A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries
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影响因子：9.794
DOI码：10.1016/j.jpowsour.2021.230774
发表刊物：Journal of Power Sources
关键字：State-of-health Lithium-ion batteries Random forest model Multi-feature Multi-model fusion
摘要：Battery state-of-health (SOH) estimation is a critical concern of the battery management system, which significantly affects the safe and stable operation of electric vehicles. The existing SOH estimation methods mainly focus on a single model with single-source features. Hence, the generalizability of these methods is limited. In this paper, a multi-feature-based multi-model fusion method is proposed for the SOH estimation of lithium-ion batteries. Firstly, the key factors of the battery aging process are analyzed from multiple sources such as voltage, temperature, and incremental capacity curves. Seven health factors are extracted as first-level input. Secondly, the preliminary SOH predictions are generated by using multiple linear regression, support vector regression, and Gaussian process regression models, respectively. Finally, a random forest model is used to fuse the preliminary SOH predictions. To improve the model prediction accuracy, the proposed model extracts features from different sources to fully describe the battery aging process. Inspired by the advantages of multi-model fusion, the random forest regressor method is applied for fusing the multi-model. To verify the effectiveness of the proposed model, comparative experiments are carried on the Oxford battery degradation dataset. Comparing with single feature or single model estimation methods, the results demonstrate the proposed method has better accuracy and stronger robustness in SOH estimation.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：518
页面范围：230774
是否译文：否
发表时间：2021-11-17
收录刊物：SCI、EI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0378775321012660