Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering

• Propose a knowledge-constrained machine learning method for batteries prognostics • Propose a data-driven stochastic model for battery degradation • Employ an ANN-based DEKF method to capture the dynamics of lithium-ion batteries • Validate the proposed approach using NASA dataset for battery prognostics Accurately predicting the remaining useful life (RUL) of lithium-ion rechargeable batteries remains challenging as the battery capacity degrades in a stochastic manner given the internal complex electrochemical reactions of the battery and the external operational/environmental conditions. In this work, a knowledge-constrained machine learning framework is developed to learn the stochastic degradation of battery performance over working cycles for health prognostics of Lithium-Ion batteries. An artificial neural network (ANN) model is first trained and synchronized using a Dual Extended Kalman Filter (DEKF) to obtain critical health information of Lithium-Ion batteries. With the obtained health information, a knowledge-constrained machine learning method (KcML) is then developed to predict the stochastic degradation of the battery capacity in operation. Specifically, prior knowledge in battery capacity fade can be formulated as extra constraints to facilitate the development of machine learning models with improved fidelity level for battery capacity predictions. A dataset published by NASA is utilized to demonstrate the effectiveness of the proposed knowledge-constrained machine learning approach.