An online identification method for equivalent model parameters of aging lithium-ion batteries

doi:10.19799/j.cnki.2095-4239.2020.0235

Abstract

Abstract:

The current challenges of modeling aging lithium-ion batteries include oversaturated model parameters and time-varying parameters, which cannot be evaluated with an online parameter identification of the model using the traditional least squares method with a fixed forgetting factor. This paper proposes a least squares method with a variable forgetting factor, which continuously updates the forgetting factor to better track the run-time utilization of battery aging characteristics. Using the first-order RC equivalent circuit model of the lithium battery as a model, a test platform was established for charge and discharge experiments, and the results were compared with the traditional least squares method with a fixed forgetting factor. The experimental results indicated that the proposed method can quickly converge and dynamically track battery aging. The average absolute error of the voltage parameters at the model terminal was found to be less than 25 mV. When the proposed method was run under a dynamic stress test with the typical working conditions of an energy storage system, the corresponding parameter identification accuracy was improved by 38.33%, indicating that the proposed method is highly accurate.

Key words: echelon use, Li-ion battery, decommissioned battery, equivalent circuit model, parameter identification, forgetting factor, least square method, SOC estimation

CLC Number:

TM 911

Banghua DU, Yu ZHANG, Tiezhou WU, Yanlin HE, Zilong LI. An online identification method for equivalent model parameters of aging lithium-ion batteries[J]. Energy Storage Science and Technology, 2021, 10(1): 342-348.

Figures/Tables 9

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