Journal
ELECTROCHIMICA ACTA
Volume 353, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.electacta.2020.136576
Keywords
State-of-charge; Parameters identification; Equivalent circuit model; Online estimation; Forgetting factor
Categories
Funding
- National Natural Science Foundation of China [2018NSFC51805100]
- China Postdoctoral Science Foundation [2019T120374]
- Guangxi Natural Science Foundation Program [2017GXNSFBA198198]
Ask authors/readers for more resources
Accurate estimation of state of charge (SOC) is extremely essential for energy management of electric vehicles, and precise identification of model parameters will directly affect the results of SOC estimation. However, the traditional recursive least squares (RLS) method cannot accurately track the changes of model parameters in actual complex conditions. To solve these problems, a new parameters identification method combining real-time variable forgetting factor recursive least squares (VFFRLS) and adaptive extended Kalman filter (AEKF) is proposed, and the unscented Kalman filter (UKF) method is used to calculate SOC in real time. Through comparative verification and analysis, this method owns good accuracy of model parameters identification and robustness in three commonly used equivalent circuit models. Finally, experiments under dynamic stress test (DST) cycles show that the root mean square error of terminal voltage and SOC are 0.19% and 0.07% respectively in dual polarization model with VFFRLS, which proves that the proposed method can significantly improve the estimation accuracy of SOC. (C) 2020 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available