Battery calendar aging and machine learning

• Published in: Joule Vol. 6; no. 7; pp. 1363 - 1367
• Main Authors: Dufek, Eric J., Tanim, Tanvir R., Chen, Bor-Rong, Sangwook Kim
• Format: Journal Article
• Language: English
• Published: United States Elsevier - Cell Press 20.07.2022
• Subjects: battery; battery aging; calendar aging; ENERGY STORAGE; machine learning
• ISSN: 2542-4351; 2542-4351
• DOI: 10.1016/j.joule.2022.06.007

Batteries continue to attract immense attention due to the critical need for new energy technologies that can help humankind transition to a net-zero future. To meet global goals for implementation of new batteries, the research community needs to more coherently consider how to go about the process of innovation, development, and validation of new technologies. Current practices for bringing a new battery technology from the benchtop to deployment can be very time consuming and complex, given that batteries can have distinct aging pathways based on both how they are cycled and their storage conditions. In nearly all use cases, batteries experience periods of active cycling and also extended periods of rest. Thus, calendar time often occurs when a battery is close to fully charged. Currently, the combined cycle and calendar life aspects receive inconsistent attention during most stages of research and development. For batteries to fulfill the critical role envisioned to meet global energy demands, greater uniformity in practice is needed to alleviate potential delays caused by the inconsistent acquisition of aging data. In this study, we present our thoughts and recommendations on the need to further advance commonality and availability of calendar aging data for emerging high-energy and long-duration batteries. While there is a broad need to adopt best practices for both calendar and cycle life assessment, a push to greater uniformity in approach and reporting is sorely needed to allow calendar life predictions to harness many of the advances that have recently been seen for cycle life prediction using machine learning (ML) and other techniques.