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; Wenbiao Li

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2018.0851

Published Article

LAPSE:2018.0851

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, Wenbiao Li

November 16, 2018

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.

Record ID

LAPSE:2018.0851

Keywords

AUKF, joint estimation, least square method with a forgetting factor

Subject

Process Monitoring

Suggested Citation

Guo X, Kang L, Yao Y, Huang Z, Li W. Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm. (2018). LAPSE:2018.0851

Author Affiliations

Guo X: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China; Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China; College of Electrical
Kang L: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China; Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China
Yao Y: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China; Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China
Huang Z: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China; Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China
Li W: New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China; Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China
[Login] to see author email addresses.

Journal Name

Energies

Volume

9

Issue

2

Article Number

E100

Year

2016

Publication Date

2016-02-08