Ekhi Zugasti

Abstract Accurate and uncertainty-aware health assessment of lithium-ion batteries is essential for ensuring the reliability, safety, and efficiency of energy storage systems. While data-driven methods provide strong predictive performance, they often lack interpretability and reliable uncertainty quantification, whereas physics-based models rely on accurate parameterization and may exhibit limited generalization.

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