Lithium-ion Battery SOC Estimation based on GA-AUKF Algorithm

Junlin Chen; Chun Wang

doi:10.62051/ijepes.v4n1.03

Authors

Junlin Chen
Chun Wang

DOI:

https://doi.org/10.62051/ijepes.v4n1.03

Keywords:

Lithium-ion Battery, SOC, Thevenin Model, Genetic Algorithm

Abstract

Lithium-ion batteries have been widely used in the field of energy storage such as electric vehicles by virtue of their high energy density, long cycle life and environmental protection characteristics. In order to enhance the precision of battery State of Charge (SOC) estimation, this paper proposes a Thevenin model as the equivalent circuit model. Utilizing a Genetic Algorithm (GA), the model parameters are optimized to ensure the accuracy of the model. The identification results are effectively verified. On this basis, three SOC estimation algorithms, GA-EKF, GA-AEKF and GA-AUKF, were designed in this paper, and simulations and error analyses were carried out based on the UDDS operating conditions data. The results indicate that the GA-AUKF algorithm demonstrates superior accuracy and stability in terms of SOC estimation accuracy, exhibiting a significantly higher level of precision than the GA-EKF and GA-AEKF algorithms.

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