Lithium battery charge state and service life combined prediction method based on deep learning

Abstract

The invention discloses a lithium battery charge state and service life combined prediction method based on deep learning, and belongs to the technical field of lithium batteries, the lithium battery is subjected to full life cycle charge and discharge cycle life experiment under specific working conditions, a deep learning model Autoformer is trained through experimental data, and the lithium battery charge state and service life combined prediction method based on deep learning is obtained. The method comprises the following steps: acquiring a combined prediction model of the state of charge and the residual service life of a lithium battery, inputting historical data into the combined prediction model, firstly acquiring a predicted SOC and a predicted voltage, and then performing autocorrelation calculation on a predicted voltage curve and a voltage curve of the first cycle of the lithium battery to obtain an autocorrelation coefficient; and inputting the self-correlation coefficient into a residual service life sub-prediction model to obtain a lithium battery capacity prediction value. According to the method, the excellent learning capability of Autoformer on super-long sequence data is utilized, the charge and discharge and capacity attenuation characteristics of the lithium battery are extracted from the full life cycle data, and an accurate prediction means is provided for the joint state estimation of the lithium battery.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium batteries, and specifically relates to a method for jointly predicting the state of charge and life of a lithium battery based on deep learning. Background Art

[0002] With the rapid development of new energy vehicles and energy storage systems, the prediction of the state of health (SOH) and remaining useful life (RUL) of lithium batteries has become a core challenge for battery management systems. In the prior art, the prediction of the state of charge (SOC) and SOH / RUL usually adopts a separate modeling method, including:

[0003] (1) SOC prediction based on an electrochemical model: estimating the SOC by combining an equivalent circuit model (such as the Thevenin model) with a Kalman filter algorithm, but without considering the parameter drift caused by battery aging (such as an increase in internal resistance and intensified polarization), and the error accumulates significantly after long-term use;

[0004] (2) Data-driven RUL prediction: The mainstream method uses LSTM or CNN to extract capacity decay features, but is limited by the memory ability of the model for long-cycle degradation sequences (>1000 cycles), and it is difficult to capture the gradual-mutation coupling mode before the capacity "dive";

[0005] (3) Multi-stage decomposition method: Using EEMD (ensemble empirical mode decomposition) or wavelet transform to decompose the voltage / capacity sequence into a trend term and a fluctuation term, and then inputting them into a shallow neural network (such as Elman) for prediction respectively, but the decomposition process depends on artificial mode selection, and no dynamic association between SOC and capacity decay is established. Summary of the Invention

[0006] In order to solve the problems of the difficult joint prediction of the SOC and RUL of lithium batteries in the prior art and the insufficient extraction of long-cycle degradation features, the present invention provides a method for jointly predicting the state of charge and life of a lithium battery based on deep learning. A full-life cycle charge and discharge cycle life experiment under specific working conditions is carried out on the lithium battery, and the deep learning model Autoformer is trained through experimental data to obtain a joint prediction model for the state of charge and remaining useful life of the lithium battery. The historical data is input into the joint prediction model to first obtain the predicted SOC and predicted voltage, and then the autocorrelation calculation is performed between the predicted voltage curve and the voltage curve of the first cycle of the lithium battery to obtain the autocorrelation coefficient. The autocorrelation coefficient is input into the remaining useful life prediction model to obtain the predicted value of the lithium battery capacity. The present invention utilizes the excellent learning ability of Autoformer for ultra-long sequence data to extract the charge and discharge and capacity decay features of the lithium battery from the full-life cycle data, providing an accurate prediction means for the joint state estimation of the lithium battery.

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] A joint prediction method for the state of charge and life of a lithium battery based on deep learning, comprising the following steps:

[0009] Step 1: Conduct a charge and discharge cycle life experiment on the lithium battery under specified working conditions to obtain experimental data, including current, voltage, temperature, state of charge, and remaining capacity of the lithium battery at different cycle numbers in the entire life cycle;

[0010] Step 2: Perform data standardization processing on the experimental data obtained in Step 1 to construct an input data set containing standardized parameters and derivative features;

[0011] Step 3: Based on the Autoformer-Transformer architecture, build a joint prediction model for the state of charge and remaining service life of the lithium battery; the joint prediction model includes:

[0012] A state-of-charge sub-prediction model, which is built with Autoformer as the architecture and is used to predict the state of charge at future times under real-time working conditions;

[0013] A voltage sub-prediction model, which is built with Autoformer as the architecture and is used to start when the state of charge is lower than a certain value and predict the voltage curve from this moment to when the state of charge is 0;

[0014] An autocorrelation coefficient calculation sub-module, which is used to calculate the autocorrelation coefficient between the predicted voltage curve output by the voltage sub-prediction model and the corresponding voltage curve of the first cycle, and input the calculated autocorrelation coefficient into the remaining service life sub-prediction model;

[0015] A remaining service life sub-prediction model, which is built with a CNN-Transformer hybrid architecture and is used to predict the capacity decay state in the next several cycles according to the autocorrelation coefficient calculated by the autocorrelation coefficient calculation sub-module;

[0016] Step 4: Input the processed feature data into the joint prediction model built in Step 3 for training and optimization to obtain an optimized joint prediction model;

[0017] Step 5: Input historical data into the optimized joint prediction model in Step 4 to obtain prediction results for the state of charge and remaining life.

[0018] Furthermore, the Step 2 includes:

[0019] S21. Normalize the current, voltage, state of charge, and remaining capacity data obtained from the experiment in Step 1 to obtain standardized parameters;

[0020] S22. Divide the time into six scales of year, month, day, hour, minute, and second according to the timestamp of the data, and use the five periodic features of month, day, hour, minute, and second as the time position encoding of the training data; construct an input data set containing normalization parameters and derivative features.

[0021] Further, the input of the state of charge sub-prediction model includes the current, voltage, current SOC, and cycle number features during the battery cycle process, and the output is the predicted value of the state of charge at a future moment.

[0022] Further, the construction principle of the state of charge sub-prediction model is as follows:

[0023] Utilize the ability of the Autoformer deep learning model to decouple periodic ultra-long time series data into trend terms and seasonal terms, and capture the changes in battery charge and discharge characteristics caused by battery aging and the periodic changes in SOC during the cycle.

[0024] The construction process of the state of charge sub-prediction model is as follows:

[0025] Record the first k historical data of current, voltage, cycle number, and SOC as and input it into the Autoformer encoder. Capture the period-based dependencies of the sequence through the autocorrelation mechanism, and aggregate similar subsequences from the underlying periods; concatenate the current, voltage, cycle number, and SOC data from the (k - l)th to the kth time and p times of 0 data as the input of the encoder, denoted as , where l is the amount of label data and p is the amount of predicted data; record the true SOC data from the kth to the (k + l)th time as as the label, and calculate the loss with the output of Autoformer to obtain the loss of this training and backpropagate to optimize the model parameters; obtain the state of charge sub-prediction model after multiple trainings.

[0026] Further, the input of the voltage sub-prediction model is the current, voltage, SOC, cycle number, and remaining capacity features of each cycle, and the output feature is the predicted value of the voltage at a future moment.

[0027] Further, the construction principle of the voltage sub-prediction model is as follows:

[0028] The Autoformer deep learning model decouples the ultra-long sequence data with periodic characteristics into trend components and seasonal components through the time-domain decomposition mechanism, and separately models the drift of charge and discharge characteristics induced by battery aging and the periodic pattern of voltage fluctuations during the cycle; through the joint representation of the two trends, multi-scale prediction of battery degradation and cycle dynamic evolution is realized.

[0029] Further, the calculation method of the autocorrelation coefficient is as follows:

[0030] Align the predicted voltage curve output by the voltage sub-prediction model with the voltage curve of the first cycle through SOC. Through normalizing the time axis and linear interpolation processing, the two voltage curves have the same length and are aligned in time. Then, splice the two voltage curves within the same SOC change value together, and set the lag k to half of the total length of the curve, that is , and the calculation formula for the autocorrelation coefficient Acf is as follows:

[0031] where , , is the i-th voltage value of the spliced voltage curve, is the i + k-th voltage value of the spliced voltage curve, and N is the total amount of spliced voltage data.

[0032] Further, the input features of the remaining useful life sub-prediction model include the number of cycles, the remaining capacity, and the voltage autocorrelation coefficient output by the autocorrelation coefficient calculation sub-module, and the output is the predicted value of the future capacity decay trajectory.

[0033] Further, the construction principle of the remaining useful life sub-prediction model is as follows:

[0034] The CNN module extracts short-term mutation features in capacity fluctuations through local convolution operations, and the Transformer module uses the global self-attention mechanism to model the long-range dependence relationship between the number of cycles, the autocorrelation coefficient, and capacity decay;

[0035] The construction process of the remaining useful life sub-prediction model is as follows:

[0036] Embed the number of cycles and the voltage autocorrelation coefficient of the historical k cycles into the encoder through CNN, and capture the global dependence relationship through the self-attention module; use the capacity offset by 1 cycle as the decoder input, and establish a non-linear relationship between the number of cycles, the voltage autocorrelation coefficient, and the cycle capacity after self-attention and cross-attention, and output the capacity prediction value.

[0037] Further, step five includes: inputting historical data into the optimized joint prediction model, first obtaining the predicted SOC and predicted voltage, then calculating the autocorrelation between the predicted voltage curve and the voltage curve of the first cycle of the lithium battery through the autocorrelation coefficient calculation sub-module to obtain the autocorrelation coefficient, and inputting the autocorrelation coefficient into the remaining useful life sub-prediction model to obtain the predicted value of the lithium battery capacity.

[0038] The present invention has the following beneficial effects:

[0039] The present invention utilizes the ability of Autoformer to capture the characteristics of ultra-long time-series data, extracts the charging and discharging characteristics and capacity attenuation characteristics of lithium batteries from the full-life cycle data, innovatively proposes using the voltage autocorrelation coefficient as the training feature, synchronously analyzes the coupling relationship between the charging and discharging characteristics and capacity attenuation through the ultra-long sequence modeling of Autoformer, realizes the joint prediction of SOC-RUL and early fault warning, and breaks through the technical bottlenecks of traditional methods in long-cycle modeling and dynamic coupling analysis. Description of the Drawings

[0040] Figure 1 It is a schematic diagram of the overall process of a method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to the present invention.

[0041] Figure 2 It is a comparison diagram of the normalized voltage autocorrelation coefficient and the battery capacity in the embodiment of the present invention.

[0042] Figure 3 It is a schematic diagram of the principle of a joint prediction model for the state of charge and remaining useful life of the lithium battery according to the present invention.

[0043] Figure 4 It is a schematic diagram of the state-of-charge sub-prediction model and voltage sub-prediction model of the lithium battery in the embodiment of the present invention.

[0044] Figure 5 It is a schematic diagram of the remaining useful life sub-prediction model of the lithium battery in the embodiment of the present invention.

[0045] Figure 6 It is a comparison diagram of single-step capacity prediction with and without autocorrelation coefficient in the input features in the embodiment of the present invention.

[0046] Figure 7 It is a comparison diagram of ten-step capacity prediction with and without autocorrelation coefficient in the input features in the embodiment of the present invention. Detailed Embodiments

[0047] The present invention will be further explained below in conjunction with the embodiments and the drawings.

[0048] In this embodiment, taking the data of graphite / lithium batteries as an example, a joint prediction model for the state of charge and remaining useful life of lithium batteries is built and trained, and the present invention is described comprehensively and in detail. This method is not limited to graphite / lithium batteries and is applicable to all commercial and self-made lithium batteries.

[0049] This embodiment is a method for jointly predicting the state of charge and life of a lithium battery based on deep learning. As Figure 1 shown, it mainly includes five steps:

[0050] Step 1: Conduct charge-discharge cycle life experiments on lithium batteries under specified working conditions to obtain data such as current, voltage, temperature, state of charge, and remaining capacity at different cycle numbers during the entire life cycle of the lithium battery, as shown in Table 1;

[0051] Table 1. Charge-discharge cycle life experiment data

[0052] month day hour minute s Number of cycles Charge and discharge state Current / A Voltage / V SOC Capacity / Ah 8 8 0 49 5 1 0 0 4.1962 1 1.4527 8 8 0 49 15 1 -1 -0.749 4.1115 1 1.4527 8 8 0 49 25 1 -1 -0.749 4.1031 0.998 1.4527 8 8 0 49 35 1 -1 -0.749 4.0962 0.997 1.4527 8 8 0 49 45 1 -1 -0.749 4.0905 0.995 1.4527

[0053] Step 2: Perform data standardization processing on the experimental data obtained in Step 1. Normalize the data such as current, voltage, state of charge, and remaining capacity, and construct an input data set containing standardized parameters and derived features;

[0054] Specifically, Step 2 includes:

[0055] S21. Normalize the data such as current, voltage, state of charge, and remaining service life obtained from the experiments in Step 1, so that data of different scales are scaled to the interval [0,1], facilitating the model to learn the features of data between different scales;

[0056] The data obtained from the charge-discharge cycle life experiments are generally current, voltage, temperature, cycle number, SOC, and battery capacity after each cycle with timestamps. For current, voltage, SOC, and remaining capacity, the data of different scales are unified through normalization calculation to avoid the situation that the model can only learn the change of a certain feature due to the too large change range of a certain feature. The normalization calculation formula is as follows:

[0057] In the formula, is the normalized data, is the input data, is the minimum value of the input data, is the maximum value in the input data.

[0058] S22. Divide the time into 6 scales according to year, month, day, hour, minute, and second according to the timestamps of the data. Use the 5 periodic features of month, day, hour, minute, and second as the time position encoding of the training data; construct an input data set containing standardized parameters and derived features.

[0059] During the implementation of this embodiment, in order to verify the non-linear relationship between the voltage autocorrelation coefficient and the capacity attenuation curve, the voltage curve of the first cycle experiment is used as the benchmark, and the voltage curve of each subsequent cycle is calculated for the autocorrelation coefficient with the voltage curve of the first cycle. An autocorrelation coefficient can be obtained for each cycle. After calculating and normalizing the autocorrelation coefficients of all cycles, it can be seen that there is a certain non-linear relationship between the autocorrelation coefficient and the capacity attenuation curve, as Figure 2 shown.

[0060] Step 3: Based on the Autoformer-Transformer architecture, build a joint prediction model for the state of charge (SOC) and remaining useful life (RUL) of a lithium battery. The joint prediction model synchronously integrates an SOC sub-prediction model, a voltage sub-prediction model, a remaining life sub-prediction model, and an autocorrelation coefficient calculation module. Since the historical m-cycle data and the historical k-moment data are time-series data of different time scales, the state parameters and the remaining useful life (RUL) are associated and modeled by sharing the time-series feature layer. As Figure 3 shown, the joint prediction model includes:

[0061] (1) The SOC sub-prediction model, that is, the SOC sub-prediction model, which is built with Autoformer as the architecture. As Figure 4 shown, the input of the SOC sub-prediction model includes features such as current, voltage, current SOC, and number of cycles during the battery cycle, and the output is the predicted value of the SOC at a future time. The SOC prediction model is used for second-level data prediction, that is, the prediction of the SOC at a future time under real-time working conditions.

[0062] The Autoformer deep learning model can decouple periodic ultra-long time-series data into trend terms and seasonal terms, and capture the changes in battery charge and discharge characteristics caused by battery aging and the periodic changes in SOC during cycling. The model architecture is as shown in the attached instructions Figure 3 shown. The model records the first k historical data of current, voltage, number of cycles, and SOC as and inputs it into the Autoformer encoder. Through the autocorrelation mechanism, the sequence-based periodic dependencies are captured, and similar subsequences are aggregated from the underlying cycles. The current, voltage, number of cycles, and SOC data from the (k - l)-th to the k-th time and p zeros are concatenated as the input of the encoder, denoted as , where l is the amount of labeled data and p is the amount of predicted data. The real SOC data from the k-th to the (k + l)-th time is denoted as as the label, and the loss is calculated with the output of Autoformer to obtain the loss of this training and backpropagate to optimize the model parameters. After sufficient training, the SOC sub-prediction model is obtained.

[0063] (2) The voltage sub-prediction model, which is built with Autoformer as the architecture. As Figure 4 shown, the input of the voltage prediction model is features such as current, voltage, SOC, number of cycles, and remaining capacity for each cycle, and the output feature is the predicted value of the voltage at a future time. The voltage prediction model is also used for second-level data prediction and is started when the SOC is lower than a certain value to predict the voltage curve from this moment to when the SOC is 0.

[0064] The Autoformer deep learning model decouples the ultra-long sequence data with periodic characteristics into a trend component and a seasonal component through a time-domain decomposition mechanism, and separately models the charge-discharge characteristic drift induced by battery aging and the periodic pattern of voltage fluctuations during cycling. This architecture realizes multi-scale prediction of battery degradation and cyclic dynamic evolution by jointly representing the two trends. In the data input process, except that the input features are changed to current, voltage, number of cycles, SOC, and remaining capacity, the rest is the same as described in the state of charge prediction model. Taking the capacity of the lithium battery as the prediction feature of voltage can increase the robustness of the model and improve the prediction accuracy of the model at different numbers of cycles.

[0065] (3) The autocorrelation coefficient calculation sub-module, which is used to calculate the autocorrelation coefficient between the predicted voltage curve output by the voltage sub-prediction model and the corresponding voltage curve of the first cycle, and input the calculated autocorrelation coefficient into the remaining useful life sub-prediction model for future RUL prediction. The calculation method of the autocorrelation coefficient Acf is as follows:

[0066] Align the predicted voltage curve output by the voltage sub-prediction model with the voltage curve of the first cycle through SOC, and make the two groups of voltage curves have the same length and be aligned in time through normalizing the time axis and linear interpolation processing. Then splice the two groups of voltage curves within the same SOC change value together, and set the lag k to half of the total length of the curve, that is The following calculation formula can be obtained:

[0067] where is the i-th voltage value of the spliced voltage curve, is the i + k-th voltage value of the spliced voltage curve, and N is the total amount of spliced voltage data.

[0068] (4) The remaining useful life sub-prediction model, which is built with a CNN-Transformer hybrid architecture, as shown in Attachment Figure 5 The input features include the number of cycles, the remaining capacity, and the autocorrelation coefficient of the lithium battery voltage (Acf) output by the autocorrelation coefficient calculation sub-module, and the output is the predicted value of the future capacity decay trajectory. The remaining useful life prediction model is used for data prediction between different cycles. After the voltage prediction model outputs the autocorrelation coefficient, it predicts the capacity decay state in the next several cycles.

[0069] The CNN module extracts short-term mutation features in capacity fluctuations through local convolution operations, while the Transformer module uses the global self-attention mechanism to model the long-range dependencies among the number of cycles, autocorrelation coefficient, and capacity decay. This hybrid architecture model embeds the number of cycles and voltage autocorrelation coefficient of the historical k cycles into the encoder after CNN embedding, and captures global dependencies through the self-attention module; takes the capacity offset by 1 cycle as the decoder input, and establishes a non-linear relationship among the number of cycles, voltage autocorrelation coefficient, and cycle capacity through self-attention and cross-attention, and outputs the capacity prediction value. Compared with Autoformer that relies on the fixed trend / cycle assumption of sequence decomposition, this architecture can capture the complex patterns of non-periodic decay and avoid the strong dependence on the steady-state time series prior of Autoformer through the synergistic effect of CNN-Transformer, thus significantly improving the adaptability to the non-stationary degradation process of lithium batteries.

[0070] Step 4: Input the processed feature data into the joint prediction model built in Step 3 for training, and iteratively update the weight parameters through the backpropagation algorithm, so that the joint prediction model synchronously learns the dynamic change law of SOC and the capacity decay trend, and finally obtains an optimized joint prediction model.

[0071] Step 5: Input the historical data into the optimized joint prediction model in Step 4 to obtain the prediction results of the state of charge and remaining life.

[0072] According to the number of cycles of the input historical m cycles, battery capacity, current, voltage, and SOC data at k historical moments, use the joint prediction model to predict the RUL of the next n cycles and the SOC of the next l moments, and realize the joint prediction of SOC and RUL.

Claims

1. A method for jointly predicting the state of charge and life of a lithium battery based on deep learning, characterized in that, It includes the following steps: Step 1: Conduct a lithium battery charge-discharge cycle life experiment under specified working conditions to obtain experimental data, including the current, voltage, temperature, state of charge, and remaining capacity of the lithium battery at different cycle numbers in the entire life cycle; Step 2: Perform data standardization processing on the experimental data obtained in Step 1 to construct an input data set containing standardized parameters and derived features; Step 3: Based on the Autoformer-Transformer architecture, build a joint prediction model for the state of charge and remaining service life of the lithium battery; The joint prediction model includes: A state-of-charge sub-prediction model, which is built with Autoformer as the architecture and is used to predict the state of charge at future times under real-time working conditions; A voltage sub-prediction model, which is built with Autoformer as the architecture and is used to start when the state of charge is lower than a certain value and predict the voltage curve from this moment to when the state of charge is 0; An autocorrelation coefficient calculation sub-module, which is used to calculate the autocorrelation coefficient between the predicted voltage curve output by the voltage sub-prediction model and the corresponding voltage curve of the first cycle, and input the calculated autocorrelation coefficient into the remaining service life sub-prediction model; A remaining service life sub-prediction model, which is built with a CNN-Transformer hybrid architecture and is used to predict the capacity attenuation state in the next several cycles according to the autocorrelation coefficient calculated by the autocorrelation coefficient calculation sub-module; Step 4: Input the processed feature data into the joint prediction model built in Step 3 for training and optimization to obtain an optimized joint prediction model; Step 5: Input historical data into the optimized joint prediction model in Step 4 to obtain the prediction results of the state of charge and remaining life.

2. The method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to claim 1, characterized in that, The said Step 2 includes: S21. Normalize the current, voltage, state of charge, and remaining capacity data obtained from the experiment in Step 1 to obtain standardized parameters; S22. Divide the time into 6 scales according to the year, month, day, hour, minute, and second according to the time stamp of the data, and use the 5 periodic features of month, day, hour, minute, and second as the time position encoding of the training data; Construct an input data set containing standardized parameters and derived features.

3. The method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to claim 1, characterized in that, The input of the state-of-charge sub-prediction model includes the current, voltage, current SOC, and cycle number features during the battery cycle, and the output is the predicted value of the state of charge at future times.

4. The method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to claim 3, characterized in that, The building principle of the state-of-charge sub-prediction model is: Utilize the ability of the Autoformer deep learning model to decouple periodic time series data into trend terms and seasonal terms to capture the changes in the battery charge-discharge characteristics caused by battery aging and the periodic changes in SOC during the cycle; The building process of the state-of-charge prediction model is: Record the first k historical data of current, voltage, number of cycles, and SOC as and input it into the Autoformer encoder, which captures the periodic dependencies of the sequence through the autocorrelation mechanism and aggregates similar subsequences from the underlying periods; Concatenate the current, voltage, number of cycles, SOC data from the (k - l)-th to the k-th time and 0 data at the p-th time as the input of the encoder, denoted as , where l is the amount of labeled data and p is the amount of predicted data; Denote the true SOC data from the k-th to the k + l-th time as as the label, and calculate the loss with the output of Autoformer to obtain the loss of this training and perform backpropagation to optimize the model parameters; After multiple trainings, a state-of-charge sub-prediction model is obtained.

5. The method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to claim 1, characterized in that, The input of the voltage sub-prediction model is the current, voltage, SOC, cycle number, and remaining capacity features of each cycle, and the output feature is the predicted value of the voltage at future times.

6. The method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to claim 5, characterized in that, The building principle of the voltage sub-prediction model is: The Autoformer deep learning model decouples sequence data with periodic characteristics into trend components and seasonal components through a time-domain decomposition mechanism, and separately models the charge and discharge characteristic drift induced by battery aging and the periodic pattern of voltage fluctuations during cycling; by jointly characterizing the two trends, multi-scale prediction of battery degradation and cyclic dynamic evolution is achieved.

7. The method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to claim 1, characterized in that, The calculation method of the autocorrelation coefficient is as follows: Align the predicted voltage curve output by the voltage sub-prediction model with the voltage curve of the first cycle through SOC. Make the two voltage curves have the same length and be aligned in time by normalizing the time axis and linear interpolation processing. Then splice the two voltage curves within the same SOC change value together. Set the lag k to half of the total length of the curve, that is , and the calculation formula for the autocorrelation coefficient Acf is as follows: ; Among them, , , is the i-th voltage value of the spliced voltage curve, is the (i + k)-th voltage value of the spliced voltage curve, and N is the total amount of spliced voltage data.

8. The method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to claim 1, characterized in that, The input features of the remaining useful life sub-prediction model include the number of cycles, the remaining capacity, and the voltage autocorrelation coefficient output by the autocorrelation coefficient calculation sub-module, and the output is the predicted value of the future capacity decay trajectory.

9. A method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to claim 8, characterized in that, The construction principle of the remaining useful life sub-prediction model is as follows: The CNN module extracts short-term mutation features in capacity fluctuations through local convolution operations, while the Transformer module uses the global self-attention mechanism to model the long-range dependence relationship between the number of cycles, the autocorrelation coefficient, and capacity decay; The construction process of the remaining useful life sub-prediction model is as follows: The number of cycles and the voltage autocorrelation coefficient of the historical k cycles are input into the encoder after being embedded by the CNN, and the global dependence relationship is captured through the self-attention module; the capacity offset by 1 cycle is used as the decoder input, and the non-linear relationship between the number of cycles, the voltage autocorrelation coefficient, and the cycle capacity is established after self-attention and cross-attention, and the capacity prediction value is output.

10. A method for jointly predicting the state of charge and life of a lithium battery based on deep learning according to claim 1, characterized in that, Step five includes: inputting historical data into the optimized joint prediction model to first obtain the predicted SOC and the predicted voltage, then calculating the autocorrelation between the predicted voltage curve and the voltage curve of the first cycle of the lithium battery through the autocorrelation coefficient calculation sub-module to obtain the autocorrelation coefficient, and inputting the autocorrelation coefficient into the remaining useful life sub-prediction model to obtain the predicted value of the lithium battery capacity.

Images