Forecasting battery capacity with dynamic transitions in operational conditions

Accurate battery capacity forecasting is crucial for ensuring safe operation and effective maintenance scheduling. However, capacity prediction remains challenging due to the complex, nonlinear degradation processes influenced by diverse operational conditions and usage patterns. Existing operational condition analysis methods either treat voltage, current, and temperature independently, losing cross-variable coupling effects, or aggregate exposure durations without preserving temporal ordering, discarding transition dynamics that influence degradation pathways. This work addresses both limitations through a transition-aware encoding method that discretizes measurements into joint operational bins, tracking the sequence of transitions between bins and preserving both coupled effects and temporal dynamics. An encoder–decoder neural network processes these compact representations to generate capacity forecasts over extended horizons. Based on experimental data from lithium–iron–phosphate (LFP) cells undergoing nonlinear degradation, the proposed transition-aware encoding forecasts absolute capacity with a mean absolute percentage error of 1.68% and captures cycle-to-cycle capacity variation to within 0.16%, while simultaneously compressing raw time-series data by 94.3%. Compared to methods that discard temporal ordering or treat measurements independently, the proposed approach reduces worst-case capacity prediction errors by more than 50%. • Developed transition-aware encoding for battery operational sequences. • Validated on encoder–decoder network for long-term capacity forecasting. • Reduced worst-case capacity prediction errors by over 50%. • Achieved 94.3% storage reduction while preserving temporal dynamics.