Xian-Jun Du

Interpretable Multi-Feature Fusion for Lithium-Ion Battery State-of-Health Prediction Using ICEEMDAN-Autoformer-BiTCN

Experiments on CALCE lithium-ion battery datasets show that the proposed ICEEMDAN-Autoformer-BiTCN model outperforms the tested baseline combinations, achieving an MAE of 6.6646 and an R2 of 0.9896 under ICEEMDAN decomposition.