A battery residual cycle life prediction method, device, medium and equipment

Abstract

The application belongs to the technical field of battery management, and provides a battery residual cycle life prediction method, device, medium and equipment, which comprises the following steps: based on battery state data at a plurality of historical moments, the battery state at the next moment is predicted through a neural network, then the SPM model calculates the voltage, current and internal resistance inside the battery according to the predicted SOC and SOH; if the voltage, current and internal resistance calculated by the SPM model are consistent with the neural network prediction, then the SPM model adjusts the physical and chemical parameters of the battery according to the battery state at the next moment predicted by the neural network; otherwise, the neural network is fine-tuned and trained, and then the battery state at the next moment is predicted again; and the residual cycle life of the battery is calculated based on the battery state at the next moment predicted by the neural network. The accuracy of residual cycle life prediction is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of battery management, and particularly relates to a battery remaining cycle life prediction method, device, medium and equipment. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] The remaining cycle life of the power battery refers to how many times of charge and discharge cycles the battery can experience without failure under the current battery health state.

[0004] Accurate prediction of the remaining cycle life (RUL) of the battery is crucial for battery management, maintenance and replacement. Existing methods usually require detailed understanding of the internal structure and material properties of the battery, are time-consuming, and are susceptible to uncertain factors such as temperature, working conditions, SOC, etc. However, at present, neural networks are mostly used for prediction of battery SOC and other data. This method is greatly affected by the pre-training data, and the error in the prediction process is uncontrollable, which easily causes the prediction accuracy of the remaining cycle life to decrease. SUMMARY

[0005] In order to solve the technical problems existing in the background art, the present application provides a battery remaining cycle life prediction method, device, medium and equipment, which realizes the monitoring of the battery state prediction error through the interaction of the neural network and the semi-physical model (SPM), and continuously optimizes the neural network and the SPM model, thereby guaranteeing the prediction accuracy of the model and improving the accuracy of the remaining cycle life prediction.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0007] The first aspect of the present application provides a battery remaining cycle life prediction method.

[0008] A battery remaining cycle life prediction method comprises:

[0009] Obtaining battery states at a plurality of historical time points, the battery states including voltage, current, SOC, SOH and internal resistance;

[0010] Based on battery status data from several historical moments, the neural network predicts the battery status at the next moment. The SPM model then calculates the battery's internal voltage, current, and internal resistance based on the predicted SOC and SOH. If the voltage, current, and internal resistance calculated by the SPM model are consistent with those predicted by the neural network, the SPM model adjusts the battery's physical and chemical parameters based on the battery status at the next moment predicted by the neural network. Otherwise, the voltage, current, and internal resistance calculated by the SPM model and the SOC and SOH predicted by the LSTM neural network are used as a set of training data. After fine-tuning the neural network, the battery status at the next moment is re-predicted.

[0011] The remaining cycle life of the battery is calculated based on the battery status at the next moment predicted by the neural network.

[0012] Furthermore, the SPM model calculates the voltage inside the battery according to the predicted SOC, and the specific steps include:

[0013] Based on the predicted SOC, the voltage is simulated through the corresponding relationship curve between voltage and SOC;

[0014] If the error between the simulated voltage and the voltage at the next moment predicted by the neural network exceeds the set value, the simulated voltage is used as the voltage output by the SPM model; otherwise, the voltage at the next moment predicted by the neural network is used as the voltage output by the SPM model.

[0015] Furthermore, the SPM model calculates the internal resistance of the battery based on the predicted SOH, and the specific steps include:

[0016] Calculate the ratio of the internal resistance at the next moment predicted by the neural network to the reference internal resistance to simulate the battery's SOH;

[0017] If the error between the simulated SOH and the SOH at the next moment predicted by the neural network exceeds the set value, the internal resistance of the battery is calculated based on the SOH at the next moment predicted by the neural network and the reference internal resistance, which is used as the internal resistance output by the SPM model; otherwise, the internal resistance at the next moment predicted by the neural network is used as the internal resistance output by the SPM model.

[0018] Furthermore, the voltage and internal resistance at the historical moment are obtained by extracting features from the high-frequency pulse current.

[0019] A second aspect of the present invention provides a device for predicting the remaining cycle life of a battery.

[0020] A battery remaining cycle life prediction device, comprising:

[0021] A data acquisition module is configured to: acquire battery status at several historical moments, wherein the battery status includes voltage, current, SOC, SOH, and internal resistance;

[0022] The interactive prediction module is configured to: predict the battery state at the next moment through a neural network based on battery state data at several historical moments, and then use the SPM model to calculate the voltage, current, and internal resistance of the battery based on the predicted SOC and SOH. If the voltage, current, and internal resistance calculated by the SPM model are consistent with those predicted by the neural network, the SPM model adjusts the battery physical and chemical parameters based on the battery state at the next moment predicted by the neural network. Otherwise, the voltage, current, and internal resistance calculated by the SPM model and the SOC and SOH predicted by the LSTM neural network are used as a set of training data, and the neural network is fine-tuned and trained to re-predict the battery state at the next moment.

[0023] The life calculation module is configured to calculate the remaining cycle life of the battery based on the battery status at the next moment predicted by the neural network.

[0024] Furthermore, the SPM model calculates the voltage inside the battery according to the predicted SOC, and the specific steps include:

[0025] Based on the predicted SOC, the voltage is simulated through the corresponding relationship curve between voltage and SOC;

[0026] If the error between the simulated voltage and the voltage at the next moment predicted by the neural network exceeds the set value, the simulated voltage is used as the voltage output by the SPM model; otherwise, the voltage at the next moment predicted by the neural network is used as the voltage output by the SPM model.

[0027] Furthermore, the SPM model calculates the internal resistance of the battery based on the predicted SOH, and the specific steps include:

[0028] Calculate the ratio of the internal resistance at the next moment predicted by the neural network to the reference internal resistance to simulate the battery's SOH;

[0029] If the error between the simulated SOH and the SOH at the next moment predicted by the neural network exceeds the set value, the internal resistance of the battery is calculated based on the SOH at the next moment predicted by the neural network and the reference internal resistance, which is used as the internal resistance output by the SPM model; otherwise, the internal resistance at the next moment predicted by the neural network is used as the internal resistance output by the SPM model.

[0030] Furthermore, the voltage and internal resistance at the historical moment are obtained by extracting features from the high-frequency pulse current.

[0031] A third aspect of the present invention provides a computer-readable storage medium.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a battery remaining cycle life prediction method as described in the first aspect above.

[0033] A fourth aspect of the present invention provides a computer device.

[0034] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for predicting the remaining cycle life of a battery as described in the first aspect above are implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] Through the interaction of the neural network and the SPM model, the present invention not only realizes the monitoring of the battery state prediction error, but also can continuously optimize the neural network and the SPM model, ensure the prediction accuracy of the model, and improve the accuracy of the remaining cycle life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 This is a flow chart of a method for predicting remaining cycle life of a battery, shown in Example 1 of the present invention;

[0039] Figure 2 Schematic diagram of the interaction between the neural network and the SPM model shown in the first embodiment of the present invention;

[0040] Figure 3 It is a structural diagram of a computer device shown in Example 4 of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0042] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0043] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0044] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the methods and devices according to various embodiments of the present invention. It should be noted that each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code can include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram, can be implemented using a dedicated hardware-based device that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0045] Example 1

[0046] This embodiment provides a method for predicting the remaining cycle life of a battery.

[0047] By combining the RUL estimation method of the Long Short-Term Memory (LSTM) network and the Semi-Physical Model (SPM), the accuracy and efficiency of the prediction can be improved. This method combines the advantages of data-driven and physical models, and improves the accuracy and robustness of the prediction by combining specific learning algorithms and physical models.

[0048] This embodiment provides a method for predicting the remaining cycle life of a battery. First, vehicle-side power battery data, such as voltage, current, SOC, SOH, high-frequency pulse current (HPPC), and other data, are collected and updated in real time into a big database. Next, the big database directly imports the multi-dimensional data into the cloud, which processes and predicts the data based on LSTM and SPM models.

[0049] This embodiment provides a method for predicting the remaining cycle life of a battery, including: obtaining battery status data (including voltage, current, SOC, SOH, and R) at several historical moments; predicting the battery status (voltage, current, SOC, SOH, and R) at the next moment through an LSTM neural network based on the battery status data at the several historical moments, and then using an SPM model to calculate the voltage, current, and internal resistance inside the battery based on the predicted SOC and SOH; if the voltage, current, and internal resistance calculated by the SPM model are consistent with those predicted by the LSTM neural network, the SPM model adjusts the battery physical and chemical parameters based on the battery status at the next moment predicted by the neural network; otherwise, using the voltage, current, and internal resistance calculated by the SPM model and the SOC and SOH predicted by the LSTM neural network as a set of training data, the LSTM neural network is fine-tuned and trained, and then the battery status at the next moment is re-predicted; and the battery remaining cycle life is calculated based on the battery status at the next moment predicted by the LSTM neural network.

[0050] Among them, the SPM model calculates the voltage inside the battery according to the predicted SOC. The specific steps include: based on the predicted SOC, simulating the voltage through the corresponding relationship curve between voltage and SOC; if the error between the simulated voltage and the voltage at the next moment predicted by the LSTM neural network exceeds the set value, the simulated voltage is used as the voltage output by the SPM model; otherwise, the voltage at the next moment predicted by the LSTM neural network is used as the voltage output by the SPM model.

[0051] Among them, the SPM model calculates the internal resistance of the battery based on the predicted SOH. The specific steps include: calculating the ratio of the internal resistance at the next moment predicted by the neural network to the reference internal resistance, and simulating the SOH of the battery; if the error between the simulated SOH and the SOH at the next moment predicted by the LSTM neural network exceeds the set value, then based on the SOH at the next moment predicted by the LSTM neural network and the reference internal resistance, calculate the internal resistance of the battery as the internal resistance output by the SPM model; otherwise, use the internal resistance at the next moment predicted by the LSTM neural network as the internal resistance output by the SPM model.

[0052] This embodiment provides a method for predicting the remaining cycle life of a battery, which is applied to a battery of a certain PHEV model: voltage range: 2.5V-4.2V, current range: -100A-100A, temperature range: -20℃-60℃, SOC range: 0%-100%, SOH range: 60%-100%.

[0053] This embodiment provides a method for predicting the remaining cycle life of a battery. Figure 1 As shown, the following steps are included:

[0054] Step 1: Data collection and preprocessing.

[0055] Step 101, real-time data collection: The battery management software in the vehicle-side controller collects battery data in real time, including voltage V, current I, SOC, SOH, high-frequency pulse current (HPPC), etc.

[0056] Step 102, HPPC data processing: Extract features from the high-frequency pulse current (HPPC) test data, including the internal resistance R of the battery cell, voltage response, etc. The specific steps are as follows:

[0057] Preprocess the HPPC data to filter out noise and outliers;

[0058] Calculate the internal resistance change of the battery cell under high-frequency pulses and extract the voltage response characteristics;

[0059] The processed HPPC features are used as one of the input data of the model.

[0060] Step 103: Anomaly detection and preprocessing: Apply anomaly detection algorithms to filter out abnormal data in real time. Z-value centering and normalization are performed on the data to speed up model training and optimization.

[0061] Step 2: Data transmission and storage.

[0062] Step 201: Edge computing nodes are deployed on the vehicle to perform preliminary data processing, reducing the transmission burden. The processed data is transmitted to the vehicle's T-box via the CAN network.

[0063] Step 202, cloud storage and processing: T-box transfers the data to a large database, extracts features from the data in the database, and directly imports it into the cloud model for processing.

[0064] Step 3: LSTM model and SPM training and optimization.

[0065] Step 301: Build a hybrid model architecture: Figure 2 As shown in the figure, LSTM is combined with the battery physical model (SPM), LSTM is used to process time series data, and the SPM model is used to simulate the physical and chemical behavior of the battery.

[0066] LSTM model: Based on time series input data such as voltage, current, SOC, SOH, and R, it predicts the battery state (voltage, current, SOC, SOH, and R) at the next moment. The LSTM input is [samples, timesteps, features], where samples represents the data volume, timesteps represents the time step, and features represents the feature dimension.

[0067] LSTM example parameters: Input data length: 100 time steps; Feature dimension: 5 (voltage, current, SOC, SOH, internal resistance); Number of LSTM layers: 3, 128 units per layer.

[0068] SPM model: The SPM model (Semi-Physical Model) combines the physical characteristics of the battery and a data-driven approach. It can simulate the battery's voltage response and capacity changes, and provide a mechanism analysis of battery aging.

[0069] SPM parameters: internal resistance: dynamic range from 10mΩ to 200mΩ; voltage-capacity curve: fitted by experimental data, based on different SOC points; electrode reaction kinetics: based on the Butler-Volmer equation.

[0070] Step 302: Coupling and interaction of LSTM and SPM models:

[0071] (1) Use the training set to train the LSTM and SPM models separately:

[0072] Initialize the LSTM model and SPM model parameters. The input of the LSTM model is the historical operating data of the battery (voltage, current, SOC, SOH, R, etc.), and the input of the SPM model is the physical and chemical characteristic parameters of the battery.

[0073] The LSTM model is trained using the training set data to extract time series features and update the model weights. The LSTM model can learn the behavioral characteristics of batteries under different operating conditions and provide time series prediction results.

[0074] LSTM model training details: Data is processed in batches with a batch size of 32; each training iteration is 100 rounds with a learning rate of 0.001; cross-validation is used to ensure the generalization ability of the model.

[0075] The same data was simulated using the SPM model to obtain the physical and chemical behavior characteristics of the battery. The SPM model can simulate the physical and chemical processes inside the battery in detail and provide the response characteristics of the battery under different aging conditions.

[0076] SPM model simulation details: the initial SOC is set to 50%, and the voltage changes under different charge and discharge rates are simulated; the internal resistance change range is set to 20mΩ to 150mΩ; the time step of each simulation is 1 second, and the total simulation time is 3600 seconds.

[0077] (2) The prediction results of the LSTM model are used as the input of the SPM model for secondary correction. Specifically, the battery state change results predicted by the LSTM model are input into the SPM model. The SPM model further simulates the physical and chemical behavior of the battery and corrects the error of the LSTM prediction. This step is specifically divided into the following sub-steps:

[0078] (101) The SOC and SOH results of the next moment predicted by the LSTM model are used as input and passed to the SPM model;

[0079] (102) The SPM model calculates the voltage change, current response, internal resistance, and corresponding capacity change inside the battery based on the SOC and SOH values ​​input by the LSTM;

[0080] (103) The SPM model feeds back the calculated physicochemical properties, such as voltage and current, to the LSTM model to correct the LSTM model’s predictions. Correction process details: Based on the voltage response calculated by the SPM, the SOC value predicted by the LSTM is adjusted, with a calculated error range of ±2%. Combined with the internal resistance changes simulated by the SPM, the SOH value predicted by the LSTM is corrected, with a calculated error range of ±1%.

[0081] The steps for adjusting the SOC value according to the voltage response are as follows:

[0082] a) Voltage response simulation: First, under the set initial conditions (such as SOC = 50%, different charge and discharge rates and different internal resistance changes), the SPM model is used to simulate the voltage response of the battery with a time step of 1 second and a total simulation time of 3600 seconds.

[0083] b) Establishing a Voltage-SOC Relationship: Through simulation, we can determine how the voltage response changes over time. Since the battery's open circuit voltage (OCV) is directly related to the SOC, a voltage-SOC curve can be established. Over time, changes in voltage reflect changes in the lithium ion concentration within the battery, which directly affects the SOC value.

[0084] c) Error calculation and SOC adjustment: The simulated voltage response is compared with the actual measured voltage (here, the voltage predicted by the LSTM neural network at the next moment). If the simulated voltage deviates from the actual measured voltage, this indicates that the current SOC estimate may be inaccurate. By minimizing the voltage error, the SOC value can be adjusted in the opposite direction. Specifically, if the simulated voltage is higher than the actual voltage, it may indicate that the SOC is overestimated and needs to be reduced; conversely, if the simulated voltage is lower than the actual voltage, it indicates that the SOC is underestimated and needs to be increased.

[0085] d) Dynamic Correction: The SOC is adjusted iteratively until the simulated voltage matches the actual measured voltage. This approach combines the battery’s physicochemical properties (simulated by the SPM model) with the actual voltage response to dynamically correct the SOC.

[0086] The SPM model primarily simulates the behavior of single particles within the battery, describing the diffusion and reaction processes of lithium ions in electrode particles. SPM assumes that each electrode is composed of countless uniform small particles, so it can predict the physicochemical behavior of the electrode through simplified mass and charge balance equations.

[0087] In the SPM model, the voltage response is obtained by calculating the polarization voltage, ohmic voltage drop, and the effect of solid phase diffusion of the battery. The specific calculation process is as follows:

[0088] Lithium ion diffusion in particles: SPM predicts the electrochemical reaction rate of the electrode by solving the changes in lithium ion concentration within the particles. The changes in lithium ion concentration affect the voltage response of the battery.

[0089] Polarization voltage: The electrochemical reaction rate is related to the electrode surface concentration. The polarization voltage can be calculated using the Butler-Volmer equation, reflecting the reaction kinetics inside the battery.

[0090] Ohmic voltage drop: Ohmic voltage drop is the resistance generated when the battery current passes through the electrodes and electrolyte. The internal resistance set in the simulation (20mΩ to 150mΩ) will affect the ohmic voltage drop.

[0091] Total voltage: The voltage response of the battery can be obtained by adding the ohmic voltage drop and polarization voltage to the open circuit voltage (OCV). The open circuit voltage is usually a function of SOC, so the OCV at different SOCs will be different.

[0092] The steps for adjusting SOH based on the internal resistance change calculated by SPM are as follows:

[0093] a) Simulation and monitoring of internal resistance:

[0094] The SPM model is used to simulate the battery, setting different aging states and cycle numbers, simulating the voltage response of the battery at different aging stages, and calculating the internal resistance of the battery.

[0095] The change in internal resistance can be calculated by comparing the voltage drop and charge and discharge current under different charge and discharge cycles. Generally, an increase in internal resistance will cause the battery's voltage response curve to become steeper and the discharge voltage to drop faster.

[0096] b) Relationship between internal resistance change and SOH:

[0097] Baseline internal resistance: For a new battery, the baseline value of internal resistance is known and is usually measured in its initial state. In a healthy battery, the internal resistance is low.

[0098] SOH estimation formula: SOH is usually characterized by the relative increase in internal resistance. A commonly used SOH estimation formula is:

[0099]

[0100] c) Dynamic adjustment of SOH:

[0101] Measuring internal resistance: When using SPM simulation, by comparing the changes in internal resistance during different charge and discharge cycles, the current internal resistance increment of the battery can be determined.

[0102] Calculate the battery's SOH based on the ratio of the current internal resistance (i.e., the internal resistance at the next moment predicted by the neural network) to the new battery baseline internal resistance. For example, if the internal resistance increases by 20%, the SOH will decrease to 80% of the initial value.

[0103] Adjustment under aging state: When the internal resistance increases to a certain threshold (for example, when the internal resistance exceeds twice the initial value), the SOH value will drop sharply, which means that the battery has aged seriously and measures such as reducing the charge and discharge rate or replacing the battery are needed.

[0104] Among them, parameter adjustment in the SPM model: In the SPM model, the effect of internal resistance change on SOH can be adjusted and simulated in the following ways:

[0105] Internal resistance setting range: Under simulation conditions, the internal resistance range is set from 20mΩ to 150mΩ. This range covers the internal resistance variation from new batteries to severely aged batteries. By simulating the battery voltage response under different internal resistance values, changes in SOH can be observed.

[0106] Response under different aging conditions: The SPM model can simulate the physical and chemical behavior of the battery in detail, so it can output different voltage response curves under different aging conditions to reflect the impact of increased internal resistance.

[0107] Dynamically adjust the step size: In a simulation with a time step of 1 second and a total time of 3600 seconds, the change in internal resistance can be calculated regularly and the SOH value can be updated.

[0108] (2) Feedback the physical and chemical characteristics output by the SPM model to the LSTM model to update the weights and biases of the LSTM model. Through this feedback mechanism, the LSTM model can more accurately learn the aging characteristics of the battery and improve the prediction accuracy. This step is specifically divided into the following sub-steps:

[0109] (201) The voltage, current, internal resistance, and other characteristic values output by the SPM model are used as input data for the new round of LSTM model training.

[0110] (202) The LSTM model re-trains its network weights and biases based on the input of the SPM model, making it more accurate in capturing the aging trend of the battery.

[0111] (203) The LSTM model continuously adjusts its parameters through multiple rounds of iterative optimization to achieve optimal prediction results.

[0112] (204) Process details: After updating the LSTM weights each time, perform 50 rounds of fine-tuning training with a learning rate of 0.0001; use mean squared error (MSE) as the loss function to ensure stable convergence of the model.

[0113] If the V, I, and R input into the SPM are corrected, return the corrected values to the LSTM; if not, return the original values.

[0114] Optimize the combined model using the Adam optimizer to reduce prediction errors. The parameters of the LSTM model and the SPM model are optimized together to ensure the accuracy and stability of the model. The specific steps are as follows:

[0115] Define the loss function to calculate the error between the LSTM model's prediction results and the actual observed values.

[0116] Use the Adam optimizer to adjust the learning rate and parameters of the LSTM model based on the loss function value; Adam optimizer parameter settings: initial learning rate: 0.001; β1 = 0.9, β2 = 0.999, ε = 1e-8.

[0117] Use the optimized LSTM model parameters as input to re-train the SPM model and adjust its parameters to reduce overall prediction errors.

[0118] SPM model details: Use the updated LSTM prediction values as initial conditions to re-simulate the SPM model. Compare the SPM model output with the actual observed values to adjust the battery's physical and chemical parameters and ensure simulation accuracy.

[0119] Evaluate the model's performance using test set data, adjust the model's parameters, and ensure the model's generalization ability. Validate the combined model's prediction accuracy and robustness by testing the test data. The specific steps are as follows:

[0120] Select data sets that did not participate in training as test sets to evaluate the combined model;

[0121] Calculate the prediction error on the test set to evaluate the accuracy and robustness of the model; prediction error evaluation indicators: mean square error (MSE): evaluates the difference between the predicted value and the true value; mean absolute error (MAE): evaluates the average absolute difference between the predicted value and the true value;

[0122] Based on the evaluation results, the model parameters and optimization algorithms are adjusted to ensure the stability and accuracy of the combined model under different working conditions.

[0123] Step 4: Algorithm optimization and parallel computing.

[0124] Algorithms and models can be improved based on the following aspects:

[0125] Parallel computing: Leverage the parallel processing capabilities of cloud computing to accelerate model training and inference and support large-scale data processing.

[0126] Adaptive model updating: Implement an adaptive learning mechanism to dynamically adjust model parameters based on newly collected data to improve the real-time adaptability of the model.

[0127] Edge learning: Partial model training is performed on the vehicle side to protect data privacy while reducing data transmission volume.

[0128] Data security: Implement data encryption and access control mechanisms to ensure the security of data transmission and storage between the vehicle and the cloud.

[0129] System redundancy: Build redundant systems and backup mechanisms to ensure the system can continue to operate stably in unexpected situations.

[0130] Step 5: System integration and application.

[0131] The present embodiment provides a method for predicting the remaining cycle life of a battery, which can be specifically applied in three aspects: battery health management system, predictive maintenance, and circular economy.

[0132] Battery Health Management System: Build a complete battery health management system, including data collection, processing, analysis, and visualization. Provide a user-friendly interface and establish a user feedback channel to collect battery usage experiences and issues. Provide personalized battery health reports, allowing users to view battery status and usage recommendations, improving user engagement and satisfaction.

[0133] Predictive maintenance: Based on RUL prediction results, predictive maintenance recommendations are provided to prevent battery failures and extend battery life.

[0134] Circular economy: Introduce battery recycling and reuse, and determine the secondary utilization value of recycled batteries through RUL assessment.

[0135] Through these detailed steps and improvements, the patent is not only more technically perfect, but also more operational in practical applications, providing a set of advanced and reliable solutions for battery management systems.

[0136] Example 2

[0137] This embodiment provides a device for predicting the remaining cycle life of a battery.

[0138] A battery remaining cycle life prediction device, comprising:

[0139] A data acquisition module is configured to: acquire battery status at several historical moments, wherein the battery status includes voltage, current, SOC, SOH, and internal resistance;

[0140] The interactive prediction module is configured to: predict the battery state at the next moment through a neural network based on battery state data at several historical moments, and then use the SPM model to calculate the voltage, current, and internal resistance of the battery based on the predicted SOC and SOH. If the voltage, current, and internal resistance calculated by the SPM model are consistent with those predicted by the neural network, the SPM model adjusts the battery physical and chemical parameters based on the battery state at the next moment predicted by the neural network. Otherwise, the voltage, current, and internal resistance calculated by the SPM model and the SOC and SOH predicted by the LSTM neural network are used as a set of training data, and the neural network is fine-tuned and trained to re-predict the battery state at the next moment.

[0141] The life calculation module is configured to calculate the remaining cycle life of the battery based on the battery status at the next moment predicted by the neural network.

[0142] The SPM model calculates the voltage inside the battery based on the predicted SOC. The specific steps include:

[0143] Based on the predicted SOC, the voltage is simulated through the corresponding relationship curve between voltage and SOC;

[0144] If the error between the simulated voltage and the voltage at the next moment predicted by the neural network exceeds the set value, the simulated voltage is used as the voltage output by the SPM model; otherwise, the voltage at the next moment predicted by the neural network is used as the voltage output by the SPM model.

[0145] The SPM model calculates the internal resistance of the battery based on the predicted SOH. The specific steps include:

[0146] Calculate the ratio of the internal resistance at the next moment predicted by the neural network to the reference internal resistance to simulate the battery's SOH;

[0147] If the error between the simulated SOH and the SOH predicted by the neural network for the next time exceeds a set value, then the internal resistance of the battery is calculated based on the SOH predicted by the neural network for the next time and the reference internal resistance, as the internal resistance output by the SPM model; otherwise, the internal resistance predicted by the neural network for the next time is taken as the internal resistance output by the SPM model.

[0148] The voltage and internal resistance at the historical time are obtained by extracting features in the high-frequency pulse current.

[0149] It should be noted that the above embodiment provides a battery remaining cycle life prediction device, which is only exemplified by the division of the above functional modules. In actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the electronic device is divided into different functional modules to complete all or part of the functions described above. In addition, the battery remaining cycle life calculation device and the battery remaining cycle life calculation method provided by the above embodiment belong to the same concept, and the specific implementation process is described in the method embodiment, which will not be repeated here.

[0150] Embodiment three

[0151] The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the steps in the battery remaining cycle life prediction method according to the above embodiment one.

[0152] Embodiment four

[0153] The embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes the steps in the battery remaining cycle life prediction method according to the above embodiment one when executing the program.

[0154] Figure 3 A structure schematic diagram of a computer device provided by an example embodiment of the present application is shown. The computer device includes a processor and a memory.

[0155] The processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field Programmable Gate Array), or PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0156] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory is used to store at least one computer program, which is used to be executed by the processor to implement the vehicle occupant detection method provided by the method embodiment of the present application.

[0157] Those skilled in the art will appreciate that the illustrated structure does not limit the computer device, and may include more or fewer components than illustrated, or combine certain components, or adopt a different component arrangement.

[0158] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for predicting the remaining cycle life of a battery, characterized in that: include: Obtaining battery status at several historical moments, where the battery status includes voltage, current, SOC, SOH, and internal resistance; Based on the battery status data at several historical moments, the neural network predicts the battery status at the next moment. The SPM model calculates the battery's internal voltage, current, and internal resistance based on the predicted SOC and SOH. If the voltage, current, and internal resistance calculated by the SPM model are consistent with those predicted by the neural network, the SPM model adjusts the battery's physical and chemical parameters based on the battery state at the next moment predicted by the neural network. Otherwise, the voltage, current, and internal resistance calculated by the SPM model and the SOC and SOH predicted by the LSTM neural network are used as a set of training data. After fine-tuning the neural network, the battery state at the next moment is re-predicted. The remaining cycle life of the battery is calculated based on the battery status at the next moment predicted by the neural network.

2. A method for predicting remaining cycle life of a battery according to claim 1, characterized in that: The SPM model calculates the voltage inside the battery based on the predicted SOC. The specific steps include: Based on the predicted SOC, the voltage is simulated through the corresponding relationship curve between voltage and SOC; If the error between the simulated voltage and the voltage at the next moment predicted by the neural network exceeds the set value, the simulated voltage is used as the voltage output by the SPM model; otherwise, the voltage at the next moment predicted by the neural network is used as the voltage output by the SPM model.

3. The method for predicting the remaining cycle life of a battery according to claim 1, wherein: The SPM model calculates the internal resistance of the battery based on the predicted SOH. The specific steps include: Calculate the ratio of the internal resistance at the next moment predicted by the neural network to the reference internal resistance to simulate the battery's SOH; If the error between the simulated SOH and the SOH at the next moment predicted by the neural network exceeds the set value, the internal resistance of the battery is calculated based on the SOH at the next moment predicted by the neural network and the reference internal resistance, which is used as the internal resistance output by the SPM model; otherwise, the internal resistance at the next moment predicted by the neural network is used as the internal resistance output by the SPM model.

4. The method for predicting the remaining cycle life of a battery according to claim 1, wherein: The voltage and internal resistance at the historical moment are obtained by extracting features from the high-frequency pulse current.

5. A battery remaining cycle life prediction device, characterized in that: include: A data acquisition module is configured to: acquire battery status at several historical moments, wherein the battery status includes voltage, current, SOC, SOH, and internal resistance; The interactive prediction module is configured to: predict the battery state at the next moment based on the battery state data at several historical moments through a neural network, and then use the SPM model to calculate the battery's internal voltage, current, and internal resistance based on the predicted SOC and SOH; If the voltage, current, and internal resistance calculated by the SPM model are consistent with those predicted by the neural network, the SPM model adjusts the battery's physical and chemical parameters based on the battery state at the next moment predicted by the neural network. Otherwise, the voltage, current, and internal resistance calculated by the SPM model and the SOC and SOH predicted by the LSTM neural network are used as a set of training data. After fine-tuning the neural network, the battery state at the next moment is re-predicted. The life calculation module is configured to calculate the remaining cycle life of the battery based on the battery status at the next moment predicted by the neural network.

6. A battery remaining cycle life prediction device according to claim 5, characterized in that: The SPM model calculates the voltage inside the battery based on the predicted SOC. The specific steps include: Based on the predicted SOC, the voltage is simulated through the corresponding relationship curve between voltage and SOC; If the error between the simulated voltage and the voltage at the next moment predicted by the neural network exceeds the set value, the simulated voltage is used as the voltage output by the SPM model; otherwise, the voltage at the next moment predicted by the neural network is used as the voltage output by the SPM model.

7. The battery remaining cycle life prediction device according to claim 5, characterized in that: The SPM model calculates the internal resistance of the battery based on the predicted SOH. The specific steps include: Calculate the ratio of the internal resistance at the next moment predicted by the neural network to the reference internal resistance to simulate the battery's SOH; If the error between the simulated SOH and the SOH at the next moment predicted by the neural network exceeds the set value, the internal resistance of the battery is calculated based on the SOH at the next moment predicted by the neural network and the reference internal resistance, which is used as the internal resistance output by the SPM model; otherwise, the internal resistance at the next moment predicted by the neural network is used as the internal resistance output by the SPM model.

8. The battery remaining cycle life prediction device according to claim 5, characterized in that: The voltage and internal resistance at the historical moment are obtained by extracting features from the high-frequency pulse current.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a method for predicting remaining cycle life of a battery as claimed in any one of claims 1 to 4 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for predicting the remaining cycle life of a battery as described in any one of claims 1 to 4 are implemented.

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