Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium-ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time-varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance.
Bibliographical noteThis is the peer reviewed version of the following article: Sun, D, Yu, X, Zhang, C, Wang, C & Huang, R 2020, 'State of charge estimation for lithium-ion battery based on an intelligent adaptive unscented Kalman filter', International Journal of Energy Research, vol. 44, no. 14, pp. 11199-11218., which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/er.5690. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
FunderThe work has received support from the UK-China Joint Research and Innovation Partnership fund from China Scholarship Council (CSC) and British Council (BC) under the grant number 201703780098 and the grants from the National Natural Science Foundation of China under grant numbers 51806189 and 51476143. The authors also would like to thank the supports from NSFC-RS Joint Project under the grant numbers 5151101443 and IE/151256. The support from Cao Guang Biao High Tech Talent Fund, Zhejiang University is also highly acknowledged. This work also received support from EPSRC for project TRENDS (reference number EP/R020973/1).
- distribution change
- error innovation sequence
- intelligent adaptive unscented Kalman filter
- lithium-ion battery
- state of charge
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Nuclear Energy and Engineering
- Fuel Technology
- Energy Engineering and Power Technology