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

Energies

Jingyu Yan²  Guoqing Xu²  Huihuan Qian¹ 

[1] Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China; E-Mails:;Shenzhen Institutes of Advance Technology, the Chinese Academy of Science , Shenzhen, China

关键词: robust SoC estimation;    electric vehicles;    nonlinear diffusion filter;    H_∞ filter;   

DOI  :  10.3390/en3101654

来源: mdpi

【 摘 要 】

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-κ 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) anfilter 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 studies in 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.

【 授权许可】

CC BY   
© 2010 by the authors; licensee MDPI, Basel, Switzerland.