Robustness evaluation of probability density function based features on state-of-health estimation used in electric vehicles. Issue 1 (October 2020)

• Record Type: Journal Article
• Authors: Dong, Honglei; Fan, Jie; Zou, Yuan; Xiao, Lingyun; Deng, Bo; Ou, Yang; Yang, Yong; Guan, Jing
• Is Part Of: IOP conference series. Volume 585:Issue 1(2020)
• Publication Date: 2020-10
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/585/1/012141
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Robust and accurate state-of-health (SOH) estimation is significant for safe and reliable operation of electric vehicles (EVs). In this paper, the robustness analysis of a specific IC analysis method based on probability density function is investigated in depth. Four aging features frequently used by existing researches to estimate SOH are extracted from IC curve. The influence of temperature and current rate on these four aging features as battery ages is analyzed in detail. These aging features' sensitiveness to data length and their appearance frequency in real-world charging events are also included in deciding which one is best for SOH estimation. Finally, one aging feature that has greatest potential to be used on EVs for SOH estimation is selected out.