Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms

Jingyu Yan, Guoqing Xu, Huihuan Qian and Yangsheng Xu
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Jingyu Yan: Shenzhen Institutes of Advance Technology, the Chinese Academy of Science , Shenzhen, China
Guoqing Xu: Shenzhen Institutes of Advance Technology, the Chinese Academy of Science , Shenzhen, China
Huihuan Qian: Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China
Yangsheng Xu: Shenzhen Institutes of Advance Technology, the Chinese Academy of Science , Shenzhen, China

Energies, 2010, vol. 3, issue 10, 1-19

Abstract: State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-? nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H ? filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studiesin simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties.

Keywords: robust SoC estimation; electric vehicles; nonlinear diffusion filter; H ? filter (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: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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