Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter

Bizhong Xia, Zizhou Lao, Ruifeng Zhang, Yong Tian, Guanghao Chen, Zhen Sun, Wei Wang, Wei Sun, Yongzhi Lai, Mingwang Wang and Huawen Wang
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Bizhong Xia: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Zizhou Lao: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Ruifeng Zhang: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Yong Tian: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Guanghao Chen: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Zhen Sun: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Wei Wang: Sunwoda Electronic Co., Ltd., Shenzhen 518108, China
Wei Sun: Sunwoda Electronic Co., Ltd., Shenzhen 518108, China
Yongzhi Lai: Sunwoda Electronic Co., Ltd., Shenzhen 518108, China
Mingwang Wang: Sunwoda Electronic Co., Ltd., Shenzhen 518108, China
Huawen Wang: Sunwoda Electronic Co., Ltd., Shenzhen 518108, China

Energies, 2017, vol. 11, issue 1, 1-23

Abstract: State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms.

Keywords: forgetting factor recursive least squares; nonlinear Kalman filter; state of charge estimation; online parameter identification; lithium-ion battery; variable temperature (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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