One-shot battery degradation trajectory prediction with deep learning

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.

Automated summary

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.