TY - JOUR ID - 539 TI - A Novel Intelligent Energy Management Strategy Based on Combination of Multi Methods for a Hybrid Electric Vehicle JO - AUT Journal of Modeling and Simulation JA - MISCJ LA - en SN - 2588-2953 AU - Ranjbar jaferi, M.H. AU - Mohammadi, S.M.A. AU - Mohammadian, M. AD - Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran Y1 - 2014 PY - 2014 VL - 46 IS - 2 SP - 31 EP - 46 KW - Hybrid Electric Vehicle KW - Fuzzy Logic Controller KW - Support Vector Machine KW - Empirical Mode Decomposition KW - supervisory Switching Control KW - Improved Differential Evolution Algorithm DO - 10.22060/miscj.2014.539 N2 - Based on the problems caused by today conventional vehicles, much attention has been put on the fuel cell vehicles researches. However, using a fuel cell system is not adequate alone in transportation applications, because the load power profile includes transient that is not compatible with the fuel cell dynamic. To resolve this problem, hybridization of the fuel cell and energy storage devices such as batteries and ultra-capacitors are usually applied. This article has studied a hybrid electric vehicle comprising a fuel cell system and battery pack. Energy management strategy is one of the essential issues in hybrid electric vehicles designing, for power optimal distribution as well as, improving both the fuel economy and the performance of vehicle's components. In this paper, an optimal hierarchical strategy has been proposed based on the load power prediction and intelligent controlling to achieve an optimal distribution of energy between the vehicle's power sources; and, to ensure reasonable performance of the vehicle's components. For load power prediction, a new method is presented that is based on Takagi – Sugeno fuzzy model trained by an improved differential evolutionary algorithm with an objective function formulated by support vector machine. A combination of empirical mode decomposition (EMD) algorithm capabilities, fuzzy logic controller, supervisory switching technique and improved differential evolution algorithm is used to design the proposed energy management strategy. The proposed strategy is assessed in the UDDS Standard drive cycle. Simulation results show that the proposed control strategy can fulfill all the requirements of an optimal energy management. UR - https://miscj.aut.ac.ir/article_539.html L1 - https://miscj.aut.ac.ir/article_539_834f33c78f260a3ef231c45adba192b8.pdf ER -