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

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 r...

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Bibliographic Details
Published inEnergies (Basel) Vol. 11; no. 1; p. 3
Main Authors Xia, Bizhong, Lao, Zizhou, Zhang, Ruifeng, Tian, Yong, Chen, Guanghao, Sun, Zhen, Wang, Wei, Sun, Wei, Lai, Yongzhi, Wang, Mingwang, Wang, Huawen
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2018
Subjects
Adaptability
Algorithms
Batteries
Circuits
Computer simulation
Equivalent circuits
forgetting factor recursive least squares
Internet
Kalman filters
Lithium
Lithium-ion batteries
lithium-ion battery
Noise measurement
nonlinear Kalman filter
online parameter identification
Parameter estimation
Power management
State of charge
state of charge estimation
Temperature
variable temperature
Working conditions
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en11010003