Journal
JOURNAL OF POWER SOURCES
Volume 506, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jpowsour.2021.230024
Keywords
Lithium-ion; Battery; Degradation; Deep learning; Prediction; Knee-point
Funding
- European Union [EVERLASTING-713771]
- German Federal Ministry for Economic Affairs and Energy (BMWi) [03EIV011F]
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Battery degradation is influenced by various internal mechanisms, making lifetime prediction challenging due to manufacturing and operating condition uncertainties. Researchers propose a deep learning-based health prognostics approach to predict future degradation trajectory and end-of-life point in one shot, with improved accuracy and computing speed compared to state-of-the-art methods.
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 kneepoint. 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-ofthe-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.
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