Artificial Intelligence Application’s for 4WD Electric Vehicle Control System


A novel speed control design of 4WD electric vehicle (EV) to improve the comportment and stability under different road constraints condition is presented in this paper. The control circuit using intelligent adaptive fuzzy PI controller is proposed. Parameters which guide the functioning of PI controller are dynamically adjusted with the assistance of fuzzy control. The 4WD is powered by four motors of 15 kilowatts each one, delivering a 384 N.m total torque. Its high torque (338 N.m) is instantly available to ensure responsive acceleration performance in built-up areas. The electric drive canister of tow directing wheels and tow rear propulsion wheels equipped with tow induction motors thanks to their light weight simplicity and their height performance. Acceleration and steering are ensure by electronic differential, the latter control separately deriving wheels to turn at any curve. Electric vehicle are submitted different constraint of road using direct torque control. Electric vehicle are simulated in MATLAB SIMULINK. The simulation results have proved that the intelligent fuzzy PI control method decreases the transient oscillations and assure efficiency comportment in all topologies road constraints, straight, curved road, descent.

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A. Nasri and B. Gasbaoui, "Artificial Intelligence Application’s for 4WD Electric Vehicle Control System," Intelligent Control and Automation, Vol. 3 No. 3, 2012, pp. 243-250. doi: 10.4236/ica.2012.33028.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. Wang, Q. Wang, L. Jin and C. Song, “Independent Wheel Torque Control of 4WD Electric Vehicle for Differential Drive Assisted Steering,” Mechatronics, Vol. 21, 2011, pp. 63-76. doi:10.1016/j.mechatronics.2010.08.005
[2] J. Wang, Q. Wang and L. Jin, “Modeling and Simulation Studies on Differential Drive Assisted Steering for EV with Four-Wheel-Independent-Drive,” Proceedings of the 4th IEEE Vehicle Power and Propulsion Conference (VPPC2008), Harbin, 3-5 September 2008, pp. 1-7.
[3] H. Yoichi, T. Yasushi and T. Yoshimasa, “Traction Control of Electric Vehicle: Basic Experimental Results Using the Test EV ‘UOT Electric March,’” IEEE Transaction on Industry Application, Vol. 34, No. 5, 1998, pp. 1131-1138.
[4] F. Wu and T. J. Yeh, “A Control Strategy for an Electrical Vehicle Using Two In-Wheel Motors and Steering Mechanism,” Proceedings of AVEC’08, Kobe, 6-9 October 2008, pp. 796-801.
[5] M. Q. Gao and S. H. He, “Self-Adapting Fuzzy-PID Control of Variable Universe in the Non-linear System,” 2008 International Conference on Intelligent Computation Technology and Automation, Changsha, 20-22 October 2008, pp. 473-478.
[6] J.-Y. Chen, P.-S. Tsai and C.-C. Wong, “Adaptive Design of a Fuzzy Cerebellar Model Arithmetic Controller Neural Network,” IEEE Proceedings of Control Theory and Applications, Vol. 152, No. 2, 2005, pp. 133-137.
[7] C.-M. Lin and Y.-F. Peng, “Adaptive CMAC-Based Supervisory Control for Uncertain Nonlinear Systems,” IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 34, No. 2, 2004, pp. 1248-1260.
[8] H. G. Han. C.-Y. Su and Y. Stepanenko, “Adaptive Control of a Class of Nonlinear Systems with Nonlinearly Parameterized Fuzzy Approximators,” IEEE Transactions on Fuzzy Systems, Vol. 9, No. 2, 2001, pp. 315-323. doi:10.1109/91.919252

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