Battery RUL prediction method and device based on variable mode decomposition, equipment and medium

Abstract

The invention discloses a battery RUL prediction method, device and equipment based on variable mode decomposition, and a medium, and relates to the technical field of batteries, and the method comprises the steps: obtaining battery capacity degradation data and a battery capacity failure threshold value, carrying out the smoothing of the battery capacity degradation data through variable mode decomposition, obtaining a plurality of mode components, and carrying out the smoothing of the multiple mode components; and obtaining battery capacity key feature data according to the plurality of modal components, inputting the battery capacity key feature data into the optimized support vector regression model to obtain a battery capacity predicted value, and when the battery capacity predicted value is smaller than a battery capacity failure threshold, performing calculation to obtain the remaining service life of the battery. According to the method, the battery capacity degradation data is smoothly processed through variable mode decomposition, key features are extracted and input into the optimized SVR model to predict the battery capacity, when the predicted battery capacity is lower than a failure threshold value, the remaining service life of the battery is calculated, and the RUL prediction precision and robustness are improved.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery RUL prediction method, device, equipment and medium based on variable mode decomposition. Background Art

[0002] Lithium-ion batteries are widely used in consumer electronics, electric vehicles, and space systems because they have higher energy density, longer cycle life, and lower self-discharge rate. However, as the use time increases, the performance of lithium batteries will gradually decline. This irreversible change affects the safety and reliability of the battery.

[0003] In terms of lithium battery RUL prediction, the method based on support vector regression (SVR) is a commonly used one. The generalization ability of the traditional SVR method is limited when faced with large data noise or large fluctuations in the nonlinear degradation process, resulting in a decrease in prediction accuracy. Relying solely on PSO for parameter optimization also has certain limitations, such as being prone to falling into local optimal solutions. In order to solve these problems, an optimization scheme combined with variable mode decomposition is proposed to improve the accuracy of SVR model prediction. Summary of the invention

[0004] The main purpose of this application is to provide a battery RUL prediction method, device, equipment and medium based on variable mode decomposition, aiming to solve the technical problem of how to improve the accuracy of battery RUL prediction.

[0005] To achieve the above objectives, the present application proposes a battery RUL prediction method based on variable mode decomposition, the method comprising: Obtain battery capacity degradation data and battery capacity failure threshold; Using variable mode decomposition to smooth the battery capacity degradation data to obtain multiple modal components; Obtaining key characteristic data of battery capacity according to the plurality of modal components; The battery capacity prediction value is obtained by inputting the key characteristic data of the battery capacity into an optimized support vector regression model; When the predicted value of the battery capacity is less than the battery capacity failure threshold, the remaining service life of the battery is calculated.

[0006] In one embodiment, before the step of obtaining a predicted value of battery capacity by inputting the key characteristic data of battery capacity into an optimized support vector regression model, the step includes: Dividing the battery capacity key feature data into a training set and a test set; Obtain training set feature data and establish an initial support vector regression model; The initial support vector regression model is optimized according to the feature data of the training set using a particle swarm optimization algorithm to obtain an optimized support vector regression model.

[0007] In one embodiment, before the step of optimizing the initial support vector regression model according to the training set feature data using a particle swarm optimization algorithm to obtain an optimized support vector regression model, the step includes: Obtain particle filtering in the feature data of the training set; Initialize algorithm parameters and set category measurement thresholds; Dividing a particle population in the particle filter into a plurality of subgroups; Calculate the fitness value of each particle in each subgroup, and initialize the best position of each individual subgroup and the best position of the group; Update the fitness value of each subgroup individual, the best position of each subgroup individual and the best position of the group according to the functional relationship; Determine whether the type metric value exceeds the type metric threshold, if the particle type metric value is less than the type metric threshold, re-plan the particle population; Determine whether the maximum number of iterations has been reached. If so, output the model parameters. After the model parameters are input into the nonlinear fitting function, the parameter penalty factor and the kernel parameter for constructing the optimized support vector model are obtained.

[0008] In one embodiment, the step of optimizing the initial support vector regression model according to the training set feature data using a particle swarm optimization algorithm to obtain an optimized support vector regression model includes: Obtaining a particle swarm in the training set data information; Initializing the positions and velocities of particles in the particle swarm and calculating the fitness of the particles; Set the number of iterations and calculate the historical optimal value of the particle; By comparing the fitness of the particles in each iteration, a global optimal value of the particle swarm is obtained; According to the historical optimal value and the global optimal value, the position and speed of the particle are updated; Determine whether the position and speed of the particle meet a preset range, and if not, perform boundary condition processing on the particle; If the number of iterations is reached or the error is less than the preset error, the optimized support vector regression model is obtained.

[0009] In one embodiment, the step of obtaining a predicted value of battery capacity by inputting the key characteristic data of battery capacity into an optimized support vector regression model comprises: Get the preset starting point cycle period and the selected cycle period; When the selected cycle period exceeds the cycle period of the preset starting point, the selected cycle period is substituted into the optimized support vector regression model to obtain a battery capacity prediction value.

[0010] In one embodiment, the step of smoothing the battery capacity degradation data using variable mode decomposition to obtain multiple modal components includes: Get modal decomposition parameters; According to the modal decomposition parameters, the number of modal components and the center frequencies of the modal components are obtained; The battery capacity degradation data is iteratively decomposed based on the number of modal components and the center frequency to extract a plurality of modal components.

[0011] In one embodiment, when the battery capacity prediction value is less than the battery capacity failure threshold, the step of calculating the remaining battery life further includes: Get preset cycle and select cycle; When the battery capacity prediction value is greater than the battery capacity failure threshold, the selected cycle period is accumulated with the preset cycle as a new cycle period to obtain the battery capacity prediction value of the new cycle period, and so on, until the battery capacity prediction value of the new cycle period does not exceed the battery capacity failure threshold.

[0012] In addition, to achieve the above purpose, the present application also proposes a battery RUL prediction device based on variable mode decomposition, and the battery RUL prediction device based on variable mode decomposition includes: An acquisition module, used to acquire battery capacity degradation data and battery capacity failure threshold; A processing module, used for smoothing the battery capacity degradation data by using variable mode decomposition to obtain multiple modal components; An extraction module, used for obtaining key characteristic data of battery capacity according to a plurality of said modal components; A calculation module, used for obtaining a predicted value of battery capacity by inputting the key characteristic data of battery capacity into an optimized support vector regression model; The result module is used to calculate the remaining service life of the battery when the battery capacity prediction value is less than the battery capacity failure threshold.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a medium, which is a computer-readable medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the battery RUL prediction method based on variable mode decomposition as described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the battery RUL prediction method based on variable mode decomposition as described above.

[0015] This application obtains battery capacity degradation data and battery capacity failure threshold, uses variable mode decomposition to smooth the battery capacity degradation data, obtains multiple modal components, obtains key feature data of battery capacity based on multiple modal components, and obtains battery capacity prediction value by inputting the key feature data of battery capacity into the optimized support vector regression model. When the battery capacity prediction value is less than the battery capacity failure threshold, the remaining battery service life is calculated. By using variable mode decomposition to smooth the battery capacity degradation data, extracting key features and inputting them into the optimized SVR model to predict the battery capacity, when the predicted battery capacity is lower than the failure threshold, the remaining battery service life is calculated, thereby improving the RUL prediction accuracy and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] Figure 1 This is a flow chart of the first embodiment of the battery RUL prediction method based on variable mode decomposition of the present application; Figure 2 This is a battery parameter table diagram of the first embodiment of the battery RUL prediction method based on variable mode decomposition of this application; Figure 3 This is an A01 battery RUL prediction performance evaluation diagram of the first embodiment of the battery RUL prediction method based on variable mode decomposition of this application; Figure 4 This is an A02 battery RUL prediction performance evaluation diagram of the first embodiment of the battery RUL prediction method based on variable mode decomposition of this application; Figure 5 A01 is a lithium battery RUL diagram for early and late battery degradation prediction of the first embodiment of the battery RUL prediction method based on variable mode decomposition of the present application; Figure 6 A02 is a lithium battery RUL diagram for predicting early and late battery degradation of the first embodiment of the battery RUL prediction method based on variable mode decomposition of the present application; Figure 7 APSO and PSO fitness curve diagram of the first embodiment of the battery RUL prediction method based on variable mode decomposition in this application; Figure 8 This is a flow chart of a second embodiment of a battery RUL prediction method based on variable mode decomposition of the present application; Fig. 9 This is a flow chart of a third embodiment of a battery RUL prediction method based on variable mode decomposition of the present application; Fig.10 This is a schematic diagram of the module structure of a battery RUL prediction device based on variable mode decomposition according to an embodiment of the present application; Fig.11 Schematic diagram of the device structure of the hardware operating environment involved in the battery RUL prediction method based on variable mode decomposition in the embodiment of the present application.

[0018] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0020] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The main solution of the embodiment of the present application is: by acquiring battery capacity degradation data and a battery capacity failure threshold, the battery capacity degradation data is smoothed by using variable mode decomposition to obtain multiple modal components, and the key characteristic data of the battery capacity is obtained according to the multiple modal components. The battery capacity prediction value is obtained by inputting the key characteristic data of the battery capacity into an optimized support vector regression model. When the battery capacity prediction value is less than the battery capacity failure threshold, the remaining service life of the battery is calculated.

[0022] Based on this, the embodiment of the present application provides a battery RUL prediction method based on variable mode decomposition, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the battery RUL prediction method based on variable mode decomposition of the present application.

[0023] In this embodiment, the battery RUL prediction method based on variable mode decomposition includes steps S10 to S50: Step S10, obtaining battery capacity degradation data and battery capacity failure threshold.

[0024] It should be noted that in order to obtain reliable battery capacity degradation data, through strictly controlled cycle life experiments, the battery capacity after each charge and discharge cycle can be recorded to form a capacity degradation curve that changes over time. This curve shows how the battery capacity gradually decreases as the number of cycles increases, and can reflect the aging trend under different conditions. For batteries in practical applications, due to the complex and changeable working environment, including the influence of factors such as temperature, load mode, and charging rate, sensors or specific data acquisition equipment are needed to monitor and accumulate these data for a long time. When determining the battery capacity failure threshold, the standard is that when the battery capacity drops to about 80% of its initial nominal capacity, the battery is considered to have reached its end of life (EOL). This standard is based on considerations of battery performance and safety, because below this threshold, the battery no longer meets the application requirements and will experience faster performance degradation. However, in some special cases, depending on the specific application scenarios, such as electric vehicles, aerospace, etc., different failure thresholds will be set to adapt to higher safety requirements or optimize cost-effectiveness.

[0025] Specifically, in this embodiment, the experimental battery capacity degradation data comes from 8 lithium batteries (battery data sets A01, A02, A03, A04 provided by Stanford University and battery data sets cs2_35, cs2_36, cs2_37, cs2_38 from CACLE of the University of Maryland).

[0026] Step S20: Smoothing the battery capacity degradation data by using variable mode decomposition to obtain multiple modal components.

[0027] It should be noted that Variational Mode Decomposition (VMD) is an advanced algorithm for processing nonlinear and non-stationary signals. It can decompose complex battery capacity degradation data into multiple intrinsic mode functions (IMFs) with different center frequencies. Each IMF represents a part of the characteristics of the original data. The process of smoothing battery capacity degradation data using VMD begins with constructing an optimization problem, the purpose of which is to find a set of optimal IMFs so that their bandwidth is minimized and the sum of all IMFs is equal to the original signal. VMD solves this constrained optimization problem by introducing a quadratic penalty factor and Lagrange multipliers to ensure that there is no cross-influence between the IMFs. Specifically, for each IMF, its Hilbert transform related to the instantaneous phase is calculated, and the spectrum information is obtained by modulating it to the baseband, and then the bandwidth of the IMF is estimated. Then, the alternating direction multiplier method is used to iteratively update the IMF and its center frequency until the convergence condition is met. In this way, multiple noise-free and mutually independent IMF components are obtained.

[0028] Step S30, obtaining key characteristic data of battery capacity according to multiple modal components.

[0029] It should be noted that from the above-mentioned multiple IMFs, those parts that are most related to the battery capacity attenuation trend are screened out, such as those low-frequency IMFs that show long-term slow changes. This step can be done by analyzing the Pearson correlation coefficient between each IMF, and selecting IMFs with strong or extremely strong correlation as the basis for the reconstructed lithium battery capacity data. This not only eliminates high-frequency noise interference, but also retains the main dynamic characteristics of the original data. Finally, based on the above-processed IMF components, the optimized support vector regression (APSO-SVR) model is used to predict the remaining battery life.

[0030] Step S40, obtaining a predicted value of battery capacity by inputting the key characteristic data of battery capacity into an optimized support vector regression model.

[0031] It should be noted that before using the optimized support vector model for prediction, two important parameters need to be determined: the penalty factor and the kernel parameter. These two parameters determine the complexity of the APSO-SVR model and the degree of fit to the training data.

[0032] Furthermore, when the optimization is completed and the parameters are selected, the next step is to input the key characteristic data of battery capacity obtained through VMD processing into the APSO-SVR model as input variables. These characteristic data contain important information reflecting the battery aging process, such as capacity decay rate, degradation mode under temperature influence, etc. By learning these features, the APSO-SVR model can capture the intrinsic connection between battery capacity change and time, and then output the predicted value of battery capacity. It is worth noting that in order to further improve the prediction effect, other relevant factors can also be considered as auxiliary inputs, such as ambient temperature, charge and discharge current, etc., which helps to more comprehensively describe the working state of the battery.

[0033] Step S50: When the predicted value of the battery capacity is less than the battery capacity failure threshold, the remaining service life of the battery is calculated.

[0034] It should be noted that when the predicted battery capacity is lower than the preset failure threshold, it means that the lithium-ion battery may be approaching the end of its life. At this time, in order to ensure the safe operation of the system and avoid unexpected failures, the remaining useful life (RUL) of the battery needs to be calculated. RUL refers to the length of time from the current moment to the complete failure of the battery.

[0035] Specifically, the previously established APSO-SVR model is used to predict the future battery capacity change trend. By inputting the latest battery status information into the APSO-SVR model, a capacity prediction curve for a period of time in the future can be obtained. Then, based on the difference between this prediction curve and the failure threshold, it is estimated how long the battery can maintain normal operation, that is, RUL.

[0036] Furthermore, the preset starting point cycle period and the selected cycle period are obtained. When the selected cycle period exceeds the preset starting point cycle period, the selected cycle period is substituted into the optimized support vector regression model to obtain the battery capacity prediction value. Specifically, the preset starting point cycle period refers to the reference number of cycles used to start RUL prediction, which is usually determined based on when the battery capacity decays to a certain level, such as when the capacity drops to 90% or 85% of the initial value. The choice of this starting point depends on application requirements and safety standards. The selected cycle period refers to the time series data points after the preset starting point, which will be used to train the APSO-SVR model test to ensure that the prediction results can reflect the actual degradation trend of the battery. At the same time, the preset cycle period and the selected cycle period are obtained. When the battery capacity prediction value is greater than the battery capacity failure threshold, the selected cycle period is accumulated by the preset cycle period as a new cycle period to obtain the battery capacity prediction value of the new cycle period, and so on, until the battery capacity prediction value of the new cycle period does not exceed the battery capacity failure threshold. The preset cycle refers to the fixed number of cycles that advance each time the prediction is made. When the predicted value of the battery capacity is still higher than the set failure threshold, we will select the cycle plus the preset cycle as the new cycle, and use the APSO-SVR model again to predict the battery capacity under the new cycle. Whenever the prediction result shows that the battery capacity is still within the safe range, that is, greater than the failure threshold, continue to increase the preset cycle and recalculate. In this way, we can gradually approach the end of the actual life of the battery until a prediction shows that the predicted value of the battery capacity of the new cycle does not exceed the failure threshold.

[0037] First, when using the APSO-SVR model for prediction, the battery capacity degradation data is normalized:

[0038] in, is the capacity of the kth cycle, is the initial capacity of the battery. In this embodiment, the experimental battery capacity degradation data are selected from the battery data sets A01 and A02 of Stanford University and the battery data sets cs2_35 and cs2_36 of the University of Maryland. Figure 2 The battery parameter table shown in the figure shows that 95% represents the number of cycles required for the capacity to decay to 95% of the nominal capacity, and the rest are similar.

[0039] Specifically, for the battery data set of Stanford University, three methods, namely standard PSO-SVR, APSO-SVR and VMD-APSO-SVR of this embodiment, are used to compare and analyze the battery RUL prediction performance of two batteries A01 and A02 of Stanford University, and RMSE, MAE, MAPE, ERUL and Five indicators are used to verify the effectiveness. Two different prediction starting points are set for the two groups of experiments (40% training 60% test and 70% training 30% test). 40% and 70% of the sample data are used as the data for training the prediction model, and the remaining data are used as the test data to predict RUL and capacity. The experiments designed in this section are as follows: the sample data and the smoothed data are normalized to 0-1, and e-SVR is selected to build a black box model with the cycle as input and capacity as output. The loss function is set to 0.001, the kernel function is set to the radial basis kernel function, and the maximum and minimum values ​​of the penalty factor and kernel parameters are set to [ The experimental results are as follows: Figure 3 A01 battery RUL prediction performance evaluation diagram and Figure 4 The A02 battery RUL prediction performance evaluation diagram shown in the figure shows that the VMD-APSO-SVR method has the smallest generalization error and is closest to the real data compared with the other two methods. Therefore, when the amount of data is small in the early stage of degradation, the three methods cannot accurately judge the accelerated degradation process of lithium batteries, but the VMD-APSO-SVR method prediction curve is closest to the real curve, while the PSO-SVR method has the worst fitting effect. Figure 5 A01 battery degradation early and late prediction lithium battery RUL diagram and Figure 6 The A02 battery degradation early and late prediction lithium battery RUL diagram shown and Figure 7 The APSO and PSO fitness curves shown in the figure show that the PSO-SVR, APSO-SVR and VMD-APSO-SVR methods have poor prediction effects in the early stages of degradation, especially in long-term predictions, and cannot predict the accelerated degradation process of lithium batteries. However, the PSO-SVR method has a larger offset in short-term predictions than the other two methods. The overall trend of the predicted value of the APSO-SVR method continues the degradation trend of the training data and has a smaller upward offset from the true value. As the training samples increase from 40% to 70%, the prediction deviation gradually decreases overall, the prediction error begins to decline, all five indicators decrease, and the prediction performance improves. This shows that the more samples, the better the model training, the smaller the generalization error, and the APSO fitness curve has a better effect. Therefore, the lithium-ion battery RUL prediction method based on VMD-APSO-SVR in this embodiment has a small short-term prediction error and high computational efficiency.

[0040] This embodiment provides a battery RUL prediction method based on variable mode decomposition, by obtaining battery capacity degradation data and battery capacity failure threshold, using variable mode decomposition to smooth the battery capacity degradation data, to obtain multiple modal components, and obtaining battery capacity key feature data based on the multiple modal components, and by inputting the battery capacity key feature data into the optimized support vector regression model, the battery capacity prediction value is obtained, and when the battery capacity prediction value is less than the battery capacity failure threshold, the remaining battery service life is calculated. By using variable mode decomposition to smooth the battery capacity degradation data, extracting key features and inputting them into the optimized SVR model to predict the battery capacity, when the predicted battery capacity is lower than the failure threshold, the remaining battery service life is calculated, thereby improving the RUL prediction accuracy and robustness.

[0041] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 8 The battery RUL prediction method based on variable mode decomposition further includes steps S401 to S403 before step S40: Step S401, dividing the key characteristic data of battery capacity into a training set and a test set.

[0042] It should be noted that, according to the chronological order or random sampling method, a part of the data is divided out from all the data as the training set, and this part of the data is used to train the model. The training set contains paired data of historical cycle periods and corresponding battery capacities. Through these data, the APSO-SVR model can learn the patterns and laws of battery degradation. At the same time, another part of the data is divided into a test set. The data in the test set does not participate in the training process of the model, but is used to test the performance of the trained model on unseen data. Doing so can avoid the occurrence of overfitting, that is, the model only remembers the training data but fails to truly grasp the logical relationship behind it. By comparing the differences between the model's predictions on the test set and the actual values, such as the root mean square error (RMSE), the coefficient of determination ( ) and other indicators can be used to quantitatively evaluate the prediction accuracy and robustness of the model.

[0043] Step S402, obtaining training set feature data and establishing an initial support vector regression model.

[0044] Step S403, optimizing the initial support vector regression model according to the feature data of the training set using a particle swarm optimization algorithm to obtain an optimized support vector regression model.

[0045] It should be noted that before obtaining the optimized support vector regression model, its parameters, namely the parameter penalty factor and the kernel parameter, need to be determined.

[0046] Furthermore, before step S403, it includes: obtaining the particle filter in the training set feature data; initializing the algorithm parameters, setting the species measurement threshold; dividing the particle population in the particle filter into multiple subgroups; calculating the fitness value of each particle in each subgroup, and initializing the optimal position of each individual subgroup and the optimal position of the group; updating the fitness value of each individual subgroup, the optimal position of each individual subgroup and the optimal position of the group according to the functional relationship; judging whether the species measurement value exceeds the species measurement threshold, if the particle species measurement value is less than the species measurement threshold, re-planning the particle population; judging whether the maximum number of iterations has been reached, if so, outputting the model parameters; after inputting the model parameters into the nonlinear fitting function, obtaining the parameter penalty factor and kernel parameters for constructing the optimized support vector model. Specifically, the algorithm parameters are initialized. Including the total number of particles M, the maximum number of iterations , the maximum value of inertia weight and minimum value , learning factors c1, c2, particle position value , the speed value is , set the population diversity metric threshold. The particle population in the particle filter is divided into multiple subgroups to explore the search space more effectively. The particles in each subgroup interact with each other according to their own position update rules and calculate their respective fitness values. The fitness function here is a standard used to measure the quality of the particle solution to a given problem. For example, in this study, it can be an error indicator between the actual battery capacity. After the initialization phase, each subgroup will record the individual best position PP(t) and the group best position PG(t), which represent the best solutions found so far by a single particle and the entire group, respectively. As the iterative process progresses, the speed and position of the particles in each subgroup are continuously updated according to the pre-defined functional relationship, and the fitness value is recalculated and the best position of the individual and the group is adjusted accordingly. This update mechanism is designed to guide the particles to move in the direction of a better solution and gradually approach the global optimal solution. During this period, it is also necessary to regularly check whether the particle species metric exceeds the preset threshold. If the particle species metric is found to be lower than the threshold, it means that the particle diversity is insufficient, which may lead to premature phenomenon. At this time, it is necessary to re-plan the particle population and introduce new mutations or crossover operations to increase diversity. When the maximum number of iterations is reached, the optimization process ends, and the output is the optimal model parameters obtained after multiple rounds of iterations - penalty factor C and kernel parameter g. These parameters are then input into the nonlinear fitting function to construct the final optimized support vector regression model.

[0047] Furthermore, step S403 also includes: obtaining a particle swarm in the training set data information; initializing the position and velocity of particles in the particle swarm and calculating the fitness of particles; setting the number of iterations, calculating the historical optimal value of particles; obtaining the global optimal value of the particle swarm by comparing the fitness of particles in each iteration; updating the position and velocity of particles according to the historical optimal value and the global optimal value; judging whether the position and velocity of particles meet the preset range, if not, performing boundary condition processing on particles; if the number of iterations is reached or the error is less than the preset error, an optimized support vector regression model is obtained. Specifically, in the initial stage, an initial position and velocity are randomly assigned to each particle. The position represents the coordinates of the particle in the search space, corresponding to a set of SVR parameters; and the velocity determines the direction and distance of the particle in subsequent iterations. At the same time, the fitness value of each particle is calculated, which is usually by using the position of the particle as an APSO-SVR model parameter to evaluate the degree of fit of the model to the training data, such as minimizing the mean square error (MSE) or maximizing the cross-validation score. Next, a reasonable number of iterations is set as one of the termination conditions, and the iteration process is started. In each iteration, the fitness value of the current particle is calculated based on its position and compared with the historical optimal value of the particle. If the new fitness is better, the historical optimal value is updated. At the same time, the historical optimal values ​​of all particles are used to update the global optimal value, that is, the best solution found by the entire group. The global optimal value reflects the best parameter combination explored by all particles so far. As the iteration proceeds, the position and speed of each particle are adjusted according to a specific speed update formula based on the historical optimal value and the global optimal value. This process simulates the information sharing and learning mechanism between particles, allowing particles to move towards a better solution. For example, the new speed of a particle can be a weighted sum of the current speed, the attraction toward the historical optimal value, and the attraction toward the global optimal value. In order to ensure that the particle remains within a reasonable range, it is necessary to determine whether the position and speed of the particle meet the preset range. If not, the particle must be subjected to boundary condition processing, such as rebound, truncation, or re-randomization, to prevent the particle from flying out of the effective search space, thereby ensuring the effectiveness and stability of the algorithm. When the set number of iterations is reached or the error is less than the preset error, it indicates that the optimization process is over and the optimized support vector regression model is output.

[0048] In this embodiment, the key characteristic data of battery capacity are divided into a training set and a test set, and an initial support vector regression model is established by using the training set and optimized by a particle swarm optimization algorithm to improve the prediction accuracy and model robustness.

[0049] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above description, and will not be described in detail later. Fig. 9The battery RUL prediction method based on variable mode decomposition step S20 also includes steps S201 to S203: Step S201, obtaining modal decomposition parameters.

[0050] It should be noted that the modal decomposition parameters are key settings in the variational mode decomposition (VMD) algorithm, which are used to decompose complex signals such as lithium-ion battery capacity degradation data into a series of intrinsic mode functions (IMFs) with different center frequencies. In the process of obtaining these parameters, the number of modal components of the sequence to be decomposed must be determined first, that is, the number of IMF components that are expected to be obtained. This choice depends on the complexity of the original signal and the purpose of the analysis; for battery capacity prediction, different numbers of modal components are usually tried to find the group of IMFs that best reflects the battery aging characteristics.

[0051] Another important parameter is the weight, which controls the weight of the bandwidth penalty term and affects the smoothness of each IMF component. A larger weight value will result in a narrower bandwidth, making the decomposed IMF smoother but may lose some detail information; on the contrary, a smaller weight value allows a wider bandwidth, which can retain more high-frequency components but may also introduce noise. Therefore, in practical applications, adjustments need to be made according to specific circumstances to achieve the best balance.

[0052] In addition, the time step is also a parameter that cannot be ignored. It determines the requirement for the accuracy of instantaneous phase estimation during the iteration process. Properly setting the time step can speed up the convergence without sacrificing the decomposition quality. Finally, the initialization noise tolerance is used as the termination condition. When the change between two consecutive iterations is less than this threshold, the algorithm is considered to have converged.

[0053] Step S202: Obtain the number of modal components and the center frequencies of the modal components according to the modal decomposition parameters.

[0054] It should be noted that after setting the key parameters of VMD, the algorithm will iteratively search for the optimal solution and finally output multiple IMFs and their corresponding center frequencies. These IMFs each represent the information of different frequency bands in the original signal, which contains various fluctuation characteristics from high frequency to low frequency. Specifically, when the appropriate number of modal components is selected, VMD can effectively separate the changes at various levels related to the battery aging process. For example, in the study of lithium-ion batteries, some IMFs may show a trend of rapid changes, corresponding to the impact of short-term charge and discharge cycles; while other lower-frequency IMFs reflect long-term aging patterns. Each IMF has its own unique center frequency, which helps to distinguish different types of degradation behaviors and can reveal the physical mechanisms hidden behind complex data. By analyzing the obtained modal components and their center frequencies, we can better understand the law of battery performance evolution over time. For example, if the center frequency of a particular IMF is close to or below a critical value, it may be caused by a slowdown in the chemical reaction rate inside the battery, indicating that the battery is about to enter an accelerated decay stage.

[0055] Step S203, iteratively decompose the battery capacity degradation data based on the number of modal components and the center frequency to extract multiple modal components.

[0056] It should be noted that VMD decomposes the original battery capacity degradation time series into multiple intrinsic mode functions (IMFs), each of which represents the fluctuation characteristics within a specific frequency band. This decomposition is not a one-time process, but is gradually completed through an iterative optimization process.

[0057] Furthermore, in each iteration, the VMD algorithm attempts to minimize the sum of all IMF bandwidths while ensuring that these IMFs can be added together to reconstruct the original signal. For each IMF, its Hilbert transform related to the instantaneous phase is calculated, and the spectrum information is obtained by modulating it to the baseband. Then, the bandwidth of the IMF is estimated and its center frequency is updated. As the number of iterations increases, each IMF gradually converges to the optimal solution, and finally a set of smooth and independent IMF components are obtained, which together reflect the different characteristics of battery capacity over time. From these extracted IMFs, those parts that best represent the battery aging behavior can be further screened out, such as those low-frequency IMFs that show a long-term slow change trend. This is because high-frequency components often contain more noise or short-term disturbances, while low-frequency IMFs are closer to the actual aging mode of the battery. In addition, the correlation between each IMF and the original signal can be measured based on the Pearson correlation coefficient, and IMFs with strong or extremely strong correlations can be selected as the basis for the reconstructed lithium battery capacity data to improve the accuracy of subsequent analysis.

[0058] This embodiment obtains modal decomposition parameters, determines the number of modal components and the center frequency, and based on this, iteratively decomposes the battery capacity degradation data to extract multiple modal components, effectively removes noise, and improves the accuracy and robustness of the prediction model.

[0059] This application also provides a battery RUL prediction device based on variable mode decomposition, please refer to Fig.10 , the device comprises: The acquisition module 10 is used to acquire battery capacity degradation data and a battery capacity failure threshold.

[0060] The processing module 20 is used to perform smoothing processing on the battery capacity degradation data by using variable mode decomposition to obtain multiple modal components.

[0061] The extraction module 30 is used to obtain key characteristic data of battery capacity according to multiple modal components.

[0062] The calculation module 40 is used to obtain a predicted value of the battery capacity by inputting the key characteristic data of the battery capacity into the optimized support vector regression model.

[0063] The result module 50 is used to calculate the remaining service life of the battery when the predicted value of the battery capacity is less than the battery capacity failure threshold.

[0064] The battery RUL prediction device based on variable mode decomposition provided by the present application adopts the battery RUL prediction method based on variable mode decomposition in the above embodiment, which can solve the technical problem of how to improve the accuracy of battery RUL prediction. Compared with the prior art, the beneficial effects of the battery RUL prediction device based on variable mode decomposition provided by the present application are the same as the beneficial effects of the battery RUL prediction method based on variable mode decomposition provided by the above embodiment, and the other technical features in the battery RUL prediction device based on variable mode decomposition are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0065] In one embodiment, the calculation module 40 is also used to divide the key characteristic data of the battery capacity into a training set and a test set; obtain the characteristic data of the training set and establish an initial support vector regression model; optimize the initial support vector regression model according to the particle swarm optimization algorithm based on the characteristic data of the training set to obtain an optimized support vector regression model.

[0066] In one embodiment, the calculation module 40 is also used to obtain the particle filter in the training set feature data; initialize the algorithm parameters and set the species measurement threshold; divide the particle population in the particle filter into multiple subgroups; calculate the fitness value of each particle in each subgroup, and initialize the optimal position of each subgroup individual and the optimal position of the group; update the fitness value of each subgroup individual, the optimal position of each subgroup individual and the optimal position of the group according to the functional relationship; determine whether the species measurement value exceeds the species measurement threshold, if the particle species measurement value is less than the species measurement threshold, re-plan the particle population; determine whether the maximum number of iterations is reached, if reached, output the model parameters; after inputting the model parameters into the nonlinear fitting function, obtain the parameter penalty factor and kernel parameter for constructing the optimized support vector model.

[0067] In one embodiment, the calculation module 40 is also used to obtain a particle swarm in the training set data information; initialize the position and velocity of particles in the particle swarm and calculate the fitness of the particles; set the number of iterations and calculate the historical optimal value of the particles; obtain the global optimal value of the particle swarm by comparing the fitness of the particles in each iteration; update the position and velocity of the particles according to the historical optimal value and the global optimal value; determine whether the position and velocity of the particles meet the preset range, and if not, perform boundary condition processing on the particles; if the number of iterations is reached or the error is less than the preset error, an optimized support vector regression model is obtained.

[0068] In one embodiment, the calculation module 40 is also used to obtain the preset starting point cycle period and the selected cycle period; when the selected cycle period exceeds the cycle period of the preset starting point, the selected cycle period is substituted into the optimized support vector regression model to obtain the battery capacity prediction value.

[0069] In one embodiment, the processing module 20 is also used to obtain modal decomposition parameters; obtain the number of modal components and the center frequency of the modal components according to the modal decomposition parameters; iteratively decompose the battery capacity degradation data based on the number of modal components and the center frequency to extract multiple modal components.

[0070] In one embodiment, the result module 50 is also used to obtain a preset period and select a cycle period; when the battery capacity prediction value is greater than the battery capacity failure threshold, the selected cycle period plus the preset period is used as a new cycle period to obtain the battery capacity prediction value of the new cycle period, and so on, until the battery capacity prediction value of the new cycle period does not exceed the battery capacity failure threshold.

[0071] The present application provides a battery RUL prediction device based on variable mode decomposition, and the battery RUL prediction device based on variable mode decomposition includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the battery RUL prediction method based on variable mode decomposition in the above-mentioned embodiment one.

[0072] Reference below Fig.11 , which shows a schematic diagram of the structure of a battery RUL prediction device based on variable mode decomposition suitable for implementing an embodiment of the present application. The battery RUL prediction device based on variable mode decomposition in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.11 The battery RUL prediction device based on variable mode decomposition is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present application.

[0073] like Fig.11As shown, the battery RUL prediction device based on variable mode decomposition may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the battery RUL prediction device based on variable mode decomposition are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other via a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the battery RUL prediction device based on variable mode decomposition to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a battery RUL prediction device based on variable mode decomposition with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or provided alternatively.

[0074] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0075] The battery RUL prediction device based on variable mode decomposition provided by the present application adopts the battery RUL prediction method based on variable mode decomposition in the above embodiment, which can solve the technical problem of how to improve the accuracy of battery RUL prediction. Compared with the prior art, the beneficial effects of the battery RUL prediction device based on variable mode decomposition provided by the present application are the same as the beneficial effects of the battery RUL prediction method based on variable mode decomposition provided by the above embodiment, and the other technical features in the battery RUL prediction device based on variable mode decomposition are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0076] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0077] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0078] The present application provides a computer-readable medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the battery RUL prediction method based on variable mode decomposition in the above-mentioned embodiment.

[0079] The computer-readable medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0080] The computer-readable medium may be included in the battery RUL prediction device based on variable mode decomposition; or may exist independently without being assembled into the battery RUL prediction device based on variable mode decomposition.

[0081] The computer-readable medium carries one or more programs. When the one or more programs are executed by the battery RUL prediction device based on variable mode decomposition, the battery RUL prediction device based on variable mode decomposition can be written in one or more programming languages ​​or a combination thereof to perform computer program codes for the operation of the present application. The programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0082] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0083] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0084] The readable medium provided in this application is a computer-readable medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned battery RUL prediction method based on variable mode decomposition, and can solve the technical problem of how to improve the accuracy of battery RUL prediction. Compared with the prior art, the beneficial effects of the computer-readable medium provided in this application are the same as the beneficial effects of the battery RUL prediction method based on variable mode decomposition provided in the above-mentioned embodiment, and will not be repeated here.

[0085] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the battery RUL prediction method based on variable mode decomposition as described above.

[0086] The computer program product provided in this application can solve the technical problem of how to improve the accuracy of battery RUL prediction. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the battery RUL prediction method based on variable mode decomposition provided in the above embodiment, which will not be repeated here.

[0087] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A battery RUL prediction method based on variable mode decomposition, characterized in that: The method comprises: Obtain battery capacity degradation data and battery capacity failure threshold; Using variable mode decomposition to smooth the battery capacity degradation data to obtain multiple modal components; Obtaining key characteristic data of battery capacity according to the plurality of modal components; The battery capacity prediction value is obtained by inputting the key characteristic data of the battery capacity into an optimized support vector regression model; When the predicted value of the battery capacity is less than the battery capacity failure threshold, the remaining service life of the battery is calculated.

2. The method according to claim 1, characterized in that Before the step of obtaining the predicted value of battery capacity by inputting the key characteristic data of battery capacity into the optimized support vector regression model, the method includes: Dividing the battery capacity key feature data into a training set and a test set; Obtain training set feature data and establish an initial support vector regression model; The initial support vector regression model is optimized according to the feature data of the training set using a particle swarm optimization algorithm to obtain an optimized support vector regression model.

3. The method according to claim 2, characterized in that Before the step of optimizing the initial support vector regression model according to the training set feature data using a particle swarm optimization algorithm to obtain an optimized support vector regression model, the method includes: Obtain particle filtering in the feature data of the training set; Initialize algorithm parameters and set category measurement thresholds; Dividing a particle population in the particle filter into a plurality of subgroups; Calculate the fitness value of each particle in each subgroup, and initialize the best position of each individual subgroup and the best position of the group; Update the fitness value of each subgroup individual, the best position of each subgroup individual and the best position of the group according to the functional relationship; Determine whether the type metric value exceeds the type metric threshold, if the particle type metric value is less than the type metric threshold, re-plan the particle population; Determine whether the maximum number of iterations has been reached. If so, output the model parameters. After the model parameters are input into the nonlinear fitting function, the parameter penalty factor and the kernel parameter for constructing the optimized support vector model are obtained.

4. The method according to claim 2, characterized in that The step of optimizing the initial support vector regression model according to the training set feature data using a particle swarm optimization algorithm to obtain an optimized support vector regression model comprises: Obtaining a particle swarm in the training set data information; Initializing the positions and velocities of particles in the particle swarm and calculating the fitness of the particles; Set the number of iterations and calculate the historical optimal value of the particle; By comparing the fitness of the particles in each iteration, a global optimal value of the particle swarm is obtained; According to the historical optimal value and the global optimal value, the position and speed of the particle are updated; Determine whether the position and speed of the particle meet a preset range, and if not, perform boundary condition processing on the particle; If the number of iterations is reached or the error is less than the preset error, the optimized support vector regression model is obtained.

5. The method according to claim 1, characterized in that The step of obtaining a predicted value of battery capacity by inputting the key characteristic data of battery capacity into an optimized support vector regression model comprises: Get the preset starting point cycle period and the selected cycle period; When the selected cycle period exceeds the cycle period of the preset starting point, the selected cycle period is substituted into the optimized support vector regression model to obtain a battery capacity prediction value.

6. The method according to claim 1, characterized in that The step of smoothing the battery capacity degradation data by using variable mode decomposition to obtain multiple modal components includes: Get modal decomposition parameters; According to the modal decomposition parameters, the number of modal components and the center frequencies of the modal components are obtained; The battery capacity degradation data is iteratively decomposed based on the number of modal components and the center frequency to extract a plurality of modal components.

7. The method according to claim 1, characterized in that The step of calculating the remaining battery life when the predicted battery capacity value is less than the battery capacity failure threshold value further includes: Get preset cycle and select cycle; When the battery capacity prediction value is greater than the battery capacity failure threshold, the selected cycle period is accumulated with the preset cycle as a new cycle period to obtain the battery capacity prediction value of the new cycle period, and so on, until the battery capacity prediction value of the new cycle period does not exceed the battery capacity failure threshold.

8. A battery RUL prediction device based on variable mode decomposition, characterized in that: The device comprises: An acquisition module, used to acquire battery capacity degradation data and battery capacity failure threshold; A processing module, used for smoothing the battery capacity degradation data by using variable mode decomposition to obtain multiple modal components; An extraction module, used for obtaining key characteristic data of battery capacity according to a plurality of said modal components; A calculation module, used for obtaining a predicted value of battery capacity by inputting the key characteristic data of battery capacity into an optimized support vector regression model; The result module is used to calculate the remaining service life of the battery when the battery capacity prediction value is less than the battery capacity failure threshold.

9. A battery RUL prediction device based on variable mode decomposition, characterized in that: The device includes: a memory, a processor, and a battery RUL prediction program based on variable mode decomposition stored in the memory and running on the processor, wherein the battery RUL prediction program based on variable mode decomposition is configured to implement the steps of the battery RUL prediction method based on variable mode decomposition as described in any one of claims 1-7.

10. A storage medium, characterized in that: The storage medium stores a battery RUL prediction program based on variable mode decomposition, and when the battery RUL prediction program based on variable mode decomposition is executed by the processor, the steps of the battery RUL prediction method based on variable mode decomposition as described in any one of claims 1 to 7 are implemented.

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