@inproceedings{56dfbf5a9e6948b181a82cd3e0ac9245,
title = "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.",
keywords = "capacity fade, feature extraction, Lithium-ion batteries, machine learning, State of Health (SOH), voltage relaxation",
author = "Nitika Ghosh and Akhil Garg and Alexander Warnecke and Panigrahi, {B. K.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 ; Conference date: 17-10-2022 Through 20-10-2022",
year = "2022",
doi = "10.1109/IECON49645.2022.9968789",
language = "English",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
booktitle = "IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society",
}
