https://scholars.lib.ntu.edu.tw/handle/123456789/638432
標題: | Using tens of seconds of relaxation voltage to estimate open circuit voltage and state of health of lithium ion batteries | 作者: | Ko, Chi Jyun KUO-CHING CHEN |
關鍵字: | Gaussian process regression | Lithium-ion battery | Open circuit voltage | Relaxation voltage | State of health | 公開日期: | 1-三月-2024 | 卷: | 357 | 來源出版物: | Applied Energy | 摘要: | Relaxation voltage (RV) of a battery is informative since it not only approximates open circuit voltage (OCV) as time evolves, but it is also related to the battery's state of charge (SOC) and state of health (SOH). Given that RV is easy to obtain by simply stopping a battery's operation, it is an excellent data source to estimate battery states. Without using complete RV history whose acquisition is time-consuming and hinders further applications, this study uses Gaussian process regression model with the input of only a small portion of RV to rapidly and simultaneously estimate the OCV and SOH of a battery. Various input lengths are tested, showing that using only 30-s RV data, the mean absolute error (MAE) for predicting OCV is 2.99 mV, and that for estimating SOH is 2.76%. As soon as the voltage difference is also treated as the model input, we find that the MAE for the SOH estimation is further declined to about 1.83%. Compared to previous methods which either estimate single battery state or require minutes of RV data for estimation, the current model is able to perform multiple battery estimation using only first tens of seconds of data. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/638432 | ISSN: | 03062619 | DOI: | 10.1016/j.apenergy.2023.122488 |
顯示於: | 應用力學研究所 |
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