期刊
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
卷 7, 期 2, 页码 382-398出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TTE.2020.3017090
关键词
Batteries; Estimation; Feature extraction; Computational modeling; Integrated circuit modeling; Aging; Mathematical model; Comprehensive comparison; feature extraction; feature selection; lithium-ion batteries; machine learning; state of health (SOH)
资金
- National Natural Science Foundation of China [51875054, U1864212]
- Graduate Scientific Research and Innovation Foundation of Chongqing, China [CYS20018]
- Chongqing Natural Science Foundation for Distinguished Young Scholars [cstc2019jcyjjq0010]
- Chongqing Science and Technology Bureau, China
This article conducts a comprehensive study on data-driven State of Health (SOH) estimation methods for lithium-ion batteries. A new classification for health indicators is proposed, and a combination of fusion-based selection method and Gaussian process regression (GPR) shows superior estimation performance.
State of health (SOH) is a key parameter to assess lithium-ion battery feasibility for secondary usage applications. SOH estimation based on machine learning has attracted great attention in recent years and holds potentials for battery informatization and cloud battery management techniques. In this article, a comprehensive study of the data-driven SOH estimation methods is conducted. A new classification for health indicators (HIs) is proposed where the HIs are divided into the measured variables and calculated variables. To illustrate the significance of data preprocessing, four noise reduction methods are assessed in the HIs extraction process; different feature selection methods, including filter-based method, wrapper-based method, and fusion-based method, are applied to select HIs subsets. The four widely used machine learning algorithms, including artificial neural network, support vector machine, relevance vector machine, and Gaussian process regression (GPR), are applied and compared. In order to evaluate the estimation performance in potential real usages under future big data era, the three HIs selection methods and four machine learning methods are evaluated using three public data sets and two estimation strategies. The results show that the combination of the fusion-based selection method and GPR has an overall superior estimation performance in terms of both accuracy and computational efficiency.
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