State of Health Estimation of Lithium-Ion Batteries for Dynamic Driving Profiles Based on Feature Extraction from Battery Relaxation Time Using Machine Learning

Abstract

The state of health (SOH) of lithium-ion battery is very crucial in accessing the performance of electric vehicle (EV) as it is the indicator of degraded battery capacity or increased internal resistance over time. In the recent years, the machine learning based SOH estimation has garnered much attention due to the complex and nonlinear nature of battery ageing process. In this paper, five Health Indicators (HIs) are extracted from the battery data, which are both convenient and feasible to be extracted in real-time driving conditions. Based on the utmost practicality, a novel HI ‘Deviational Voltage over Relaxation Time (DVR)’ fed to Gaussian Process Regression (GPR) network is used to evaluate the estimation performance in potential real usage using NASA battery dataset. The results show that DVR correctly captured the battery ageing phenomena and provides superior estimation performance in terms of computational time and accuracy.