What question did this study set out to answer?

The aim is to develop a robust feature engineering strategy for accurate SOH estimation of lithium-ion batteries using explainable AI methods.

March 12, 2026Open Access

An explainable artificial intelligence-based feature engineering strategy for lightweight battery health estimation

Key Result

Hybrid-feature fusion using voltage-related and derived features led to improved accuracy and robustness in Lithium-ion battery SOH estimation with three-minute data.

Key Points

The aim is to develop a robust feature engineering strategy for accurate SOH estimation of lithium-ion batteries using explainable AI methods.
Integrated framework combining feature engineering with explainability analysis
Comparison of various feature groups during battery charging and discharging
Utilization of multiple machine learning models and SHAP for insights
Accurate SOH estimation achieved using short-time data from lithium-ion batteries
Hybrid-feature fusion consistently outperformed single-feature groups
External validation confirmed robust and low error SOH estimates across multiple cells

Structured PICO

Population

Lithium-ion (Li-ion) batteries

Intervention

Explainable artificial intelligence-based feature engineering strategy (hybrid-feature fusion)

Comparator

Single-feature groups and complex models without systematic feature engineering

Outcome

State-of-Health (SOH) estimation accuracy

Carefully designed and explainable feature engineering strategies are more important than model complexity for short-time battery State-of-Health estimation.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Accurate and efficient Lithium-ion (Li-ion) battery State-of-Health (SOH) estimation is essential for reliable energy storage management. Current data-driven approaches often prioritize model complexity over systematic and explainable feature engineering, which may limit their robustness and transparency in practical applications. We propose an integrated framework that combines systematic feature engineering with explainability analysis to identify robust feature engineering strategies for lightweight data-driven SOH estimation using multiple machine learning models and SHapley Additive exPlanations (SHAP). Our results and findings show that accurate battery SOH estimation can be achieved using short-time data by adopting explainable feature engineering strategies. Hybrid-feature fusion that integrates raw measurements with derived features and their smoothed counterparts consistently outperforms single-feature groups across both charging and discharging phases. External validation on an independent dataset confirms the robustness and generalization capability of the identified feature combinations, yielding stable and low error SOH estimation across multiple battery cells. SHAP-based analysis reveals clear phase-dependent feature relevance, with voltage-related features dominating during charging, while derived features capturing dynamic behavior are more critical during discharging. Overall, our results and findings indicate that carefully designed and explainable feature engineering strategies are more important than model complexity for short-time SOH estimation, providing a practical and lightweight solution for fast battery health diagnostics. • An explainable feature engineering framework for short-time battery SOH estimation • Systematic comparison of different feature groups across charging and discharging • Hybrid-feature fusion enhances model accuracy and robustness in complex scenarios. • Efficient SOH estimation achieved using three-minute short-time data • Effective feature design outweighs model complexity for short-time SOH estimation.