Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect

Abstract The multidimensional parameter space resulting from the interplay of complex physicochemical mechanisms and dynamic operating conditions renders traditional trial‐and‐error methods increasingly inadequate for advanced battery research. Although data‐driven approaches have demonstrated considerable potential for accurate battery prognosis, their inherently opaque architectures often hinder the extraction of mechanistic insights, thereby limiting their applicability in guiding the refinement of operating strategies and the design of next‐generation battery systems. In response to this limitation, interpretable machine learning frameworks that balance predictive fidelity with physicochemical relevance have emerged as a compelling alternative. Building on this paradigm shift, this review systematically examines state‐of‐the‐art interpretable battery lifetime prediction techniques, focusing on four critical dimensions: white‐box model, physics‐informed machine learning, physics‐inspired feature engineering, and post‐hoc analysis techniques. Emerging challenges and strategic research directions are analyzed to guide the next‐generation battery innovation, further increasing confidence in the vast potential of interpretable machine learning to revolutionize the future sustainable energy landscape.