@inproceedings{1372cd857810430aa957727d539cbed8,
title = "State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging",
abstract = "State of health (SOH) estimation has deep insights into the lithium-ion battery (LIB) life diagnostic and protection. A machine learning-based SOH estimator is established, utilizing a new set of health indicators (His) extracted from the regional constant-voltage (CV) charging. First, a thorough analysis is performed over different CV-based His to obtain the informative ones with strong correlation against the SOH. Second, an artificial neural network model is employed to construct the nonlinear mapping from the selected His to the battery SOH. The proposed SOH estimator is validated with long-term degradation experiments performed on LiNiCoAlO2 (NCA) cells. Results imply the proposed method manifests itself with high estimation accuracy, low charging integrity requirements, and a high robustness to cell inconsistency.",
keywords = "health indicators, lithium-ion battery, neural network, state of health",
author = "Haokai Ruan and Zhongbao Wei and Hongwen He",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021 ; Conference date: 24-05-2021 Through 27-05-2021",
year = "2021",
month = may,
day = "24",
doi = "10.1109/ECCE-Asia49820.2021.9479412",
language = "English",
series = "Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "950--952",
booktitle = "Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021",
address = "United States",
}
