Work

Enhanced state of health estimation for lithium-ion batteries using statistical feature extraction and feature selection

Year: 2025

Type: article

Abstract: State of health (SOH) estimation remains a critical research focus in battery management systems, where feature extraction and selection play crucial roles in improving estimation accuracy. This study examines the effectiveness of statistical feature extraction from constant current-constant voltage (CC-CV) charging curves, focusing on voltage and current characteristics. A total of 50 health indicators (HIs) are derived, including several novel features introduced for the first time. Five fundamental machine learning models—including backpropagation neural networks (BPNN), regression trees (RT), and three types of linear regression (Ridge, Lasso, and Elastic Net)—are trained on these features, with hyperparameter optimization conducted via random search. The best-performing model, RT, is further refined through seven feature selection techniques. Experimental results demonstrate that selecting only the top five features using sequential feature selection (backward) (SFS_backward) and recursive feature elimination (RFE) significantly enhances performance. Compared to using all features, SFS_backward and RFE reduce root mean square error (RMSE) by 12.8 % and 12.5 %, respectively, while mean absolute error (MAE) decreases by 13.1 % and 15.2 %. The proposed methodology also outperforms conventional and deep learning approaches, achieving up to a 127.0 % reduction in RMSE and 113.3 % in MAE. These findings underscore the potential of statistical feature engineering and selection to enhance SOH estimation accuracy while reducing model complexity.

Source: International Journal of Electrochemical Science

DOI: 10.1016/j.ijoes.2025.101012

Authors: Shao-Ying Li, Yi-Hua Liu, Chun-Liang Liu, Guan-Jhu Chen, Shun-Chung Wang

Institutions: National Taiwan University of Science and Technology, National Yunlin University of Science and Technology, National Changhua University of Education, National Taiwan Ocean University

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Good health and well-being

Open Access status: gold

Estimated APC paid: $1,670