State-of-Health Monitoring and Prediction of Lithium-Ion Battery Using Probabilistic Indication and State-Space Model

In this paper, a battery health prognostics system is developed based on Bayesian-inference probabilistic (BIP) indication and state-space model (SSM) that integrates logistic regression (LR) and particle filtering (PF). In this system, generative topographic mapping is constructed to model distribution of multisensor data from healthy battery under an assumption that predictable fault patterns are not available. BIP is developed as a quantification indication of battery state-of-health. BIP is capable of offering failure probability for the monitored batteries, which has intuitionist explanation related to health degradation state. SSM is used for modeling health propagation of battery on the time flow, where LR and PF are integrated to predict remaining useful life of the battery. The experimental results on a lithium-ion battery testbed illustrate the potential applications of the proposed system as an effective tool for battery health prognostics.