Integrated Framework for SOH Estimation of Lithium-Ion Batteries Under Capacity Heterogeneity in Real-World Fleets

In real-world driving conditions, onboard state-of-health (SOH) estimation for lithium-ion batteries faces three major challenges: (1) most operational data lack accurate capacity labels, limiting their usefulness for supervised learning; (2) labels derived from ampere-hour integration are highly sensitive to operating conditions and measurement errors; and (3) vehicle-side observable features often exhibit weak mechanistic relevance and limited comparability across battery platforms with different nominal capacities. To address these issues, this study proposes a unified SOH estimation framework tailored for low-confidence fleet data. The method first calibrates capacity labels through SOC linearization, temperature/current-rate correction, and robust constraints, converting low-confidence operational segments into more reliable supervision. A multi-level feature system is then constructed by coupling dimensionless statistical descriptors with mechanism-based indicators, including incremental capacity (IC) peaks and relaxation signatures, thereby strengthening feature–SOH associations and improving robustness under capacity heterogeneity. Because IC and relaxation information cannot be extracted for every charging event, explicit binary masks and time-since-last-measurement variables are introduced so that models can down-weight stale or missing mechanistic features while still utilizing them when available. Based on the calibrated labels and unified feature representation, multiple representative models, including Informer, Autoformer, TCN, LSTM, N-BEATS, CNN, MLP, SVR, and XGBoost, are systematically benchmarked under a common evaluation protocol. The outputs of selected base models are further combined through a simple averaging ensemble, which exploits complementary error patterns to improve prediction stability without increasing model complexity. Evaluation using a three-year dataset from 300 vehicles with 155 Ah batteries and a limited 180 Ah cross-capacity validation setting provides preliminary evidence of applicability under capacity heterogeneity. On the 155 Ah fleet, the ensemble model achieves a fleet-level MAE of 1.11% and a MAPE of 1.31%, while the predicted end-of-life SOH of the held-out 180 Ah test vehicle remains close to the measured value.