Modeling and Simulation of Electric Vehicle to Optimize Its Cost and Range
Shivangi Kaushik, Bachelor of Technology degree from the Department of Electrical and Electronics Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 415-419 | Volume-8 Issue-6, August 2019. | Retrieval Number: E7819068519/2019©BEIESP | DOI: 10.35940/ijeat.E7819.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the present days, the vehicles are primarily operated for transportation applications. The conventional energy sources like oil and gas are used as fuel sources of various vehicles. Since the year 2000, electric vehicles are commonly used in various countries. Within the previous few years, the electric vehicle remains a constant topic for the research community. The drive train can be the main challenge for the researchers. In this paper, we developed an EV drive train configuration with the help of AC motors. The developed model contains a battery source, AC motors (Induction (squirrel cage) and Synchronous (PMSM), motor controller (DTC (Direct torque control) and FOC (field Oriented control), PI control, wheels configuration (Front and Rear) and vehicle body. The model developed on the Simulink tool of Matlab. A Metaheuristic optimization algorithm WOA (Whale Optimization Algorithm) used to optimize the gain parameters of PI control. WOA based optimized PI controllers can adjust their gains values (Kp and Ki) in correspondence to deviations of EV speed and torque and results in stable speed and torque conditions. The proposed optimization controllers possess advantages over conventional controllers in terms of its robustness, to achieve better EV stability, no speed overshoot and accurate speeds.
Keywords: Electric vehicles, Motors, Drive train, PI controllers, Whale optimization algorithm (WOA).