@inproceedings{6a6109894abe43cc9978d8395b1a0730,
title = "Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data",
abstract = "State of health estimation of battery is crucial to ensure the safety and durability of electric vehicles. This paper presents six methods to extract the battery health indicator from electric vehicle field testing data. The methods for extracting health indicators from the discharge cycle show the ability to cope with the variable driving condition. In total, 157 health indicators are extracted from the collected data. Pearson correlation coefficient and Spearman's rank correlation coefficient are used to measure the correlation between the health indicators and the state of health. The results suggest that health indicators extracted by the presented methods have high correlations to the battery state of health.",
keywords = "battery degradation, correlation analysis, electric vehicle, field testing data, lithium-ion battery, state of health",
author = "Wong, {Kei Long} and Chou, {Ka Seng} and Davide Aguiari and Rita Tse and Tang, {Su Kit} and Giovanni Pau",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Electrical Power and Energy Conference, EPEC 2022 ; Conference date: 05-12-2022 Through 07-12-2022",
year = "2022",
doi = "10.1109/EPEC56903.2022.9999742",
language = "English",
series = "2022 IEEE Electrical Power and Energy Conference, EPEC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "206--211",
booktitle = "2022 IEEE Electrical Power and Energy Conference, EPEC 2022",
address = "United States",
}
