Dynamic Bayesian Network-Based Lithium-Ion Battery Health Prognosis for Electric Vehicles

Abstract

Battery prognostics and health management (PHM) is essential for lithium-ion batteries in electric vehicles. In the battery PHM, accurate estimation of the battery state-of-health (SOH) and prediction of the remaining useful life (RUL) are crucial to ensure a safe and efficient battery operation. This article presents a probabilistic method for the battery degradation modeling and health prognosis based on the features extracted from the charging process using the dynamic Bayesian network (DBN). First, an aggregated feature, combining the incremental capacity analysis of constant-current (CC) charging and the time constant of constant-voltage (CV) charging, is developed to characterize the battery degradation dynamics in case some CC or CV charging information is absent. The DBN is then employed to explore the underlying correlation between the battery health indicator and the extracted features. The proposed model treats the degradation dynamics as a rich family of probability distributions to model real-world battery operation more accurately. Moreover, the battery SOH estimation and RUL prediction are carried out using the particle filtering inference algorithm. Experimental tests are conducted on two different battery cells and the results show that the proposed methods can provide an accurate and robust battery SOH estimation and reliable RUL prediction.