Data-Driven Analysis and Machine Learning-Based Estimation of SOC and RUL in Lithium-Ion Batteries Using Heterogeneous Operational Data

The accurate estimation of State of Charge (SOC) and Remaining Useful Life (RUL) is a key challenge in lithium-ion battery management systems, due to the nonlinear, time-varying, and multi-physics nature of battery dynamics. This work presents a systematic comparative study for SOC and RUL estimation based on the analysis of the NASA battery dataset, characterized by significant heterogeneity in operating conditions, temperature regimes, and cycle durations. The study combines a physically informed feature engineering process with machine learning models, including tree-based ensembles, kernel methods, and neural networks. The dataset is analyzed from an electrochemical, thermal, and impedance perspective, highlighting the role of internal resistance evolution, SOC–voltage characteristics, and temperature dynamics as indicators of battery degradation. Based on these observations, two regression problems are formulated: a local window-based representation for SOC estimation and a cycle-level representation for RUL prediction. Particular attention is devoted to the impact of dataset heterogeneity, feature construction, and target representation on the predictive behavior of the considered models. In addition, the work investigates the effect of normalized RUL representations and provides an interpretability-oriented comparison of the learned regressors through feature-importance analysis and parity plots. Experimental results show that SOC estimation is a comparatively well-conditioned problem, achieving high accuracy across nonlinear models, although the dominant role of temporal and current-derived features highlights the strong dependence of the prediction task on the structure of the experimental protocol. In contrast, RUL prediction exhibits significantly higher complexity due to long-term degradation uncertainty and inter-battery variability. The introduction of a normalized RUL representation substantially improves prediction accuracy and stability, particularly for ensemble-based approaches. Feature importance analysis confirms that capacity-related variables dominate RUL estimation, while voltage, temporal, and current-derived features play a central role in SOC prediction. Overall, the results show that physically interpretable feature construction combined with ensemble learning methods provides an effective framework for battery state estimation and degradation analysis under heterogeneous operating conditions.