Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model

Lin Su, Guangxu Zhou, Dairong Hu, Yuan Liu and Yunhai Zhu
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Lin Su: Shandong Provincial Key Laboratory of Automotive Electronics Technology, Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Guangxu Zhou: Shandong Provincial Key Laboratory of Automotive Electronics Technology, Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Dairong Hu: Shandong Provincial Key Laboratory of Automotive Electronics Technology, Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Yuan Liu: Shandong Provincial Key Laboratory of Automotive Electronics Technology, Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Yunhai Zhu: Science and Technology Service Platform, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China

Energies, 2021, vol. 14, issue 19, 1-23

Abstract: Accurate estimation of the state of charge (SOC) of lithium batteries is paramount to ensuring consistent battery pack operation. To improve SOC estimation accuracy and suppress colored noise in the system, a fractional order model based on an unscented Kalman filter and an H-infinity filter (FOUHIF) estimation algorithm was proposed. Firstly, the discrete state equation of a lithium battery was derived, as per the theory of fractional calculus. Then, the HPPC experiment and the PSO algorithm were used to identify the internal parameters of the second order RC and fractional order models, respectively. As discovered during working tests, the parameters identified via the fractional order model proved to be more accurate. Furthermore, the feasibility of using the FOUHIF algorithm was evaluated under the conditions of NEDC and UDDS, with obvious colored noise. Compared with the fractional order unscented Kalman filter (FOUKF) and integer order unscented Kalman filter (UKF) algorithms, the FOUHIF algorithm showed significant improvement in both the accuracy and robustness of the estimation, with maximum errors of 1.86% and 1.61% under the two working conditions, and a terminal voltage prediction error of no more than 5.29 mV.

Keywords: lithium ion battery; fractional order model; SOC estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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