XiuzeZhou/RUL
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
Implements attention-based and mixture-of-experts variants (AttMoE) for cycle-level degradation modeling, evaluated on NASA and CALCE benchmark datasets. Built with PyTorch, the architecture incorporates tunable dropout and noise injection parameters to improve robustness during training. Supports end-to-end RUL forecasting from electrochemical time-series data with configurable sequence encoding strategies.
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