An Enhanced Method to Estimate State of Health of Li-Ion Batteries Using Feature Accretion Method (FAM)

Leila Amani (), Amir Sheikhahmadi () and Yavar Vafaee
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Leila Amani: Department of Computer Engineering, Faculty of Engineering, Sanandaj Branch Islamic Azad University (IAU), Sanandaj 6616935391, Iran
Amir Sheikhahmadi: Department of Computer Engineering, Faculty of Engineering, Sanandaj Branch Islamic Azad University (IAU), Sanandaj 6616935391, Iran
Yavar Vafaee: Department of Computer Engineering, Faculty of Engineering, Sanandaj Branch Islamic Azad University (IAU), Sanandaj 6616935391, Iran

Energies, 2025, vol. 18, issue 19, 1-27

Abstract: Accurate estimation of State of Health (SOH) is pivotal for managing the lifecycle of lithium-ion batteries (LIBs) and ensuring safe and reliable operation in electric vehicles (EVs) and energy storage systems. While feature fusion methods show promise for battery health assessment, they often suffer from suboptimal integration strategies and limited utilization of complementary health indicators (HIs). In this study, we propose a Feature Accretion Method (FAM) that systematically integrates four carefully selected health indicators–voltage profiles, incremental capacity (IC), and polynomial coefficients derived from IC–voltage and capacity–voltage curves—via a progressive three-phase pipeline. Unlike single-indicator baselines or naïve feature concatenation methods, FAM couples’ progressive accretion with tuned ensemble learners to maximize predictive fidelity. Comprehensive validation using Gaussian Process Regression (GPR) and Random Forest (RF) on the CALCE and Oxford datasets yields state-of-the-art accuracy: on CALCE, RMSE = 0.09%, MAE = 0.07%, and R 2 = 0.9999; on Oxford, RMSE = 0.33%, MAE = 0.24%, and R 2 = 0.9962. These results represent significant improvements over existing feature fusion approaches, with up to 87% reduction in RMSE compared to state-of-the-art methods. These results indicate a practical pathway to deployable SOH estimation in battery management systems (BMS) for EV and energy storage applications.

Keywords: artificial intelligence; battery health estimation; data mining; feature engineering; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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