State of health estimation for lithium-ion batteries based on optimal feature subset algorithm

Jing Sun and Haitao Wang

Energy, 2025, vol. 322, issue C

Abstract: Currently, most of data-driven lithium-ion battery capacity estimation studies use feature screening by analyzing the level of linear association between features and capacity, leading to the neglect of the hidden non-linear relationship between features and capacity. To overcome this limitation, this study constructs a feature selection evaluation function COMAN, which comprehensively describes the dependency between features and capacity originating in linear and nonlinear aspects. Based on it, the paper further presents an Optimal Feature Subset Algorithm (OFSA), which selects the optimal subset of features that are significantly associated with capacity and have low redundancy with the features. First, 26 features are extracted grounded in various parameters, including voltage, current, temperature, and time. Next, the proposed OFSA is applied to these features to select the optimal feature combination. Finally, CatBoost model was employed to build the connections between the selected features and capacity, with automated hyperparameter tuning performed using the Optuna library. Comparing with existing machine learning algorithms, the root mean square error (RMSE) and mean absolute error (MAE) of the method do not exceed 0.5 %, and the R2 value exceeds 0.9887. The findings from experiments indicate that the proposed method enhances the robustness of battery's SOH.

Keywords: Lithium-ion batteries; State-of-Health; Multi-feature; Optimal feature subset algorithm; CatBoost (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225013271
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:322:y:2025:i:c:s0360544225013271

DOI: 10.1016/j.energy.2025.135685

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().