Machine learning for battery systems applications: Progress, challenges, and opportunities

Machine learning has emerged as a transformative force throughout the entire engineering life cycle of electrochemical batteries. Its applications encompass a wide array of critical domains, including material discovery, model development, quality control during manufacturing, real-time monitoring, state estimation, optimization of charge cycles, fault detection, and life cycle management. Machine learning excels in its ability to identify and capture complex behavioral trends in batteries, which may be challenging to model using more traditional methods. The goal of this survey paper is to synthesize the rich existing literature on battery machine learning into a structured perspective on the successes, challenges, and prospects within this research domain. This critical examination highlights several key insights. Firstly, the selection of data sets, features, and algorithms significantly influences the success of machine learning applications, yet it remains an open research area with vast potential. Secondly, data set richness and size are both pivotal for the efficacy of machine learning algorithms, suggesting a potential for active machine learning techniques in the battery systems domain. Lastly, the field of machine learning in battery systems has extensive room for growth, moving beyond its current focus on specific applications like state of charge (SOC) and state of health (SOH) estimation, offering ample opportunities for innovation and expansion.