Incremental capacity curve health-indicator extraction based on gaussian filter and improved relevance vector machine for lithium–ion battery remaining useful life estimation.

Fan, Yongcun; Qiu, Jingsong; Wang, Shunli; Yang, Xiao; Liu, Donglei; Fernandez, Carlos

Abstract

Accurate prediction of the remaining useful life (RUL) of lithium–ion batteries is the focus of lithium–ion battery health management. To achieve high–precision RUL estimation of lithium–ion batteries, a novel RUL prediction model is proposed by combining the extraction of health indicators based on incremental capacity curve (IC) and the method of improved adaptive relevance vector machine (RVM). First, the IC curve is extracted based on the charging current and voltage data. To attenuate the noise effects on the IC curve, Gaussian filtering is used and the optimal filtering window is determined to remove the noise interference. Based on this, the peak characteristics of the IC curve are analyzed and four groups of health indicators are extracted, and the strong correlation between health indicators and capacity degradation is determined using Pearson correlation analysis. Then, to optimize the traditional fixed kernel parameter RVM model, an RVM regression model whose kernel parameters are optimized by the Bayesian algorithm is established. Finally, four sets of datasets under CS2 battery in the public dataset of the University of Maryland are carried out for experimental validation. The validation results show that the improved RVM model has better short–term prediction performance and long–term prediction stability, the RUL prediction error is less than 20 cycles, and the mean absolute error is less than 0.02. The performance of the improved RVM model is better than that of the traditional RVM model.

Citation

FAN, Y., QIU, J., WANG, S., YANG, X., LIU, D. and FERNANDEZ, C. 2022. Incremental capacity curve health-indicator extraction based on gaussian filter and improved relevance vector machine for lithium–ion battery remaining useful life estimation. Metals [online], 12(8), article 1331. Available from: https://doi.org/10.3390/met12081331

• Journal Article Type: Article
• Acceptance Date: Aug 4, 2022
• Online Publication Date: Aug 9, 2022
• Publication Date: Aug 31, 2022
• Deposit Date: Sep 8, 2022
• Publicly Available Date: Sep 8, 2022
• Journal: Metals
• Electronic ISSN: 2075-4701
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 8
• Article Number: 1331
• DOI: https://doi.org/10.3390/met12081331
• Keywords: Lithium–ion battery; Incremental capacity curve; Gaussian filtering; Adaptive kernel function; Remaining useful life estimation
• Public URL: https://rgu-repository.worktribe.com/output/1745113
• Additional Information: The data that support the findings of this study are available in CALCE Engineering Center of University of Maryland at https://calce.umd.edu/data. These data were derived from the following resources available in the public domain: CS2 Battery Data Set.