Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

Xiangwei Guo, Longyun Kang, Yuan Yao, Zhizhen Huang and Wenbiao Li
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Xiangwei Guo: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
Longyun Kang: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
Yuan Yao: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
Zhizhen Huang: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
Wenbiao Li: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China

Energies, 2016, vol. 9, issue 2, 1-16

Abstract: An estimation of the power battery state of charge ( SOC ) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC . Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.

Keywords: least square method with a forgetting factor; AUKF; joint estimation (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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