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Energies 2016, 9(2), 100; doi:10.3390/en9020100

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

1
New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
2
Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China
3
College of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China
*
Author to whom correspondence should be addressed.
Received: 6 October 2015 / Revised: 27 December 2015 / Accepted: 22 January 2016 / Published: 8 February 2016
(This article belongs to the Special Issue Electrochemical Energy Storage - 2015)
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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. View Full-Text
Keywords: least square method with a forgetting factor; AUKF; joint estimation least square method with a forgetting factor; AUKF; joint estimation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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. Energies 2016, 9, 100.

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