Augmented model-based framework for battery remaining useful life prediction. (15th October 2022) - Thelen, Adam; Li, Meng; Hu, Chao; Bekyarova, Elena; Kalinin, Sergey; Sanghadasa, Mohan
Graphical abstract: Highlights: Machine learning models learn to correct model-based prediction of battery RUL. Uncertainty in model-based prediction is propagated through probabilistic models. The proposed method is validated on five datasets consisting of a total of 237 cells. Reduce RUL prediction error by 40% and mean uncertainty calibration error by 34%. Abstract: Traditional, model-based approaches for predicting the remaining useful life (RUL) of a rechargeable battery cell simply update and extrapolate a mathematical model which describes the evolution of the cell's capacity fade trend. These approaches are straightforward but tend to break down when the capacity fade trend changes over the cell's lifetime. To retain the desirable properties of model-based prediction approaches (uncertainty quantification, long-term accuracy, limited physical meaning) and improve their overall accuracy in RUL prediction, we augment empirical model-based prediction with data-driven error correction. Our approach decomposes the task of RUL prediction into two steps: 1) Offline training of data-driven models for RUL error correction and 2) Online data-driven correction of model-based RUL prediction. The approach is evaluated on five datasets consisting of 237 cells: 1) three open-source datasets, 2) one proprietary dataset, and 3) a simulated out-of-distribution dataset. Results show that data-driven error correction effectively reduces root-mean-square-error by 40% and mean uncertainty calibration error by 34% compared to a model-based approach alone. The proposed approach is also shown to be more conservative in its uncertainty estimates than a purely data-driven RUL prediction approach. Special attention is given to ensure the initial model-based uncertainty estimates are propagated through the data-driven error correction model and considered in the final RUL prediction. The enhanced uncertainty quantification of our approach makes it suitable for deployment in an online predictive maintenance scheduling framework.