@inproceedings{e61638883f2a487ab89229096ea4768a,
title = "State of charge estimation and state of health diagnostic method using multilayer neural networks",
abstract = "Lithium batteries are the most common energy storage devices in fields such as electric vehicles, portable devices, and energy storage systems. Continuously using the battery in a degradation state creates a fire or explosion risk. To prevent such accidents, research on a battery management system (BMS) that diagnoses the state of a battery was conducted. This study proposes a method that uses multilayer neural networks (MNN) for state of charge (SOC) estimation and state of health (SOH) diagnosis. The proposed method uses four MNN models as SOH diagnostic models and three SOC estimation models. Each SOC estimation model comprises a normal model, a caution model, and a fault model according to the learned data based on the output result of the SOH diagnostic model. From the experiments, the proposed method estimates and diagnoses SOC and SOH well.",
keywords = "Estimation method, Lithium battery, Multilayer deep neural network, State of charge, State of health",
author = "Lee, \{Jong Hyun\} and Kim, \{Hyun Sil\} and Lee, \{In Soo\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Electronics, Information, and Communication, ICEIC 2021 ; Conference date: 31-01-2021 Through 03-02-2021",
year = "2021",
month = jan,
day = "31",
doi = "10.1109/ICEIC51217.2021.9369782",
language = "English",
series = "2021 International Conference on Electronics, Information, and Communication, ICEIC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 International Conference on Electronics, Information, and Communication, ICEIC 2021",
address = "United States",
}