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Robust battery state-of-charge estimation under different operating conditions using short-term electrical responses and long-term aging information
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AI
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洪
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DOI：10.1016/j.est.2026.124044.png)
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Abstract
En 中文
• A dual-timescale representation is developed for robust battery state-of-charge estimation. • A GRU-CNN-FiLM expert jointly models fast dynamics and slow degradation. • A protocol-aware router adaptively fuses case-specific experts and a fallback branch. • The framework enables accurate and robust SOC estimation under heterogeneous operating conditions.
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