Autonomous Diagnostics and Prognostics in Lithium-Ion Battery Systems via Self-Healing Machine Learning

Abstract Lithium-ion batteries have emerged as critical enablers of electrified transport, renewable energy integration, and distributed power systems. Their deployment in real-world environments marked by variable loads, heterogeneous usage patterns, thermal fluctuations, and long-term degradation poses significant modeling and control challenges. Also increased complexity and dynamism of its electrochemistry pose significant challenges for conventional machine learning models used in battery management systems. These challenges include data non-stationarity, sensor anomalies, and aging-related performance drifts, which degrade prediction accuracy and compromise safety. This paper outlines a roadmap for integrating self-healing machine learning into next-generation battery management systems to enhance safety, longevity, and intelligence. It proposes an interdisciplinary framework combining online learning, meta learning, uncertainty quantification, and adaptive control for robust, continuous model correction. This Review analyses and classifies recent self-healing machine learning methodologies based on architectural and functional principles.