Authors: Karima Houacine; Rabah Mellah; Said Guermah
Addresses: Faculty of Electrical Engineering, Tizi-Ouzou University, 15000 Tizi-Ouzou, Algeria ' Faculty of Electrical Engineering, Tizi-Ouzou University, 15000 Tizi-Ouzou, Algeria ' Faculty of Electrical Engineering, Tizi-Ouzou University, 15000 Tizi-Ouzou, Algeria
Abstract: This paper presents a novel speed control design of electric vehicles (EV) to improve behaviour and stability under different road constraints conditions. The proposed control is intended to increase the efficiency of a circuit using adaptive fuzzy reasoning method with compensatory fuzzy operators. The compensatory neural fuzzy (CNF) networks are made of both control-oriented fuzzy neurons and decision-oriented fuzzy neurons. The CNF networks are not only adaptively adjust fuzzy membership functions but also dynamically optimise the adaptive fuzzy reasoning by using a compensatory learning algorithm. The proposed traction system consists of two induction motors (IMs) that ensure the drive of the two rear wheels. The controller is designed based on a control structure that realises an independent speed control. Simulation results show that the CNF control method reduces the transient oscillations and ensures efficient behaviour in all types of road constraints.
Keywords: electric vehicles; induction motors; compensatory neural fuzzy control; electronic differential; two wheeled vehicles; neural networks; fuzzy logic; speed control; controller design; adaptive fuzzy reasoning; simulation; two wheel drives; rear wheel drives.
International Journal of Electric and Hybrid Vehicles, 2015 Vol.7 No.2, pp.189 - 207
Available online: 10 Aug 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article