WO2022257211A1 - Procédé et système de prédiction de la durée de vie restante d'une batterie au lithium - Google Patents

Procédé et système de prédiction de la durée de vie restante d'une batterie au lithium Download PDF

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WO2022257211A1
WO2022257211A1 PCT/CN2021/104784 CN2021104784W WO2022257211A1 WO 2022257211 A1 WO2022257211 A1 WO 2022257211A1 CN 2021104784 W CN2021104784 W CN 2021104784W WO 2022257211 A1 WO2022257211 A1 WO 2022257211A1
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matrix
feature
lithium battery
capacity value
voltage
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Chinese (zh)
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宋艳
崔明
李沂滨
贾磊
高辉
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山东大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing

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  • the invention belongs to the technical field of lithium batteries, in particular to a method and system for predicting the remaining service life of lithium batteries.
  • Lithium batteries are widely used in electric vehicles, aerospace, mobile devices and other fields due to their stability, safety and environmental friendliness. Although the repeated charging and discharging of lithium-ion batteries brings convenience to the operation of the equipment, as the charging and discharging cycles of lithium-ion batteries increase, their capacity will decrease and their safety will also deteriorate. If the lithium battery is not replaced before the capacity decays to a certain extent, it will have an unpredictable impact on the equipment and even cause a safety accident. Therefore, it is necessary to predict the remaining service life of lithium batteries.
  • CNN convolutional neural network
  • RNN recurrent neural network
  • CNN treats each charging and discharging data as an independent feature vector, ignoring the key information in the feature.
  • RNN can utilize time series information, but it often brings the problem of exploding or disappearing gradients.
  • Long short-term memory unit (LSTM) is an optimization of RNN that can solve the above problems.
  • the LSTM-based method usually uses the previous prediction value as the feature data of the next prediction. Due to the accumulation of prediction errors in this iterative method, the later battery capacity prediction will become more and more inaccurate.
  • the present invention proposes a method and system for predicting the remaining service life of lithium batteries. Specifically, this method forms each measurement data and each feature into a feature matrix, weights the features through a mixed attention mechanism, and focuses on features that are beneficial to high prediction accuracy. At the same time, the present invention only uses the measurement data as the characteristics of the lithium battery, which eliminates the influence of the prediction accumulation error.
  • a first aspect of the present invention provides a method for predicting the remaining service life of a lithium battery.
  • a method for predicting the remaining service life of a lithium battery comprising:
  • the characteristic matrix of voltage, current and temperature data corresponding to each battery capacity value is obtained;
  • the measurement data feature matrix and feature matrix corresponding to different attention mechanisms are obtained.
  • type matrix and measurement data and feature type fusion matrix; the measurement data feature matrix, feature type matrix and measurement data and feature type fusion matrix are spliced to obtain the predicted remaining life of the lithium battery.
  • the historical battery capacity value of the lithium battery after obtaining the historical battery capacity value of the lithium battery, it includes obtaining the voltage, current and temperature data corresponding to each battery capacity value; and preprocessing the voltage, current and temperature data corresponding to each battery capacity value, extracting A characteristic matrix of voltage, current, and temperature data corresponding to battery capacity values.
  • the preprocessing includes: obtaining the row vectors of the voltage data matrix, current data matrix, and temperature data matrix corresponding to each battery capacity value, and then extracting the energy in the voltage row vector, current row vector, and temperature data row vector respectively features, volatility index features, skewness index features, and kurtosis index features.
  • linear regression features energy features, volatility index features, skewness index features, and kurtosis index features in the voltage line vector, current line vector, and temperature data line vector are used to predict the remaining life of the lithium battery .
  • the preprocessing includes: normalizing the voltage, current and temperature data corresponding to each battery capacity value.
  • acquiring the historical battery capacity value of the lithium battery includes, using a Gaussian filter to filter the historical battery capacity value of the lithium battery to filter out noise.
  • the influence of different characteristic types on the battery capacity value includes: the influence of the weight of different characteristic types on the characteristic matrix of the voltage, current and temperature data corresponding to the capacity value.
  • a second aspect of the present invention provides a lithium battery remaining service life prediction system.
  • a lithium battery remaining service life prediction system comprising:
  • the first processing module is configured to: obtain a characteristic matrix of voltage, current and temperature data corresponding to each battery capacity value according to the historical battery capacity value of the lithium battery;
  • the second processing module is configured to: consider the weight value of the voltage, current and temperature data and the impact of different feature types on the battery capacity value, and combine the feature matrix of the voltage, current and temperature data corresponding to the battery capacity value to obtain different attention
  • the measurement data feature matrix, feature type matrix, and measurement data and feature type fusion matrix corresponding to the force mechanism; the measurement data feature matrix, feature type matrix, and measurement data and feature type fusion matrix are spliced to obtain the predicted remaining life of the lithium battery.
  • a third aspect of the present invention provides a computer readable storage medium.
  • a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, it realizes the steps in the method for predicting the remaining service life of a lithium battery as described in the first aspect above.
  • a fourth aspect of the present invention provides a computer device.
  • a computer device comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the program, the remaining service life of the lithium battery as described in the first aspect above is realized The steps in the prediction method.
  • the present invention forms a feature matrix for each measurement data and each feature, weights the features through a mixed attention mechanism, and focuses on high prediction accuracy The characteristics of favorable rate, while reducing and calculating, improve the prediction accuracy of the remaining service life of lithium batteries. At the same time, only the measured data are used as the characteristics of the lithium battery, which eliminates the influence of the cumulative error in prediction.
  • the method proposed by the invention is tested on the NASA lithium battery data set, and the experiment shows that the method can increase the prediction accuracy rate by 44.4%.
  • Fig. 1 is the flow chart of the lithium battery remaining service life prediction method of the present invention
  • Fig. 2 (a) is the capacity graph of general battery
  • Figure 2(b) is the capacity curve of a general battery after a Gaussian filter
  • Fig. 3 is when the present embodiment only adopts energy index as feature type, the contrast figure of prediction curve and target curve;
  • Fig. 4 is a comparison diagram between the forecast curve and the target curve when only the energy index and the volatility index are used as the feature types in this embodiment;
  • Fig. 5 is a comparison diagram between the forecast curve and the target curve when only energy index, volatility index, and skewness index are used as feature types in this embodiment;
  • Fig. 6 is a comparison diagram between the forecast curve and the target curve when only energy index, volatility index, skewness index, and kurtosis index are used as feature types in this embodiment;
  • FIG. 7 is a comparison diagram between the prediction curve and the target curve when the present embodiment only uses the attention mechanism for each physical parameter
  • FIG. 8 is a comparison diagram between the prediction curve and the target curve when only the attention mechanism for each feature is used in this embodiment
  • FIG. 9 is a comparison diagram between the prediction curve and the target curve when only the attention mechanism for each data point is used in this embodiment.
  • FIG. 10 is a comparison diagram between the prediction curve and the target curve when only the attention mechanism for each physical parameter and the attention mechanism for each feature are used in this embodiment;
  • FIG. 11 is a comparison diagram between the prediction curve and the target curve when the present embodiment only adopts the attention mechanism for each physical parameter and the attention mechanism for each data point;
  • FIG. 12 is a comparison diagram between the prediction curve and the target curve when only the attention mechanism for each feature and the attention mechanism for each data point are used in this embodiment;
  • FIG. 13 is a comparison diagram of the prediction curve and the target curve when the attention mechanism for each physical parameter, the attention mechanism for each feature, and the attention mechanism for each data point are used in this embodiment.
  • Fig. 14 is a schematic diagram of linear regression feature calculation in this embodiment.
  • each block in a flowchart or a block diagram may represent a module, a program segment, or a part of a code
  • the module, a program segment, or a part of a code may include one or more An executable instruction for a specified logical function.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block in the flowchart and/or block diagrams, and combinations of blocks in the flowchart and/or block diagrams can be implemented using a dedicated hardware-based system that performs the specified functions or operations , or can be implemented using a combination of dedicated hardware and computer instructions.
  • this embodiment provides a method for predicting the remaining service life of a lithium battery.
  • This embodiment uses this method to be applied to a server as an example. It can be understood that this method can also be applied to a terminal, or It includes terminals, servers and systems, and is realized through the interaction between terminals and servers.
  • the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or it can provide cloud services, cloud database, cloud computing, cloud function, cloud storage, network server, cloud communication, intermediate Cloud servers for basic cloud computing services such as software services, domain name services, security service CDN, and big data and artificial intelligence platforms.
  • the terminal may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto.
  • the terminal and the server may be connected directly or indirectly through wired or wireless communication, which is not limited in this application.
  • the method includes the following steps:
  • the characteristic matrix of voltage, current and temperature data corresponding to each battery capacity value is obtained;
  • the measurement data feature matrix and feature matrix corresponding to different attention mechanisms are obtained.
  • type matrix and measurement data and feature type fusion matrix; the measurement data feature matrix, feature type matrix and measurement data and feature type fusion matrix are spliced to obtain the predicted remaining life of the lithium battery.
  • the battery capacity of the battery is used as the target output of the RUL prediction, and the data sequence of voltage, current and temperature corresponding to each capacity value is used as the input feature.
  • the method mainly includes three parts: 1. Data preprocessing and feature extraction; 2. Using the mixed attention mechanism to calculate the contribution value of different attribute features; 3. Using the trained network for battery RUL prediction. The implementation process of each part will be introduced in detail below.
  • the data set needs to be preprocessed. Assuming that the capacitance value of the jth measurement is y j , the corresponding voltage, current, and temperature sequence data are respectively.
  • This method saves the data features as a two-dimensional matrix, and the row vector of the matrix is where k ⁇ ⁇ v,c,tp ⁇ .
  • all the All are divided into 10 equal parts, and the mean value of each part is calculated as arrive value. and different meaning, is the original measurement data, is transformed
  • This preprocessing method can ensure that the lengths of feature data corresponding to different capacitance values are consistent.
  • feature and feature The linear fitting factor of . feature respectively The energy (Eg), volatility index (FI), skewness index (SI), and kurtosis index (KI) characteristics of , the specific acquisition methods are as follows:
  • Figure 2(a) shows the capacity curve of a general battery. It can be seen from this that the raw capacity data has volatility, which may be caused by measurement errors, electromagnetic interference, complex chemicals, etc. Therefore, the raw volumetric data is processed using a Gaussian filter. The filtered results of the processed capacity curve are shown in Fig. 2(b). For statistical scaling, the target curve needs to be normalized to
  • a modified self-attention mechanism is used to calculate the contribution weights by considering different attributes of features.
  • the characteristic matrix of the jth cycle be j denotes the jth measurement.
  • the row data in are feature vectors about different measurements (voltage, current, and temperature), while the data in different columns represent different kinds of features. Since the eigenmatrix is calculated separately Each row vector, each column vector and the contribution value of each feature need to use different attention mechanisms, so the method proposed in this paper is called lithium battery RUL prediction based on hybrid attention mechanism. Specifically, the following formula is used to calculate the weight of different measurements (or row data)
  • the weighted features C j , D j and E j will be concatenated and then input to the fully connected layer. Eventually, the network will output the predicted RUL of the lithium battery.
  • the fully connected layer is used to linearly transform the features. If the feature input of the fully connected layer is x, the output is y, and the parameter matrix is W, then the fully connected layer realizes the following functions:
  • the lithium battery data set used in this embodiment comes from the Prognostics Center of Excellence of National Aeronautics and Space Administration (NASA), which is a widely used data set in lithium battery RUL prediction.
  • NSA National Aeronautics and Space Administration
  • This data set records the multi-physical parameter data of the content degradation of multiple lithium batteries during use. The invention will be verified on #5, #6, #7 and #18 batteries.
  • RMSE Root Mean Square Error
  • MAPE Mean Absolute Percentage Error
  • Liu et al. Liu, Kailong, et al. "A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-Ion Battery.” IEEE Transactions on Industrial Electronics, vol.68, no .4, 2021, pp.3170–3180.
  • Chen et al. Chen, Liaogehao et al. "Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation.” Neurocomputing, vol.414, 2020 , pp.245-254.
  • Table 2 shows the RMSE and MAPE of the method of this example and other methods on different batteries, which shows that the method of this example performs better than other methods on #5, #7 and #18 batteries.
  • This embodiment provides a method for predicting the remaining service life of a lithium battery.
  • a method for predicting the remaining service life of a lithium battery comprising:
  • the characteristic matrix of voltage, current and temperature data corresponding to each battery capacity value is obtained;
  • the measurement data feature matrix and feature matrix corresponding to different attention mechanisms are obtained.
  • type matrix and measurement data and feature type fusion matrix; the measurement data feature matrix, feature type matrix and measurement data and feature type fusion matrix are spliced to obtain the predicted remaining life of the lithium battery.
  • the historical battery capacity value of the lithium battery after obtaining the historical battery capacity value of the lithium battery, it includes obtaining the voltage, current and temperature data corresponding to each battery capacity value; and the voltage, current and temperature data corresponding to each battery capacity value The data is preprocessed to extract the characteristic matrix of the voltage, current and temperature data corresponding to the battery capacity value.
  • Preprocessing includes: obtaining the row vectors of the voltage data matrix, current data matrix, and temperature data matrix corresponding to each battery capacity value, and then extracting the energy features and fluctuation index features in the voltage row vectors, current row vectors, and temperature data row vectors respectively , skewness index feature and kurtosis index feature.
  • This dataset contains various monitoring data during charging and discharging, such as measured voltage, current and temperature data.
  • LR linear regression
  • Eg energy index
  • FI volatility index
  • SI skewness index
  • KI kurtosis index
  • linear regression refers to: if the abscissa represents the feature number i, and the ordinate represents the observed data (such as current, voltage or temperature data), then the relationship between i and the observed data can be fitted by a straight line, as shown in Figure 14 straight line in .
  • the slope and intercept of the line are two linear regression features.
  • This embodiment provides a lithium battery remaining service life prediction system.
  • the first processing module is configured to: obtain a characteristic matrix of voltage, current and temperature data corresponding to each battery capacity value according to the historical battery capacity value of the lithium battery;
  • the second processing module is configured to: consider the weight value of the voltage, current and temperature data and the impact of different feature types on the battery capacity value, and combine the feature matrix of the voltage, current and temperature data corresponding to the battery capacity value to obtain different attention
  • the measurement data feature matrix, feature type matrix, and measurement data and feature type fusion matrix corresponding to the force mechanism; the measurement data feature matrix, feature type matrix, and measurement data and feature type fusion matrix are spliced to obtain the predicted remaining life of the lithium battery.
  • This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps in the method for predicting the remaining service life of a lithium battery as described in Embodiment 1 or Embodiment 2 above are implemented .
  • This embodiment provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor.
  • the processor executes the program, the above-mentioned embodiment 1 or embodiment 2 is implemented.
  • the steps in the method for predicting the remaining service life of the lithium battery are implemented.
  • the embodiments of the present invention may be provided as methods, systems, or computer program products. Accordingly, the present invention can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) having computer-usable program code embodied therein.
  • a computer-usable storage media including but not limited to disk storage and optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
  • the device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM) or a random access memory (Random AccessMemory, RAM), etc.

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Abstract

La présente invention concerne le domaine technique des batteries au lithium, et concerne un procédé et un système de prédiction de la durée de vie restante d'une batterie au lithium. Le procédé consiste : selon une valeur de capacité de batterie historique d'une batterie au lithium, à obtenir une matrice de caractéristiques de données de tension, de courant et de température correspondant à chaque valeur de capacité de batterie ; à prendre en compte des valeurs de poids des données de tension, de courant et de température et l'effet de différents types de caractéristiques sur les valeurs de capacité de la batterie, à combiner des matrices de caractéristiques des données de tension, de courant et de température correspondant aux valeurs de capacité de batterie, et à obtenir une matrice de caractéristiques de données de mesure, une matrice de type de caractéristiques et une matrice de fusion de données de mesure et de type de caractéristiques correspondant à différents mécanismes d'attention ; et à épisser la matrice de caractéristiques de données de mesure, la matrice de type de caractéristiques et la matrice de fusion de données de mesure et de type de caractéristiques pour obtenir la durée de vie restante prédite de la batterie au lithium.
PCT/CN2021/104784 2021-06-08 2021-07-06 Procédé et système de prédiction de la durée de vie restante d'une batterie au lithium WO2022257211A1 (fr)

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116449223A (zh) * 2023-06-20 2023-07-18 苏州精控能源科技有限公司 一种基于压缩感知的储能电池容量预测方法、装置
CN116494816A (zh) * 2023-06-30 2023-07-28 江西驴宝宝通卡科技有限公司 充电桩的充电管理系统及其方法
CN116738932A (zh) * 2023-08-16 2023-09-12 杭州程单能源科技有限公司 锂电池梯次利用的电芯压差优化方法及装置
CN117148165A (zh) * 2023-09-15 2023-12-01 东莞市言科新能源有限公司 聚合物锂离子电池的测试分析方法及其系统
CN117269766A (zh) * 2023-08-28 2023-12-22 广东工业大学 一种面向不平衡使用场景的电池soh预测方法
CN117368777A (zh) * 2023-10-17 2024-01-09 昆明理工大学 基于小样本学习算法的锂离子电池寿命预测方法和系统
CN117554846A (zh) * 2024-01-12 2024-02-13 烟台海博电气设备有限公司 计及约束条件的锂电池寿命预测方法及系统

Families Citing this family (2)

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Publication number Priority date Publication date Assignee Title
CN113985294B (zh) * 2021-12-29 2022-04-01 山东大学 一种电池剩余寿命的预估方法及装置
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190086478A1 (en) * 2017-09-20 2019-03-21 Samsung Electronics Co., Ltd. Apparatus and method for estimating state of battery
CN111103544A (zh) * 2019-12-26 2020-05-05 江苏大学 基于长短时记忆lstm和粒子滤波pf的锂离子电池剩余使用寿命预测方法
CN111243682A (zh) * 2020-01-10 2020-06-05 京东方科技集团股份有限公司 药物的毒性预测方法及装置、介质和设备
CN112666480A (zh) * 2020-12-02 2021-04-16 西安交通大学 一种基于充电过程特征注意力的电池寿命预测方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105224849B (zh) * 2015-10-20 2019-01-01 广州广电运通金融电子股份有限公司 一种多生物特征融合身份鉴别方法以及装置
US20220367053A1 (en) * 2019-09-27 2022-11-17 The Brigham And Women's Hospital, Inc. Multimodal fusion for diagnosis, prognosis, and therapeutic response prediction
CN112147432A (zh) * 2020-08-25 2020-12-29 国网上海市电力公司 基于注意力机制的BiLSTM模块、变压器状态诊断方法和系统

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190086478A1 (en) * 2017-09-20 2019-03-21 Samsung Electronics Co., Ltd. Apparatus and method for estimating state of battery
CN111103544A (zh) * 2019-12-26 2020-05-05 江苏大学 基于长短时记忆lstm和粒子滤波pf的锂离子电池剩余使用寿命预测方法
CN111243682A (zh) * 2020-01-10 2020-06-05 京东方科技集团股份有限公司 药物的毒性预测方法及装置、介质和设备
CN112666480A (zh) * 2020-12-02 2021-04-16 西安交通大学 一种基于充电过程特征注意力的电池寿命预测方法

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116449223A (zh) * 2023-06-20 2023-07-18 苏州精控能源科技有限公司 一种基于压缩感知的储能电池容量预测方法、装置
CN116449223B (zh) * 2023-06-20 2023-08-29 苏州精控能源科技有限公司 一种基于压缩感知的储能电池容量预测方法、装置
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