DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for ensuring safety, reducing maintenance costs, and optimizing usage. However, predicting RUL is challenging due to the nonlinear characteristics of the degradation caused by complex chemical reactions. Machine learning allows precise predictions by learning the latent functions of degradation relationships based on cycling behavior. This study introduces an accurate RUL prediction approach based on feature engineering and DLinear, applied to the dataset from NASA's Prognostics Center of Excellence. Among the 20 features generated from current, voltage, temperature, and time provided in this dataset, key features contributing to degradation are selected using Pearson correlation coefficient and Shapley values. Shapley value-based feature selection effectively reflects cell-to-cell variability, showing similar importance rankings across all cells. The DLinear-based RUL prediction using key features efficiently captures the time-series trend, demonstrating significantly better performance compared to Long Short-Term Memory and Transformer models.