@inproceedings{4a4fad6788cf4177b78b467918be4b66,
title = "Robustness Evaluation of Unscented Kalman Filter for State of Charge Estimation Based on Battery Capacity Degradation Model",
abstract = "In this paper, a robustness evaluation of Unscented Kalman Filter (UKF) in comparison with the Extended Kalman Filter (EKF) for State of Charge (SOC) estimation of a lithium-ion battery based on capacity degradation model is investigated. To more comprehensively evaluate the performance of EKF and UKF, A first-order RC equivalent circuit model was used to characterize the dynamic behavior of a 30Ah lithium-ion battery. Based on the relationship between the Arrhenius formula, battery capacity, temperature and charge-discharge current accelerated stress, a fitting formula is obtained to predict the battery capacity degradation rate. The simulation results show that UKF outperforms EKF in terms of estimation accuracy and convergence rate against temperature effects, current and voltage noises.",
keywords = "Arrhenius formul, Capacity degradation model, EKF, Robustness evaluation, SOC estimation, UKF",
author = "Hamza Mediouni and {El Hani}, Soumia and Harouri, {Khadija El} and Jo{\~a}o Martins and Gon{\c c}alves, {Ricardo Jardim}",
note = "Funding Information: This work is supported by the project “Collaborative Research: Integrated Framework for the optimal Control and Communication of Electric Vehicles systems in future Smart Grid environment” funded by CNRST/FCT 2019/2020. Publisher Copyright: {\textcopyright} 2019 IEEE.; 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 ; Conference date: 14-10-2019 Through 17-10-2019",
year = "2019",
month = oct,
doi = "10.1109/IECON.2019.8926868",
language = "English",
isbn = "978-1-7281-4879-3",
series = "Annual Conference of Industrial Electronics Society",
publisher = "IEEE Computer Society Press",
pages = "4537--4542",
booktitle = "Proceedings",
}
