Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

Xiangdong Sun, Jingrun Ji, Biying Ren, Chenxue Xie and Dan Yan
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Xiangdong Sun: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Jingrun Ji: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Biying Ren: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Chenxue Xie: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Dan Yan: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China

Energies, 2019, vol. 12, issue 12, 1-15

Abstract: With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm.

Keywords: lithium-ion battery; equivalent circuit model; recursive least square; adaptive forgetting factor; parameter identification (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: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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