An Improved Battery On-line Parameter Identification and State-of-charge Determining Method

Li, Zhirun; Xiong, Rui; He, Haibo

doi:10.1016/j.egypro.2016.11.303

Cited by 10 publications

(1 citation statement)

References 3 publications

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“…In 2016, an improved battery on-line parameter identification method was put forward by Li et al [17]. In his research, the astringency and reliability of the RLS algorithm are proved.…”

Section: Introductionmentioning

confidence: 99%

A Novel Variable Forgetting Factor Recursive Least Square Algorithm to Improve the Anti-Interference Ability of Battery Model Parameters Identification

Song

Lai

2019

IEEE Access

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Recursive least square (RLS) algorithms are considered as a kind of accurate parameter identification method for lithium-ion batteries. However, traditional RLS algorithms usually employ a fixed forgetting factor, which does not have adequate robustness when the algorithm has interfered. In order to solve this problem, a novel variable forgetting factor method is put forward in this paper. Comparing with traditional variable forgetting factor methods, it has higher stability and sensitivity by using some mathematic improvements. The improvements in the robustness of recursive least square with a variable forgetting factor (VFF-RLS) algorithm is verified in this paper. A Thevenin model which is frequently-used in battery management system is employed in the verification. A data loss battery working condition is designed to simulate the interference to the algorithm. A simulation platform is established in MATLAB/Simulink software, and the data used in the verification is obtained by battery experiments. The analysis indicated that the novel VFF-RLS algorithm has better robustness and convergence ability, and has an acceptable identification accuracy.

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“…In 2016, an improved battery on-line parameter identification method was put forward by Li et al [17]. In his research, the astringency and reliability of the RLS algorithm are proved.…”

Section: Introductionmentioning

confidence: 99%