| Issue |
E3S Web Conf.
Volume 194, 2020
2020 5th International Conference on Advances in Energy and Environment Research (ICAEER 2020)
|
|
|---|---|---|
| Article Number | 02023 | |
| Number of page(s) | 4 | |
| Section | Renewable Energy and New Energy Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202019402023 | |
| Published online | 15 October 2020 | |
State-of-charge Estimation of Lithium-ion Battery Based Online Parameter Identification
1 Engineering Technology Research Center of Rail Transit Power and Control, Huainan Normal University, 232038, Huainan, China
2 Manager of Technical Department, Huainan Research Institute of Mine electronic Technology, 232038, Huainan, China
* Juqiang Feng: fjq5060912@126.com
Accurately estimating the state of charge (SOC) of lithium-ion is very important to improving the dynamic performance and energy utilization efficiency. In order to reduce the influence of model parameters and system coloured noise on SOC estimation accuracy, this paper proposes the SOC estimation based on online identification. Based on the mixed simplified electrochemical model, the forgetting factor recursive least squares (FFRLS) method was used to identify the parameters online, and the SOC estimation was carried out in combination with Unscented Kalman Filter (UKF). Finally, the accuracy and feasibility of the method are verified by Federal Urban Driving Schedule (FUDS), the online identification and SOC estimation are carried out. The experimental results show that the SOC estimation of online parameter identification is more accurate, the system stability is faster and the error is smaller.
© The Authors, published by EDP Sciences, 2020
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