Machine learning for predictive maintenance in energy storage systems: A systematic review of health prediction, fault diagnosis, and deployment challenges

This research examines the function of machine learning in the predictive maintenance of energy storage systems (ESS), highlighting its capacity to improve system reliability, efficiency, and longevity. The text commences with a summary of the foundational principles of predictive maintenance in Energy Storage Systems (ESS), emphasizing the significance of real-time monitoring and the early identification of faults. Multiple machine learning methodologies spanning supervised, unsupervised, and deep learning are analyzed for their efficacy in detecting patterns and abnormalities within intricate ESS data. Practical applications are examined across many energy sectors, encompassing grid storage, electric vehicles, and the integration of renewable energy. Nonetheless, the effectiveness of machine learning for ESS predictive maintenance depends fundamentally on the availability of representative, high-quality data. This limitation is especially severe for deep learning models and for emerging ESS chemistries or deployment contexts, where public datasets are often small, laboratory-specific, or short in duration. Under such conditions, models may overfit narrow operating regimes and fail to generalize across unseen duty cycles, environmental conditions, or degradation pathways. Accordingly, machine learning should be viewed as a promising but data-dependent and security-sensitive solution whose reliability can be improved through rigorous auditing of noisy multivariate measurements, continuous monitoring of data integrity during operation, authenticated and cybersecure IoT/edge/cloud data pipelines, adversarial-robustness testing for deep models, standardized metadata, uncertainty-aware validation, and hybrid physics-informed modeling.