The challenge and opportunity of battery lifetime prediction from field data

Journal article

Accurate battery life prediction is a critical part of the business case for electric vehicles, stationary energy storage, and nascent applications such as electric aircraft. Existing methods are based on relatively small but well-designed lab datasets and controlled test conditions but incorporating field data is crucial to build a complete picture of how cells age in real-world situations. This comes with additional challenges because end-use applications have uncontrolled operating conditions, less accurate sensors, data collection and storage concerns, and infrequent access to validation checks. We explore a range of techniques for estimating lifetime from lab and field data and suggest that combining machine learning approaches with physical models is a promising method, enabling inference of battery life from noisy data, assessment of second-life condition, and extrapolation to future usage conditions. This work highlights the opportunity for insights gained from field data to reduce battery costs and improve designs.

Authors: Sulzer, V; Mohtat, P; Aitio, A; Lee, S; Yeh, YT

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Cell Press
• Journal: Joule
• Volume: 5
• Issue: 8
• Pages: 1934-1955
• Publication date: 2021-07-09
• Acceptance date: 2021-06-10
• DOI: 10.1016/j.joule.2021.06.005
• EISSN: 2542-4351
• Language: English
• Keywords: FFR