One-shot battery degradation trajectory prediction with deep learning

Journal article

The degradation of batteries is complex and dependent on several internal mechanisms. Variations arising from manufacturing uncertainties and real-world operating conditions make battery lifetime prediction challenging. Here, we introduce a deep learning-based battery health prognostics approach to predict the future degradation trajectory in one shot without iteration or feature extraction. We also predict the end-of-life point and the knee-point. The model correctly learns about intrinsic variability caused by manufacturing differences, and is able to make accurate cell-specific predictions from just 100 cycles of data, and the performance improves over time as more data become available. Validation in an embedded device is demonstrated with the best-case median prediction error over the lifetime being 1.1% with normal data and 1.3% with noisy data. Compared to state-of-the-art approaches, the one-shot approach shows an increase in accuracy as well as in computing speed by up to 15 times. This work further highlights the effectiveness of data-driven approaches in the domain of health prognostics.

Authors: Li, W; Sengupta, N; Dechent, P; Howey, D; Annaswamy, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Journal of Power Sources
• Volume: 506
• Article number: 230024
• Publication date: 2021-06-10
• Acceptance date: 2021-05-07
• DOI: 10.1016/j.jpowsour.2021.230024
• ISSN: 0378-7753
• Language: English
• Keywords: FFR