Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges

Abstract

Machine learning (ML) has been the focus in recent studies aiming to improve battery and supercapacitor technology. Its application in materials research has demonstrated promising results for accelerating the discovery of energy materials. Additionally, battery management systems incorporating data-driven techniques are expected to provide accurate state estimation and improve the useful lifetime of batteries. This review briefs the ML process, common algorithms, advantages, disadvantages, and limitations of first-principles materials science research techniques. The focus of discussion is on the latest approaches, algorithms, and model accuracies for screening materials, determining structure–property relationships, optimizing electrochemical performance, and monitoring electrochemical device health. We emphasize the current challenges of ML-based energy materials research, including limited data availability, sparse datasets, and high dimensionality, which can lead to low generalizability and overfitting. An analysis of ML models is performed to identify the most robust algorithms and important input features in specific applications for batteries and supercapacitors. The accuracy of various algorithms for predicting remaining useful life, cycle life, state of charge, state of health, and capacitance has been collected. Given the wide range of methods for developing ML models, this manuscript provides an overview of the most robust models developed to date and a starting point for future researchers at the intersection of ML and energy materials. Finally, an outlook on areas of high-impact research in ML-based energy storage is provided.