Accurate estimation of State of Health (SoH) and Remaining Useful Life (RUL) is essential for the safe and reliable operation of Lithium-ion Batteries (LiBs). Most data-driven approaches, however, rely on computationally intensive deep learning architectures or require long, continuous degradation histories, limiting their deployment in embedded Battery Management System (BMS). This study presents a lightweight machine learning framework that achieves robust State of Health (SoH) and Remaining Useful Life (RUL) prognostics from statistical features extracted from a single diagnostic charge–discharge cycle. The framework was developed and validated on a publicly available dataset of 124 commercial Lithium Iron Phosphate (LFP)/graphite cells cycled to failure under 72 distinct fast-charging protocols. Sixteen statistical descriptors were computed from the voltage, current, and temperature signals of each cycle. The primary predictive model is a Quantile Extremely Randomized Trees (QERT) ensemble, optimised via Tree-structured Parzen Estimator (TPE) Bayesian search using a generalisation-aware objective. Prediction uncertainty is quantified by applying conformal prediction to the quantile outputs, yielding nominal 95% prediction intervals that achieved empirical coverage of 94.1% for SoH and 92.2% for RUL on the held-out test cells. Permutation-based feature importance analysis reduced the descriptor set to compact subsets of 8 and 10 features for SoH and RUL, respectively. Evaluated on 25 held-out cells unseen during training, the framework achieved aggregate test-set RMSEs of 1.31 percentage points for SoH and 198.87 cycles for RUL, with per-cell median RMSEs of 0.95 percentage points and 89.51 cycles, respectively.
Sanches et al. (Mon,) studied this question.