Leakage-Resistant and Censor-Aware Evaluation of Capacity-Only Online Remaining-Useful-Life Prediction Across Heterogeneous Lithium-Ion Battery Protocols

Online battery remaining-useful-life (RUL) predictions can be optimistic when cycles from the same cell cross data splits, predictors use future observations, or right-censored records are labeled as failures. We present a leakage-resistant, censor-aware evaluation using a 209-cell pool from NASA, CALCE, HUST, and MATR. Of these, 86 cells formed the internal pool; 21 observed-endpoint MATR development cells froze the external model; 21 official MATR primary-test cells with a strict-80% state-of-health (SOH) endpoint provided the primary point evaluation; and 82 official MATR test cells (21 events and 61 censored) supported a survival sensitivity. Predictions are causal, cells are independent units, and censored cells receive no pseudo-endpoint. A benchmark-informed heuristic routes among ridge, LSBoost, and compact XGBoost using outer-training descriptors; it is a hypothesis, not a validated selector. Internal fold-macro RMSEs for NASA, CALCE, HUST repeated grouped, and HUST protocol-family holdout were 12.15, 38.91, 137.16, and 126.39 cycles. On the 21 primary-test cells, protocol-aware XGBoost achieved 26.98-cycle battery-macro RMSE and 92.92% coverage for nominal 90% intervals. Protocol-aware features reduced battery- macro RMSE by 13.36%; this improvement remains suggestive (p = 0.0868) rather than statistically conclusive. A matched audit showed XGBoost significantly outperformed ridge but not LSBoost or random forest. A HUST learning curve improved from 20 training cells to the full set, so the routing cutoff is not a sample-sufficiency threshold. At the strict-80% SOH endpoint, nonlinear XGBoost-AFT did not improve significantly over penalized linear AFT. This work introduces a leakage-resistant, censor-aware evaluation framework for capacity-only online lithium-ion battery remaining-useful-life (RUL) prediction, rather than proposing a novel standalone prediction model. Within this framework, existing base predictors including ridge regression, LSBoost, compact XGBoost, TCN, and GRU are reused as model backends. Protocol-aware metadata features serve as an optional add-on feature branch for ablation comparison and are not mandatory inputs for the core capacity-only framework. This study performs algorithm-oriented offline evaluation using ground-truth capacity/SOH readings from public cycling datasets, rather than testing on embedded BMS hardware. The prediction performance will deteriorate when fed with noisy SOH/capacity estimates output from real battery-management systems. The benchmark-informed routing scheme serves as a heuristic hypothesis, not a universally validated or preregistered model-selection criterion. The evidence supports rigorous cell-level evaluation and cautious, representation-aware model choice, while strict-endpoint sample size and early-life uncertainty remain unresolved. This study conducts algorithm-oriented offline evaluation using ground-truth capacity and state-of-health (SOH) readings from public battery-cycling datasets, rather than performing tests on embedded BMS hardware. Prediction performance will degrade when fed with noisy SOH/capacity estimates output by real-world battery-management systems.