Title :
Modeling and fuzzy logic control of electrical vehicle with an adaptive operation mode
Author :
Kraa, O. ; Becherif, M. ; Aboubou, A. ; Ayad, M.Y. ; Tegani, I. ; Haddi, A.
Author_Institution :
Lab. of Energy Syst. Modeling, Mohamed Kheider Biskra Univ., Biskra, Algeria
Abstract :
This paper presents the modelling and the traction control of an Electrical Vehicle (EV) speed based on the Energetic Macroscopic Representation (EMR) and the Maximum Control Structure (MCS). The EMR-MCS have been first developed by L2EP (Lille, France). By using a specific Fuzzy Logic Control (FLC) in MCS, an Adaptive operation Mode (AOM) is developed in this paper to reduce the energy consumption of the EV. This AOM can be Economic, Dynamic or Eco-Dynamic (EOM, DOM or EDOM) according to the battery state of charge. The EMR methodology leads to a global model of the studied vehicle and facilitating its control. The results obtained by the new proposed method of MCS-FLC under Matlab/Simulink software tool are given.
Keywords :
adaptive control; electric vehicles; fuzzy control; machine control; traction motors; velocity control; EMR methodology; Matlab software tool; Simulink software tool; adaptive operation mode; battery state of charge; electrical vehicle speed; energetic macroscopic representation; fuzzy logic control; maximum control structure; traction control; Batteries; Fuzzy logic; System-on-chip; Torque; Vehicle dynamics; Vehicles; Wheels; Adaptive Operation Mode; Battery; Electric Vehicl; Energetic Macroscopic Representation; Fuzzy Logic Contro; Maximum Control Structure;
Conference_Titel :
Power Engineering, Energy and Electrical Drives (POWERENG), 2013 Fourth International Conference on
Conference_Location :
Istanbul
DOI :
10.1109/PowerEng.2013.6635592
