Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends

Abstract

Machine Learning has garnered significant attention in lithium-ion battery research for its potential to revolutionize various aspects of the field. This paper explores the practical applications, challenges, and emerging trends of employing Machine Learning in lithium-ion battery research. Delves into specific Machine Learning techniques and their relevance, offering insights into their transformative potential. The applications of Machine Learning in lithium-ion-battery design, manufacturing, service, and end-of-life are discussed. The challenges including data availability, data preprocessing and cleaning challenges, limited sample size, computational complexity, model generalization, black-box nature of Machine Learning models, scalability of the algorithms for large datasets, data bias, and interdisciplinary nature and their mitigations are also discussed. Accordingly, by discussing the future trends, it provides valuable insights for researchers in this field. For example, a future trend is to address the challenge of small datasets by techniques such as Transfer Learning and N-shot Learning. This paper not only contributes to our understanding of Machine Learning applications but also empowers professionals in this field to harness its capabilities effectively.