Title :
The Lithium-ion battery capacity prediction error analysis based on extended Kalman filtering
Author :
Zhenwei Zhou ; Yun Huang ; Yudong Lu ; Zhengyu Shi ; Liangbiao Zhu ; Jiliang Wu ; Hui Li
Author_Institution :
China Electron. Product Reliability & Environ. Testing Res. Inst., Guangzhou, China
Abstract :
The Lithium-ion battery capacity prediction error is analyzed by use of extended Kalman filtering(EKF) and curve fitting algorithms. This paper employs the capacity degradation model described by Colum efficient factor, rest time and other two unknown parameters. Then, a nonlinear state-space model is introduced, and the EKF is presented to estimate the capacity and the two unknown parameters. The parameter setting in EKF is discussed in details. The capacity prediction error is analyzed with the help of curve fitting. The experiment example demonstrates the algorithms efficiency.
Keywords :
Kalman filters; curve fitting; error analysis; nonlinear filters; secondary cells; Colum efficient factor; Li; capacity degradation model; capacity prediction error; curve fitting algorithms; extended Kalman filtering; lithium-ion battery capacity prediction error analysis; nonlinear state-space model; Algorithm design and analysis; Batteries; Degradation; Estimation; Fitting; Prediction algorithms; Predictive models; Lithiun-ion battery; capacity; curve fitting; extended Kalman filtering; prediction erro;
Conference_Titel :
Reliability, Maintainability and Safety (ICRMS), 2014 International Conference on
Print_ISBN :
978-1-4799-6631-8
DOI :
10.1109/ICRMS.2014.7107181