Multi-expert fusion for state-of-health estimation of lithium-ion batteries

To achieve both high accuracy and interpretability in battery State-of-Health (SoH) estimation, this study proposes a dynamic time-varying multi-expert fusion network (MEFNet) framework. The framework consists of three specialized experts: a mechanism-based general expert that captures fundamental degradation patterns, an LSTM-based local expert for short-term dynamics, and a Transformer-based global expert for long-term dependencies. These experts are integrated through a novel linear dynamic weighting scheme that adapts to evolving battery health states. This fusion framework balances interpretability and accuracy while accounting for the scarcity of full lifecycle battery data, particularly addressing challenges stemming from limited real-world data collection conditions that typically only cover early-stage operations. The experimental validation demonstrates that the critical end-of-life threshold (SoH = 70% or 80%) typically occurs within the early (0-30%) to middle (30-60%) degradation stages. The proposed MEFNet achieves superior estimation accuracy using only 25% of the lifecycle data, outperforming models trained on complete datasets particularly during early and middle degradation stages.