State-of-Health Prediction for EV Lithium-Ion Batteries via DLinear and Robust Explainable Feature Selection
This work addresses battery management for electric vehicles, offering a scalable and interpretable solution, though it is incremental as it builds on existing methods with new feature selection.
The paper tackled the problem of predicting state-of-health (SOH) for electric vehicle lithium-ion batteries by proposing an explainable data-driven framework, achieving higher accuracy with DLinear compared to LSTM and Transformer while using fewer training cycles and lower computational cost.
Accurate prediction of the state-of-health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and efficient operation of electric vehicles (EVs). Battery packs in EVs experience nonuniform degradation due to cell-to-cell variability (CtCV), posing a major challenge for real-time battery management. In this work, we propose an explainable, data-driven SOH prediction framework tailored for EV battery management systems (BMS). The approach combines robust feature engineering with a DLinear. Using NASA's battery aging dataset, we extract twenty meaningful features from voltage, current, temperature, and time profiles, and select key features using Pearson correlation and Shapley additive explanations (SHAP). The SHAP-based selection yields consistent feature importance across multiple cells, effectively capturing CtCV. The DLinear algorithm outperforms long short-term memory (LSTM) and Transformer architectures in prediction accuracy, while requiring fewer training cycles and lower computational cost. This work offers a scalable and interpretable framework for SOH forecasting, enabling practical implementation in EV BMS and promoting safer, more efficient electric mobility.