery packs through a graph convolutional network, and at the same time captures the temporal characteristics of battery capacity changes through temporal modeling. This way of fusing multi-dimensional features can improve the comprehensiveness, accuracy, and robustness of SOH prediction. The battery pack health state prediction method provided by the embodiments of the present disclosure also proposes an adaptive learning framework based on multi-layer feature expression, which can dynamically adjust feature weights and convolution operation parameters at different levels to enhance the model's understanding ability of the battery pack health state (SOH). Thus, it can not only capture the local features of the battery pack (such as the relationships between adjacent nodes), but also construct a unified description of the battery pack health state through global feature integration. Moreover, the battery pack health state prediction method provided by the embodiments of the present disclosure also considers the periodic volatility of battery capacity. By analyzing the change rules of battery capacity and further optimizing the model in combination with temporal data, the model can more accurately predict the periodic changes of battery capacity, thereby improving the SOH prediction accuracy. Further, by accurately predicting the SOH of the battery pack, it can also help the energy storage power station to implement precise battery maintenance and replacement strategies, thereby significantly reducing the operation and maintenance costs, timely discovering battery health problems, adopting optimized charge and discharge strategies, and improving the benefits of the energy storage system.

[0091] To make the battery pack health state prediction method provided by the embodiments of the present disclosure clearer, the following is described in conjunction with the attached Figure 2 illustrations. As Figure 2 shown, the battery pack health state prediction method includes: 1. Construct a data set, including obtaining the battery pack operation data of at least two battery packs within a preset time period and constructing a data set; wherein, the battery pack operation data includes battery pack temperature data, battery pack current data, and battery pack voltage data.

[0092] 2. Construct multi-dimensional features, including processing the data set into a form represented by a dynamic time series.

[0093] 3. Construct the spatial dependence relationship of the battery pack based on feature similarity quantification, including constructing temperature similarity, current similarity, and voltage similarity between battery packs.

[0094] 4. Construction of spatial, dynamic, and steady-state adjacency graph structures, including constructing temperature adjacency graphs, current adjacency graphs, and voltage adjacency graphs.

[0095] 5. Multi-graph convolutional network (GCN) modeling and feature fusion, including constructing a state of health (SOH) prediction model for batteries based on a multi-graph convolutional network, and learning the complex spatial relationships between battery packs through layer-by-layer information transfer, graph convolutional operations, and multi-graph feature fusion, and extracting key features helpful for SOH prediction from multi-dimensional features. It includes graph convolutional operations, fusion of multiple graphs, and non-linear activation and other processes.

[0096] 6. LSTM-based long-term time series dependence modeling and dynamic learning, including capturing the long-term and short-term dependencies of battery capacity changes and learning the dynamic features of the health state of battery packs over time.

[0097] 7. Transformer-based long-term time series pattern recognition and adaptive attention mechanism, including Transformer capturing longer-distance dependencies through self-attention mechanism.

[0098] 8. Adaptive spatio-temporal feature fusion and weighted decision-making mechanism, including after parallelizing LSTM and Transformer, fusing their outputs, and through a weighted fusion mechanism, making their outputs be weighted combined through a non-linear transformation.

[0099] 9. Test error <θ, including checking whether the test error of the model is less than a preset threshold θ.

[0100] If yes, proceed to the next step.

[0101] If no, update the hyperparameters (adjust the non-learning parameters of the model), and return to the multi-graph convolutional network modeling and feature fusion step.

[0102] 10. SOH prediction and calculation, including calculating the state of health (SOH) of the battery pack based on the prediction results of the model.

[0103] The specific implementation and technical effects of each step in this embodiment are similar to those of the above method embodiment, and will not be elaborated here.

[0104] Based on the same inventive concept, an embodiment of the present disclosure also provides a device for predicting the state of health of a battery pack. As Figure 3 shown, the device 300 for predicting the state of health of the battery pack includes: A data acquisition module 310, configured to acquire battery pack operation data of at least two battery packs within a preset time period; wherein, the battery pack operation data includes battery pack temperature data, battery pack current data, and battery pack voltage data, and the battery pack operation data is represented in the form of a time series; A temperature similarity calculation module 320, configured to calculate the temperature difference of the battery pack at each time point based on the battery pack temperature data, and calculate the temperature similarity between the battery packs based on the temperature difference at each time point; A current similarity calculation module 330, configured to calculate the current similarity between the battery packs based on the frequency domain representation of the battery pack current data; A voltage similarity calculation module 340, configured to calculate the covariance matrix of the voltage change based on the battery pack voltage data, and calculate the voltage similarity between the battery packs based on the covariance matrix; A graph construction module 350, configured to construct a temperature adjacency graph, a current adjacency graph, and a voltage adjacency graph based on the temperature similarity, current similarity, and voltage similarity between the battery packs; A prediction module 360, configured to predict the battery pack health state of each battery pack through a preset health state prediction model based on the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph; wherein, the preset health state prediction model is constructed based on a graph convolutional network.

[0105] In a possible implementation manner, the current similarity calculation module 330 is configured to: Perform Fourier transform on the battery pack current data of each battery pack to obtain the frequency domain representation of the battery pack current data of each battery pack; Calculate the current similarity between the battery packs based on the frequency domain representation of the battery pack current data of each battery pack; The voltage similarity calculation module 340 is configured to: Calculate the covariance matrix of the voltage change based on the battery pack voltage data of each battery pack at each time point.

[0106] In a possible implementation manner, when the battery pack includes battery pack i and battery pack j in the case of ， The calculation formula for the temperature similarity between the battery packs is:

[0107] Wherein, α The weighting factor of the temperature difference, n is an exponential parameter for adjusting the influence of temperature change on the adjacency relationship, △T ij (t ) is the temperature difference of the battery pack at each time point t of, T i (t), T j(t) respectively represent battery packs i and battery packs j at time point t the temperature of, T is the length of the time series, S temp (i, j) is the temperature similarity between battery packs, i represents battery pack i , j represents battery pack j .

[0108] In a possible implementation manner, the calculation formula for the current similarity between the battery packs is:

[0109] wherein, I i (f) represents the frequency-domain current signal of battery pack i , I j (f) represents the frequency-domain current signal of battery pack j , I i (t) represents the frequency-domain current signal of battery pack i at time point t , i represents battery pack i , j represents battery pack j , S current (i, j) represents the current similarity between battery packs.

[0110] In a possible implementation manner, the calculation formula for the voltage similarity between the battery packs is:

[0111] wherein, and are respectively the average values of the voltages of battery pack i and battery pack j , V i (t) is the voltage of battery pack i at time point t , V j (t) is the voltage of battery pack j at time point t , Tis the length of the time series, represents the battery pack i and the battery pack j the voltage similarity between.

[0112] In a possible implementation, the prediction module 360 is configured to: Input the temperature adjacency graph, current adjacency graph, and voltage adjacency graph into the preset health state prediction model, and process the temperature adjacency graph, current adjacency graph, and voltage adjacency graph through the preset health state prediction model to obtain the graph convolution processing results of the temperature adjacency graph, current adjacency graph, and voltage adjacency graph; Process the graph convolution processing results based on a time series model to obtain the long-term and short-term dependencies and long-term time series patterns of the battery capacity change of the battery pack; wherein, the time series model includes a long short-term memory network and a Transformer; Predict the health state of each battery pack based on the long-term dependencies and long-term time series patterns of the battery capacity change of the battery pack.

[0113] The specific implementation manners and technical effects of the device provided by the embodiments of the present disclosure are similar to those of the above method embodiments, and will not be described in detail herein.

[0114] According to the embodiments of the present disclosure, the present disclosure also discloses an electronic device, a computer-readable storage medium, and a computer program product.

[0115] Figure 4 FIG. shows a schematic block diagram of an electronic device 400 that can be used to implement the embodiments of the present disclosure. The electronic device 400 is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0116] As Figure 4As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to computer programs stored in a read-only memory (ROM) 402 or computer programs loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0117] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disc, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0118] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the battery pack health state prediction method. For example, in some embodiments, the battery pack health state prediction method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the battery pack health state prediction method described above can be executed. Alternatively, in other embodiments, the computing unit 401 can be configured to execute the battery pack health state prediction method in any other appropriate way (e.g., by means of firmware).

[0119] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0120] The program code of a computer program product for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0121] In the context of the present disclosure, a computer-readable storage medium can be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a computer-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0122] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input received from the user can be in any form (including acoustic input, speech input, or tactile input).

[0123] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), the Internet, and blockchain network.

[0124] A computer system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server can also be a server of a distributed system, or a server combined with blockchain.

[0125] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this disclosure can be achieved, and no limitation is imposed herein.

[0126] The above specific embodiments do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for predicting the health status of a battery pack, characterized in that: include: Acquire battery pack operation data of at least two battery packs within a preset time period; wherein the battery pack operation data includes battery pack temperature data, battery pack current data, and battery pack voltage data, and the battery pack operation data is represented in the form of a time series; calculating a temperature difference of the battery pack at each time point based on the battery pack temperature data, and calculating a temperature similarity between the battery packs based on the temperature difference at each time point; Calculating current similarity between the battery groups based on the frequency domain representation of the battery group current data; Calculating a covariance matrix of voltage variation based on the battery pack voltage data, and calculating voltage similarity between the battery packs based on the covariance matrix; Based on the temperature similarity, current similarity, and voltage similarity between the battery groups, construct a temperature adjacency graph, a current adjacency graph, and a voltage adjacency graph; The health status of each battery pack is predicted by a preset health status prediction model based on the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph; wherein the preset health status prediction model is constructed based on a graph convolutional network.

2. The method for predicting the health status of a battery pack according to claim 1, characterized in that: The calculating the current similarity between the battery packs based on the frequency domain representation of the battery pack current data comprises: Performing Fourier transformation on the battery pack current data of each of the battery packs to obtain a frequency domain representation of the battery pack current data; Calculating current similarity between the battery groups based on the frequency domain representation of the current data of each of the battery groups; The step of calculating the covariance matrix of the voltage change based on the battery pack voltage data includes: Based on the battery pack voltage data of each battery pack at each time point, a covariance matrix of voltage variation is calculated.

3. The method for predicting the health status of a battery pack according to claim 2, characterized in that: The battery pack includes a battery pack i and battery pack j In the case ， The calculation formula for the temperature similarity between the battery packs is: in, α Weighting factor for temperature difference, n is an exponential parameter that regulates the effect of temperature changes on adjacency. △T ij (t ) is the battery pack at each time point t The temperature difference, T i (t), T j (t) Respectively represent battery pack i and battery pack j At the point in time t The temperature, T is the length of the time series, S temp (i, j) is the temperature similarity between battery packs, i Indicates battery pack i , j Indicates battery pack j .

4. The method for predicting the health status of a battery pack according to claim 3, characterized in that: The calculation formula for the current similarity between the battery packs is: in, I i (f) Indicates battery pack i The frequency domain current signal, I j (f) Indicates battery pack j The frequency domain current signal, I i (t) Indicates battery pack i At the point in time t The frequency domain current signal, i Indicates battery pack i , j Indicates battery pack j , S current (i,j) Indicates the current similarity between battery packs.

5. The method for predicting the health status of a battery pack according to claim 3, characterized in that: The calculation formula for the voltage similarity between the battery packs is: in, and Battery pack i and battery pack j The mean value of the voltage, V i (t) It's a battery pack i At the point in time t The voltage, V j (t) It's a battery pack j At the point in time t The voltage, T is the length of the time series, Indicates battery pack i and battery pack j The voltage similarity between them.

6. The method for predicting the health status of a battery pack according to claim 1, characterized in that: The method of predicting the battery pack health status of each battery pack based on the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph by using a preset health status prediction model includes: Inputting the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph into the preset health status prediction model, and processing the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph by the preset health status prediction model to obtain graph convolution processing results of the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph; Processing the graph convolution processing result based on a timing model to obtain the long-term and short-term dependencies and long-term timing patterns of the battery capacity changes of the battery pack; wherein the timing model includes a long short-term memory network and a Transformer; A battery pack health state is predicted for each of the battery packs based on long-term dependencies and long-term temporal patterns of battery capacity variations of the battery packs.

7. A battery pack health status prediction device, characterized in that: include: A data acquisition module, used to acquire battery pack operation data of at least two battery packs within a preset time period; wherein the battery pack operation data includes battery pack temperature data, battery pack current data, and battery pack voltage data, and the battery pack operation data is represented in the form of a time series; a temperature similarity calculation module, configured to calculate a temperature difference of the battery pack at each time point based on the battery pack temperature data, and calculate a temperature similarity between the battery packs based on the temperature difference at each time point; A current similarity calculation module, used for calculating the current similarity between the battery packs based on the frequency domain representation of the battery pack current data; A voltage similarity calculation module, used to calculate a covariance matrix of voltage variation based on the battery pack voltage data, and to calculate voltage similarity between the battery packs based on the covariance matrix; A graph construction module, used for constructing a temperature adjacency graph, a current adjacency graph, and a voltage adjacency graph based on the temperature similarity, current similarity, and voltage similarity between the battery packs; A prediction module is used to predict the battery pack health status of each battery pack based on the temperature adjacency graph, the current adjacency graph, and the voltage adjacency graph through a preset health status prediction model; wherein the preset health status prediction model is constructed based on a graph convolutional network.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the battery pack health status prediction method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the health status of a battery pack according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method for predicting the health status of a battery pack according to any one of claims 1 to 6 is implemented.

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