Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery
Artikel i vetenskaplig tidskrift, 2018

Model-based observers appeal to both research and industry utilization due to the high accuracy and robustness. To further improve the robustness to dynamic work conditions and battery ageing, the online model identification is integrated to the state estimation, giving rise to the co-estimation methods. This paper systematically compares three types of co-estimation methods for the online state of charge of lithium-ion battery. This first method is dual extended Kalman filter which uses two parallel filters for co-estimation. The second method is a typical data-model fusion method which uses recursive least squares for model identification and extended Kalman filter for state estimation. Meanwhile, a noise compensating method based on recursive total least squares and Rayleigh quotient minimization is exploited for online model identification, which is further designed in conjunction with the extended Kalman filter to estimate the state of charge. Simulation and experimental studies are carried out to compare the performances of three methods in terms of the accuracy, convergence property, and noise immunity. The computing cost and tuning effort are further discussed to give insights to the application prospective of different methods.

Lithium-ion battery


Model identification

State of charge

Noise corruption


Zhongbao Wei

Beijing Institute of Technology

Jiyun Zhao

City University of Hong Kong

Changfu Zou

Chalmers, Elektroteknik, System- och reglerteknik

Tuti Mariana Lim

Nanyang Technological University

King Jet Tseng

Singapore Institute of Technology

Journal of Power Sources

0378-7753 (ISSN)

Vol. 402 189-197


Sannolikhetsteori och statistik





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