State of health prediction for lithium-ion batteries using multiple-view feature fusion and support vector regression ensemble

• Published in: International journal of machine learning and cybernetics Vol. 10; no. 9; pp. 2269 - 2282
• Main Authors: Ma, Chao, Zhai, Xu, Wang, Zhaopei, Tian, Mingguang, Yu, Qiusheng, Liu, Lei, Liu, Hao, Wang, Hao, Yang, Xibei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Complex Systems; Computational Intelligence; Control; Datasets; Design; Electronic systems; Engineering; Feature extraction; Lithium; Lithium-ion batteries; Machine learning; Mechatronics; Neural networks; Original Article; Pattern Recognition; Rechargeable batteries; Robotics; Support vector machines; Systems Biology; Lithium-ion batteries; Support vector regression; Multiple-view feature fusion; Ensemble learning; State of health; Sliding window
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-018-0865-y

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.