On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

Battery state of health (SOH) monitoring has become a crucial challenge in hybrid electric vehicles (HEVs) and all electric vehicles (EVs) research, as SOH significantly affects the overall vehicle performance and life cycle. In this paper, we focus on the identification of Li-ion battery capacity fading, as the loss of capacity and therefore the driving range is a primary concern for EV and plug-in HEV (PHEV). While most studies on battery capacity fading are based on laboratory measurement such as open circuit voltage (OCV) curve, few publications have focused on capacity loss monitoring during on-board operations. We propose a battery SOH monitoring scheme based on partially charging data. Through analysis of battery aging cycle data, a robust signature associated with battery aging is identified through incremental capacity analysis (ICA). Several algorithms to extract this signature are developed and evaluated for on-board SOH monitoring. The use of support vector regression (SVR) is shown to provide the most consistent identification results with moderate computational load. For battery cells tested, we show that the SVR model built upon the data from one single cell is able to predict the capacity fading of 7 other cells within 1% error bound.

► An on-board battery state-of-health (SOH) monitoring framework is proposed.
► Capacity loss and therefore SOH can be monitored by using partially charging data.
► Support vector regression algorithm is used for robust aging signature extraction.
► Established a quantitative correlation to predict capacity fade with high accuracy.