@inproceedings{e7118e22b0234c13bd7e688b9c49e1b0,
title = "Improved single particle model based state of charge and capacity monitoring of lithium-ion batteries",
abstract = "State of charge and state of health monitoring of lithium-ion batteries is a hot topic in the area of battery management. Although much work has been done for state estimation based on equivalent circuit model, more research is needed to monitor battery state using electrochemical model which can reflect chemical reactions inside the battery. In this paper, an online state of charge and capacity estimation strategy is proposed based on improved single particle model using extended Kalman filter. Firstly, an improved single particle model which incorporates Li-ion concentration distribution in electrolyte phase is established. Then two extended Kalman filters with different time scales based on the model are used to estimate state of charge and capacity. Finally, the ability of the method to against erroneous initial values is evaluated, and the experimental results show the feasibility of the proposed approach.",
keywords = "Extended Kalman filter, Improved single particle model, Lithium-ion battery, State of charge, State of health",
author = "Rui Xiong and Linlin Li and Quanqing Yu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 89th IEEE Vehicular Technology Conference, VTC Spring 2019 ; Conference date: 28-04-2019 Through 01-05-2019",
year = "2019",
month = apr,
doi = "10.1109/VTCSpring.2019.8746690",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings",
address = "United States",
}
