A method and system for predicting the health status and remaining life of a lithium battery

Abstract

The present invention discloses a method and system for predicting the health status and remaining life of a lithium battery. The method comprises: pre-processing the original charging data, eliminating noise and outliers, and obtaining normalized data. The normalized data is structured by differential means to generate structured data. A convolutional trunk module, a channel sparse attention residual module, a spatial sparse attention residual module, and a global attention residual module are combined to extract a global time series feature map from the structured data. These feature maps are fused with the historical data of the health status of the lithium battery to form a fused feature map, and predictions are made based on this to obtain the health status and remaining life of the lithium battery. By using an optimized differential method, the amount of information in the data can be enriched, so that the model can keenly capture subtle state changes between consecutive cycle periods. At the same time, the attention residual module can effectively extract potential time series features in the charging data, thereby improving the accuracy of the prediction.

Description

Technical Field

[0001] The present invention belongs to the field of lithium-ion battery state prediction, and more specifically, relates to a method and system for predicting the health state and remaining life of a lithium battery. Background Art

[0002] Lithium-ion batteries offer advantages such as high energy density, long life, no memory effect, and low self-discharge, making them widely used in consumer electronics, electric vehicles, and energy storage systems. However, over time, lithium-ion battery performance gradually degrades, manifesting as a decrease in capacity and a shortened remaining useful life. At the end of their lifespan, lithium-ion batteries can cause device failures and increase safety risks. Therefore, accurately predicting the aging level of lithium-ion batteries is crucial for battery management systems.

[0003] Because deep learning methods are better at handling complex nonlinear scenarios under multiple charging strategies, they have become a promising approach for battery state prediction. However, deep learning methods still have limitations in data preprocessing and model building, making it difficult for the model to fully extract the inherent time series characteristics of charging data. On the other hand, the differences between battery charge and discharge data cycles are very small, and discharge data is affected by different operating conditions, which leads to distribution differences between the data and the model. Existing differential methods for processing battery data differ from the battery data of the current cycle to the battery data of the first cycle. This means that their predictive ability depends largely on the measurement accuracy of the first cycle, and after the difference, the first cycle is all zeros, resulting in information loss.

[0004] The prior art invention patent with publication number CN116522140A proposes a multi-task deep learning model and a method for predicting and controlling the health status and remaining life of a lithium battery, including a bottom shared layer, a top task layer, and a gating network; the bottom shared layer includes multiple feature extraction models, each of which is used to extract features from the input data; the top task layer has a specific task model corresponding to each learning task, and the gating network is set up in a one-to-one correspondence with the specific task model. The gating network uses the input data as input features and outputs weight parameters processed by the Softmax function; the features extracted by all the feature extraction models are weighted by the weight coefficients and assembled to obtain features as the input of the corresponding specific task model to perform a specific learning task. This solution mainly relies on gated recurrent units or long short-term memory networks for feature extraction. The model is relatively simple and it is difficult to fully extract the inherent temporal features in the charging data. Summary of the Invention

[0005] The present invention aims to overcome the defects in the existing methods for predicting the health status and remaining life of lithium batteries, especially the information loss problem caused by differentiating the battery data of the current cycle with the battery data of the first cycle, and the shortcomings of deep learning models in capturing the potential timing characteristics in charging data. A method and system for predicting the health status and remaining life of lithium batteries are provided.

[0006] The primary purpose of the present invention is to solve the above technical problems, and the technical solutions of the present invention are as follows:

[0007] A first aspect of the present invention provides a method for predicting the health status and remaining life of a lithium battery, comprising the following steps:

[0008] The raw charging data is preprocessed to remove noise and outliers to obtain normalized data;

[0009] Using a preset differential method to perform structural processing on the normalized data to obtain structured data;

[0010] Inputting the structured data into a preset multi-strategy attention regression network for feature extraction to obtain a first task global temporal feature graph and a second task global temporal feature graph, where the first task and the second task are different prediction tasks;

[0011] The first task global time series feature graph and the second task global time series feature graph are respectively combined with the lithium battery health status historical data and input into a preset predictor to output the lithium battery health status prediction result and the remaining life prediction result.

[0012] Furthermore, the pre-processing comprises the following steps:

[0013] Replace outliers with new values ​​generated by linear interpolation;

[0014] Delete data that disrupts the monotonic decrease in charging capacity;

[0015] Convert the data from the time dimension to the battery state of charge dimension and use a filter to smooth the data;

[0016] Normalize the smoothed data.

[0017] Furthermore, the structured processing includes the following steps:

[0018] The normalized data of the first t cycles and the normalized data of the most recent L cycles are used as input. For each cycle, except the first cycle, the data of the cycle is differentially calculated with the data of the previous cycle to obtain the differential data.

[0019] The differential data is used to form a multi-dimensional data vector and obtain structured data.

[0020] Furthermore, the preset multi-strategy attention regression network includes a convolutional trunk module, a residual module with channel sparse attention, a residual module with spatial sparse attention, and a residual module with global attention; the feature extraction includes the following steps:

[0021] Input structured data into the convolutional trunk module and output preliminary feature maps;

[0022] Input the preliminary feature map into the residual module with channel sparse attention and output the channel temporal feature map;

[0023] Input the channel temporal feature map into the residual module with spatial sparse attention and output the spatial temporal feature map;

[0024] The spatial temporal feature map is input into the residual module with global attention and the global temporal feature map is output.

[0025] Furthermore, the preset predictor includes a preset neural network and three multi-layer perceptrons; the method for predicting the health status prediction result and remaining life of the lithium battery includes the following steps:

[0026] Input the lithium battery health status historical data into the first multi-layer perceptron to adjust the channel size of the data to match the global time series feature map;

[0027] Perform channel splicing on the lithium battery health status historical data after adjusting the channel size and the global time series feature map of the first task or the global time series feature map of the second task to obtain a fusion feature map;

[0028] The fused feature map is passed through the neural network, the second multi-layer perceptron, and the third multi-layer perceptron in sequence to output the lithium battery health status prediction result or the remaining life prediction result.

[0029] A second aspect of the present invention provides a lithium battery health status and remaining life prediction system for implementing the steps of a lithium battery health status and remaining life prediction method, the system comprising:

[0030] A preprocessing module is used to preprocess the raw charging data to remove noise and outliers to obtain normalized data;

[0031] A structuring module is used to perform structural processing on the smoothed data using a preset difference method to obtain structured data;

[0032] The multi-strategy attention regression network module is used to extract features from structured data and obtain the global temporal feature map of the first task and the global temporal feature map of the second task;

[0033] The prediction module is used to input the first task global timing feature diagram and the second task global timing feature diagram into a preset predictor in combination with the lithium battery health status historical data, and output the lithium battery health status prediction result and the remaining life prediction result.

[0034] Furthermore, the raw charging data is preprocessed using a preprocessing module, including the following steps:

[0035] Replace outliers with new values ​​generated by linear interpolation;

[0036] Delete data that disrupts the monotonic decrease in charging capacity;

[0037] Convert the data from the time dimension to the battery state of charge dimension and use a filter to smooth the data;

[0038] Normalize the smoothed data.

[0039] Furthermore, the normalized data is structured using a structuring module, including the following steps:

[0040] Take the normalized data of the first t cycles and the normalized data of the most recent L cycles as input. For each cycle, except the first cycle, perform a differential calculation between the data of the cycle and the data of the previous cycle to obtain the differential data.

[0041] The differential data is used to form a multi-dimensional data vector and obtain structured data.

[0042] Furthermore, the multi-strategy attention regression network module includes a convolutional trunk module, a residual module with channel sparse attention, a residual module with spatial sparse attention, and a residual module with global attention; the method for extracting a global temporal feature map using the preset multi-strategy attention regression network module includes the following steps:

[0043] Input structured data into the convolutional trunk module and output preliminary feature maps;

[0044] Input the preliminary feature map into the residual module with channel sparse attention and output the channel temporal feature map;

[0045] Input the channel temporal feature map into the residual module with spatial sparse attention and output the spatial temporal feature map;

[0046] The spatial temporal feature map is input into the residual module with global attention and the global temporal feature map is output.

[0047] Furthermore, the prediction module includes a preset neural network and three multi-layer perceptrons; the method for predicting the health status prediction result and remaining life of the lithium battery using the prediction module includes the following steps:

[0048] Input the lithium battery health status historical data into the first multi-layer perceptron to adjust the channel size of the data to match the global time series feature map;

[0049] Perform channel splicing on the lithium battery health status historical data after adjusting the channel size and the global time series feature map of the first task or the global time series feature map of the second task to obtain a fusion feature map;

[0050] The fused feature map is passed through the preset neural network, the second multi-layer perceptron, and the third multi-layer perceptron in sequence to output the lithium battery health status prediction result or the remaining life prediction result.

[0051] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0052] The differential method proposed in this paper differentiates the battery data of the current cycle from the battery data of the previous cycle while retaining the values ​​of the first cycle. This processed structured data is more informative and enables the model to capture subtle state changes between two consecutive cycles, addressing the information loss problem of existing differential methods. Furthermore, a multi-strategy attention regression network is used to fully capture the potential temporal characteristics of charging data, effectively fusing shallow and deep features to ensure that shallow features are retained when propagating to deeper layers of the network, improving prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to make the purpose and technical solution of the present invention clearer, the present invention provides the following drawings and descriptions:

[0054] Figure 1 Provide a method flow chart for an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of differential structured data provided by an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of the structure of the method provided in an embodiment of the present invention;

[0057] Figure 4 Network structure diagram of three attention strategies provided by embodiments of the present invention;

[0058] Figure 5 Schematic diagram of the preprocessing module and differential structuring module provided in an embodiment of the present invention;

[0059] Figure 6 Schematic diagram of the multi-strategy attention regression network module and prediction module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0062] Example 1:

[0063] The present invention provides a method for predicting the health status and remaining life of a lithium battery. Figure 1 The figure shows a flow chart for a lithium battery state-of-health and remaining life prediction method. This method uses only charging data to jointly predict the state of health (SOH) and remaining life (RUL), including terminal voltage, current, and temperature (VIT). These three types of data are widely used in various battery-powered devices and are easy to obtain.

[0064] The specific steps are as follows:

[0065] S1: Preprocess the raw charging data to remove noise and outliers to obtain normalized data.

[0066] The specific process is:

[0067] Since raw charging data often contains noise, the following data cleaning was performed: First, outliers with values ​​greater than 15 times the standard deviation were replaced with new values ​​generated by linear interpolation.

[0068] Delete the data that destroys the monotonic decrease of charging capacity to ensure that the charging capacity decreases monotonically.

[0069] The state of charge (SOC) of a battery ranges from 0% to 100% in any cycle. Therefore, a standardized reference is needed to change the variable describing the VIT curve from time to SOC:

[0070] SOC i =C i / C current ×100% (1)

[0071] Among them, C i Indicates the charging capacity of the i-th sampling point in the current cycle, C current Indicates the discharge capacity of the battery at the end of the current cycle. In addition, the SOC length is resampled to a commonly used length of 224 using linear interpolation.

[0072] The data were smoothed using a Savitzky-Golay (SG) filter with a window length of 5 and an order of 2.

[0073] Since the units and scales of the smoothed data are different, these data are normalized to ensure that the model converges easily:

[0074] X normal =(X raw -X min ) / (X max -X min ) (2)

[0075] where X normal is the normalized data, X raw is the smoothed data, X min and X max are the minimum and maximum values ​​of all smoothed data respectively.

[0076] In order to adapt to deep learning models, data structuring is widely used to convert VIT curves into structured images. This paper proposes a new differential method to structure the normalized data, where the input data structure consists of the normalized data of the first 5 cycles and the normalized data of the last L cycles. Except for the data of the first cycle, the data of all other cycles are differentiated from the data of the previous cycle, such as Figure 2 As shown. Among them, is a vector containing VIT data, where i and j represent the cycle number and sampling point respectively.

[0077] S2: Use a preset difference method to perform structured processing on the normalized data to obtain structured data.

[0078] The specific process is:

[0079] The normalized data of the first five cycles and the normalized data of the most recent L cycles are used as input. For each cycle, except the first cycle, the data of the cycle is differentially calculated with the data of the previous cycle to obtain the differential data.

[0080] Using the differential data, we can form a multi-dimensional data vector and obtain structured data, such as Figure 2 As shown in the figure, the structured data processed in this way has richer information and can enable the model to capture the slight state changes between two consecutive cycles, which will be more beneficial for the joint prediction of the health status and remaining life of the lithium battery.

[0081] The method first extracts channel and spatial features from I. Then, it extracts global temporal features for a specific task in each branch of the task-specific feature extraction stage. Finally, the extracted task-specific global temporal features of each branch go through the prediction stage to achieve specific predictions. The method structure is as follows Figure 3 shown.

[0082] S3: Input the structured data into a preset multi-strategy attention regression network for feature extraction to obtain a first task global temporal feature map and a second task global temporal feature map, where the first task and the second task are different prediction tasks.

[0083] The specific process is:

[0084] Input the structured data into the convolution trunk module and output the preliminary feature map. Given the structured data after differential processing Where H is set to the resampling length 224 and W is the total number of cycles. The structured cyclic data I first passes through the convolution backbone to obtain the feature map The expression is as follows:

[0085] F1=BN(Conv2d(ReLU(BN(Conv2d(I))))) (3)

[0086] where BN(·), Conv2d(·), and ReLU(·) denote batch normalization, 3×3 convolution operation, and the rectified linear unit (ReLU) activation function, respectively.

[0087] The preliminary feature map is input into the residual module with channel sparse attention, and the channel temporal feature map is output. The feature map F1 passes through the residual module with CSA, which consists of a convolution residual block and a CSA+downsampling block to obtain the channel temporal feature map. The expression is as follows:

[0088] F2=D1+C1 (4)

[0089] D1=MP(BN(Conv2d(CSA(F1)))) (5)

[0090] C1=MP(Conv2d(F1)) (6)

[0091] where D1 and C1 are the outputs of the CSA+downsampling block and the convolutional residual block, respectively. MP(·) denotes a 2×1 max pooling operation. CSA(·) is the output of the designed CSA.

[0092] The channel temporal feature map is input into the residual module with spatial sparse attention, and the spatial temporal feature map is output. The channel temporal feature map F2 is obtained by the residual module with SSA to obtain the spatial temporal feature map It has similar operations as those in the above expression, except that SSA is used instead of CSA.

[0093] The spatial temporal feature map is input into the residual module with global attention, and the global temporal feature map is output. The feature map F3 passes through two branches in parallel to obtain the global temporal feature map of the specific task. and They also have similar operations as in the above expressions, except that GA is used instead of CSA and the convolution kernel is set to 5×3.

[0094] The three attention strategies mentioned above, channel sparse attention, spatial sparse attention, and global attention have similar structures, but multi-head self-attention (MSA) is different, such as Figure 4 As shown in Figure 1, they all consist of a deep convolutional layer (DWConv), a layer normalization (LN), two or one MSAs with the same structure, an LN, and a feedforward network (FFN) consisting of two linear layers. To prevent performance loss, there is a residual connection between the input of the first LN and the input of the second LN.

[0095] The GA functions similarly to traditional MSA by merging the H and W dimensions into one dimension, and then re-splitting the H and W dimensions after MSA, as shown below:

[0096] MSA(Q,K,V)=Concat(head1,…,head h )W o (7)

[0097] head i =Attention(Q i ,K i ,V i ) (8)

[0098]

[0099] Q=W Q X, K=W K X, V=W V X (10)

[0100] where MSA(·) and Attention(·) denote the multi-head self-attention operation and self-attention operation, respectively. Softmax(·) is the softmax function. T denotes transpose. W, X, h, and d denote the number of trainable weights, input data, and attention heads, and the channel dimension of the input data, respectively.

[0101] If GA is used in the early stages of the network, the large number of tokens attempting to model global information will make self-attention computationally expensive. Therefore, to address this issue, we exploit the sparsification of self-attention and design two new types of self-attention that interact with the input data W and H from different perspectives. Specifically, CSA places the channel C in the last dimension, and the expression is as follows:

[0102]

[0103] Among them, MSA CSA (·) indicates the MSA operation of CSA.

[0104] SSA places channel C in the penultimate dimension, and the expression is as follows:

[0105]

[0106] Among them, MSA SSA (·) represents the MSA operation of SSA.

[0107] The three attention strategies extract temporal features from different domains. The convolutional residual block in the module effectively fuses shallow and deep features, ensuring that shallow features are preserved as they propagate through deeper layers of the network. Furthermore, the optimal execution order of the three attention strategies was designed. Because the battery's voltage, current, and temperature data are highly correlated, performing channel attention first can reveal this correlation across the three channels of the input structured data, while performing the GA last can avoid significant computational overhead.

[0108] S4: The first task global time series feature graph and the second task global time series feature graph are respectively combined with the lithium battery health status historical data and input into a preset predictor, and the lithium battery health status prediction result and the remaining life prediction result are output.

[0109] The specific process is:

[0110] The multi-layer perceptron (MLP) is used to adjust the channel size of the lithium battery health status historical data to match the global time series feature map. As a key health indicator, the lithium battery health status historical data It is crucial for prediction tasks. Due to the global temporal feature map of a specific task and The channel size is different from the lithium battery health status historical data, so the lithium battery health status historical data X SOH Channel expansion is required through MLP, increasing its channel size from 1 to 4C.

[0111] The lithium battery health status history data after adjusting the channel size is spliced ​​with the global time series feature map of the specific task, and the global time series feature map of the specific task is spliced ​​with the lithium battery health status history data after channel expansion to realize the fusion feature maps F6 and F7, as shown in Figure 3 shown.

[0112] The fused feature map is processed by a bidirectional long short-term memory network (BiLSTM) and two multi-layer perceptrons to output the health status and remaining life prediction results of the lithium battery. The fused feature map is then passed through the BiLSTM and two MLPs to achieve the final prediction. These process expressions are as follows:

[0113]

[0114] F6=Concat[MLP(X SOH ),Flatten(F4)] (18)

[0115] in, Represents the predicted state of health (SOH) of a lithium battery. BiLSTM(·), Concat(·), and Flatten(·) represent BiLSTM, channel concatenation, and flattening operations, respectively. BiLSTM processes a one-dimensional sequence from two directions, discards irrelevant features through its internal memory mechanism, and outputs a sequence at its endpoints. Predicted remaining life prediction results It is implemented similarly according to the above expression.

[0116] Example 2:

[0117] This embodiment provides a lithium battery health status and remaining life prediction system, which is used to implement the lithium battery health status and remaining life prediction method described in Example 1, including:

[0118] A preprocessing module is used to preprocess the raw charging data to remove noise and outliers to obtain normalized data;

[0119] A structuring module is used to perform structural processing on the smoothed data using a preset difference method to obtain structured data;

[0120] The multi-strategy attention regression network module is used to extract features from structured data and obtain the global temporal feature map of the first task and the global temporal feature map of the second task;

[0121] The prediction module is used to input the first task global timing feature diagram and the second task global timing feature diagram into a preset predictor in combination with the lithium battery health status historical data, and output the lithium battery health status prediction result and the remaining life prediction result.

[0122] More specifically, the raw charging data is preprocessed using a preprocessing module, such as Figure 5 As shown, the following steps are included:

[0123] Replace outliers with new values ​​generated by linear interpolation;

[0124] Delete data that disrupts the monotonic decrease in charging capacity;

[0125] Convert the data from the time dimension to the battery state of charge dimension and use a filter to smooth the data;

[0126] Normalize the smoothed data.

[0127] More specifically, the structured module is used to perform structured processing on the normalized data, such as Figure 5 As shown, specifically including:

[0128] Take the normalized data of the first t cycles and the normalized data of the most recent L cycles as input. For each cycle, except the first cycle, perform a differential calculation between the data of the cycle and the data of the previous cycle to obtain the differential data.

[0129] The differential data is used to form a multi-dimensional data vector and obtain structured data.

[0130] More specifically, the multi-strategy attention regression network module includes a convolutional trunk module, a residual module with channel sparse attention, a residual module with spatial sparse attention, and a residual module with global attention, such as Figure 6 As shown in FIG, a method for extracting a global temporal feature map using a preset multi-strategy attention regression network module specifically includes:

[0131] Input structured data into the convolutional trunk module and output preliminary feature maps;

[0132] Input the preliminary feature map into the residual module with channel sparse attention and output the channel temporal feature map;

[0133] Input the channel temporal feature map into the residual module with spatial sparse attention and output the spatial temporal feature map;

[0134] The spatial temporal feature map is input into the residual module with global attention and the global temporal feature map is output.

[0135] More specifically, the prediction module includes a preset neural network and three multi-layer perceptrons, such as Figure 6 As shown; a method for predicting the health status prediction result and remaining life of a lithium battery using a prediction module includes the following steps:

[0136] Input the lithium battery health status historical data into the first multi-layer perceptron to adjust the channel size of the data to match the global time series feature map;

[0137] Perform channel splicing on the lithium battery health status history data after adjusting the channel size and the global time series feature map of the first task or the global time series feature map of the second task to obtain a fusion feature map;

[0138] The fused feature map is passed through the preset neural network, the second multi-layer perceptron, and the third multi-layer perceptron in sequence to output the lithium battery health status prediction result or the remaining life prediction result.

[0139] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the health status and remaining life of a lithium battery, characterized in that: The steps include: The raw charging data is preprocessed to remove noise and outliers to obtain normalized data; The normalized data of the first t cycles and the normalized data of the most recent L cycles are used as input. For each cycle, except the first cycle, the data of the cycle is differentially calculated with the data of the previous cycle to obtain the differential data. The differential data is used to form a multidimensional data vector to obtain structured data. Inputting the structured data into a preset multi-strategy attention regression network for feature extraction to obtain a first task global temporal feature graph and a second task global temporal feature graph, where the first task and the second task are different prediction tasks; The first task global time series feature graph and the second task global time series feature graph are respectively combined with the lithium battery health status historical data and input into a preset predictor to output the lithium battery health status prediction result and the remaining life prediction result.

2. A method for predicting the health status and remaining life of a lithium battery according to claim 1, characterized in that: The pretreatment comprises the following steps: Replace outliers with new values ​​generated by linear interpolation; Delete data that disrupts the monotonic decrease in charging capacity; Convert the data from the time dimension to the battery state of charge dimension and use a filter to smooth the data; Normalize the smoothed data.

3. A method for predicting the health status and remaining life of a lithium battery according to claim 1, characterized in that: The preset multi-strategy attention regression network includes a convolutional trunk module, a residual module with channel sparse attention, a residual module with spatial sparse attention, and a residual module with global attention; The feature extraction comprises the following steps: Input structured data into the convolutional trunk module and output preliminary feature maps; Input the preliminary feature map into the residual module with channel sparse attention and output the channel temporal feature map; Input the channel temporal feature map into the residual module with spatial sparse attention and output the spatial temporal feature map; The spatial temporal feature map is input into the residual module with global attention and the global temporal feature map is output.

4. A method for predicting the health status and remaining life of a lithium battery according to claim 1, characterized in that: The preset predictor includes a preset neural network and three multi-layer perceptrons; the method for predicting the health status prediction result and remaining life of a lithium battery using the preset predictor includes the following steps: Input the lithium battery health status historical data into the first multi-layer perceptron to adjust the channel size of the data to match the global time series feature map; Perform channel splicing on the lithium battery health status historical data after adjusting the channel size and the global time series feature map of the first task or the global time series feature map of the second task to obtain a fusion feature map; The fused feature map is passed through the neural network, the second multi-layer perceptron, and the third multi-layer perceptron in sequence to output the lithium battery health status prediction result or the remaining life prediction result.

5. A lithium battery health status and remaining life prediction system, used to implement a lithium battery health status and remaining life prediction method according to any one of claims 1 to 4, characterized in that: include: A preprocessing module is used to preprocess the raw charging data to remove noise and outliers to obtain normalized data; The structuring module is used to take the normalized data of the first t cycles and the normalized data of the most recent L cycles as input, and for each cycle, except the first cycle, perform a differential calculation between the data of the cycle and the data of the previous cycle to obtain differential data; the differential data is used to form a multidimensional data vector to obtain structured data; The multi-strategy attention regression network module is used to extract features from structured data and obtain the global temporal feature map of the first task and the global temporal feature map of the second task; The prediction module is used to input the first task global timing feature diagram and the second task global timing feature diagram into a preset predictor in combination with the lithium battery health status historical data, and output the lithium battery health status prediction result and the remaining life prediction result.

6. A lithium battery health status and remaining life prediction system according to claim 5, characterized in that: The raw charging data is preprocessed using the preprocessing module, including the following steps: Replace outliers with new values ​​generated by linear interpolation; Delete data that disrupts the monotonic decrease in charging capacity; Convert the data from the time dimension to the battery state of charge dimension and use a filter to smooth the data; Normalize the smoothed data.

7. A lithium battery health status and remaining life prediction system according to claim 5, characterized in that: The multi-strategy attention regression network module includes a convolutional trunk module, a residual module with channel sparse attention, a residual module with spatial sparse attention, and a residual module with global attention; The method for extracting a global temporal feature map using a preset multi-strategy attention regression network module includes the following steps: Input structured data into the convolutional trunk module and output preliminary feature maps; Input the preliminary feature map into the residual module with channel sparse attention and output the channel temporal feature map; Input the channel temporal feature map into the residual module with spatial sparse attention and output the spatial temporal feature map; The spatial temporal feature map is input into the residual module with global attention and the global temporal feature map is output.

8. A lithium battery health status and remaining life prediction system according to claim 5, characterized in that: The prediction module includes a preset neural network and three multi-layer perceptrons; the method for predicting the health status prediction result and remaining life of the lithium battery using the prediction module includes the following steps: Input the lithium battery health status historical data into the first multi-layer perceptron to adjust the channel size of the data to match the global time series feature map; Perform channel splicing on the lithium battery health status history data after adjusting the channel size and the global time series feature map of the first task or the global time series feature map of the second task to obtain a fusion feature map; The fused feature map is passed through the preset neural network, the second multi-layer perceptron, and the third multi-layer perceptron in sequence to output the lithium battery health status prediction result or the remaining life prediction result.

Images