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首页> 外文期刊>International journal of machine learning and cybernetics >State of health prediction for lithium-ion batteries using multiple- view feature fusion and support vector regression ensemble
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State of health prediction for lithium-ion batteries using multiple- view feature fusion and support vector regression ensemble

机译：基于多视图特征融合和支持向量回归集成的锂离子电池健康状态预测

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摘要

Lithium-ion batteries have been widely used in many electronic systems. Accurately estimating the state of health (SOH) of a lithium-ion battery is important for ensuring its safety and reliability. Among the various kinds of methods for predicting the SOH of lithium-ion batteries, machine-learning-based methods are the most popular. However, two common critical problems in machine-learning-based methods are extracting discriminative features and effectively utilizing the extracted features. In this study, we focused on solving these two issues. First, a sliding-window-based feature extraction technology (SWBFE) was designed to effectively extract features from different views in the discharge process of lithium-ion batteries. Second, we developed a multiple-view feature fusion with a support vector regression (SVR) ensemble strategy (MVFF-ESVR) for enhancing the performance in fusing multiple extracted features. The basic idea of MVFF-ESVR is to transform the feature-level fusion problem into a decision-level fusion problem. More specifically, for each feature, an SVR was modeled on the corresponding training set, and the AdaBoost and Stacking algorithms were utilized to incorporate multiple trained SVRs for generating two ensemble SVR models. By combining SWBFE with MVFF-ESVR, we further implemented two predictors, namely, Ada-TargetSOH and Sta-TargetSOH, for robust prediction of lithium-ion battery SOH. To evaluate the efficacy of the proposed predictors, we applied Ada-TargetSOH and Sta-TargetSOH on three types of lithium-ion battery datasets. The experimental results have demonstrated that our predictors outperform other existing lithium-ion battery SOH predictors.
机译：锂离子电池已广泛用于许多电子系统中。准确估计锂离子电池的健康状态（SOH）对于确保其安全性和可靠性很重要。在各种预测锂离子电池SOH的方法中，基于机器学习的方法最为流行。但是，在基于机器学习的方法中，两个常见的关键问题是提取判别特征并有效利用提取的特征。在这项研究中，我们专注于解决这两个问题。首先，设计了基于滑动窗口的特征提取技术（SWBFE），以在锂离子电池放电过程中有效地从不同角度提取特征。其次，我们开发了带有支持向量回归（SVR）集成策略（MVFF-ESVR）的多视图特征融合，以增强融合多个提取特征的性能。 MVFF-ESVR的基本思想是将特征级融合问题转换为决策级融合问题。更具体地说，对于每个功能，均会在相应的训练集上对SVR进行建模，并利用AdaBoost和Stacking算法来合并多个训练后的SVR，以生成两个整体SVR模型。通过将SWBFE与MVFF-ESVR结合，我们进一步实现了两个预测器，即Ada-TargetSOH和Sta-TargetSOH，以对锂离子电池SOH进行可靠的预测。为了评估所提出的预测指标的功效，我们将Ada-TargetSOH和Sta-TargetSOH应用于三种类型的锂离子电池数据集。实验结果表明，我们的预测器性能优于其他现有的锂离子电池SOH预测器。

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    Ma Chao; Zhai Xu; Wang Zhaopei; Tian Mingguang; Yu Qiusheng; Liu Lei; Liu Hao; Wang Hao; Yang Xibei;

  • 作者单位

    State Grid Shandong Elect Power CO, Informat & Commun Branch, Jinan 250001, Shandong, Peoples R China;

    State Grid Shandong Elect Power CO, Informat & Commun Branch, Jinan 250001, Shandong, Peoples R China;

    State Grid Shandong Elect Power CO, Informat & Commun Branch, Jinan 250001, Shandong, Peoples R China;

    State Grid Shandong Elect Power CO, Informat & Commun Branch, Jinan 250001, Shandong, Peoples R China;

    State Grid Shandong Elect Power CO, Informat & Commun Branch, Jinan 250001, Shandong, Peoples R China;

    State Grid Shandong Elect Power CO, Informat & Commun Branch, Jinan 250001, Shandong, Peoples R China;

    NARI Grp Corp, State Grid Elect Power Res Inst, Nanjing 210003, Jiangsu, Peoples R China;

    State Grid Shandong Elect Power CO, Informat & Commun Branch, Jinan 250001, Shandong, Peoples R China;

    Jiangsu Univ Sci & Technol, Sch Comp, Zhenjiang 212003, Jiangsu, Peoples R China;

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  • 关键词

    State of health; Lithium-ion batteries; Sliding window; Multiple-view feature fusion; Ensemble learning; Support vector regression;

    机译：健康状况;锂离子电池;滑动窗口;多视图特征融合;集合学习;支持矢量回归;

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