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

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 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 on 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 approach.