Health Degradation Detection and Monitoring of Lithium-Ion Battery Based on Adaptive Learning Method

Abstract

Battery health degradation detection and monitoring are crucial to realize equipment's near-zero downtime and maximum productivity. A big challenge is how to construct an effective monitoring system that can consistently exemplify the degradation propagation of battery health over its whole life. This paper proposes an on-line method based on adaptive learning scheme for monitoring of state-of-health (SOH) of battery. An adaptive Gaussian mixture model (AGMM) is developed for online learning dynamic changes of battery health in its full life. A Bayesian-inference-based probability indication is developed for detection of new health states that are online modeled by deleting and adding of components in AGMM. Furthermore, health changes are quantified by an AGMM-based health index that measures the overlap between two density distributions approximated by the adapted GMM and the historic GMM, respectively. Research results of its application in a lithium-ion battery life test illustrate that AGMM-based SOH monitoring system is effective for detection and adaptive assessment of battery health degradation. This paper provides a guidance for the development of battery SOH monitoring model based on adaptive learning scheme without too much human intervention.