INTELLIGENT SPEED CONTROL OF ELECTRIC VEHICLES USING BIO-INSPIRED ANT COLONY OPTIMIZATION ALGORITHMS

Abstract

This paper presents a speed control strategy for an interior permanent magnet synchronous motor (IPMSM) used in electric vehicles employing the ant colony optimization algorithm (ACO). The ACO algorithm is utilized to optimize the speed controller parameters, aiming to minimize overshoots, settling time, and tracking error speed of an electric vehicle (EV). Specifically, the EV model is developed in the MATLAB Simulink environment, utilizing an IPMSM as the primary drive system and powered by a 100 Ah lithium-ion battery. The proposed method is evaluated through simulation and field testing on the MicroAutoBox III dSPACE hardware-in-the-loop. Notably, ACO achieved the lowest threshold overshoot of 0% and the highest of 1.54%, outperforming PI by 24.15% and fuzzy PI by 10.56%, while maintaining comparable response time and stability under a wide range of conditions. In terms of energy efficiency, the ACO algorithm achieved a remaining battery capacity of 99.99702739%, outperforming the conventional PI controller at 99.99702723% and the fuzzy PI controller at 99.99701246%. This corresponds to improvements of approximately 1.6e−7% and 1.493e−5%, respectively. These results demonstrate the significant potential of ACO in improving energy efficiency for electric vehicles.