Modeling, Analysis, and Neural Network Control of an EV Electrical Differential

Abdelhakim Haddoun (1) , Mohamed Benbouzid (1) , Demba Diallo (2) , Rachid Abdessemed (3) , Jamel Ghouili (4) , Kamel Srairi (5)

1 LBMS - Laboratoire brestois de mécanique et des systèmes
2 LGEP - Laboratoire de génie électrique de Paris
3 LEB - Laboratoire d'Electrotechnique de Batna
4 GRET - Groupe de Recherche en Electrotechnologie
5 LMSE - Laboratoire de Modélisation des Systèmes Energétiques

This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electricmotorsmakes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily.

Mots clés: Electric vehicle (EV); induction motor; neural networks; speed estimation; traction control

Domaines: Energie électrique

Abdelhakim Haddoun, Mohamed Benbouzid, Demba Diallo, Rachid Abdessemed, Jamel Ghouili, et al.. Modeling, Analysis, and Neural Network Control of an EV Electrical Differential. IEEE Transactions on Industrial Electronics, 2008, 55 (6), pp.2286-2294. ⟨10.1109/TIE.2008.918392⟩. ⟨hal-00524624⟩