battery-rul-estimation and RUL
These are competitors offering alternative deep learning architectures (LSTM vs. Transformer) for the same battery RUL prediction task, so practitioners would typically choose one based on model performance and implementation preferences rather than using both together.
About battery-rul-estimation
MichaelBosello/battery-rul-estimation
Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs
About RUL
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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