Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

Abstract

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life (RUL). However, this task is challenging due to the diverse ageing mechanisms, various operating conditions, and limited measured signals. Although data-driven methods are perceived as a promising solution, they ignore intrinsic battery physics, leading to compromised accuracy, low efficiency, and low interpretability. In response, this study integrates domain knowledge into deep learning to enhance the RUL prediction performance. We demonstrate accurate RUL prediction using only a single charging curve. First, a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data. The parameters inform a deep neural network (DNN) to predict RUL with high accuracy and efficiency. The trained model is validated under 3 types of batteries working under 7 conditions, considering fully charged and partially charged cases. Using data from one cycle only, the proposed method achieves a root mean squared error (RMSE) of 11.42 cycles and a mean absolute relative error (MARE) of 3.19% on average, which are over 45% and 44% lower compared to the two state-of-the-art data-driven methods, respectively. Besides its accuracy, the proposed method also outperforms existing methods in terms of efficiency, input burden, and robustness. The inherent relationship between the model parameters and the battery degradation mechanism is further revealed, substantiating the intrinsic superiority of the proposed method.