Back close

Performance Comparison of Model Predictive Control and Direct Torque Control for Induction Motor Based Electric Vehicle

Publication Type : Conference Proceedings

Publisher : 2021 International Conference on System, Computation, Automation and Networking

Source : 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), p.1-6 (2021)

Campus : Coimbatore

School : School of Engineering

Department : Electrical and Electronics

Year : 2021

Abstract : Efficiency optimisation is one of the critical issues concerned with the electric vehicle. It has a significant influence on the vehicle performance parameter - milage or running distance per battery charge. Modification of the existing structural design and minimising the component losses are two possible solutions. Minimising the component and its interaction losses by modifying the control strategy has less impact on the existing system architecture. Direct torque control (DTC) is often preferred for electric vehicle thanks to its fast dynamic response. However, inverter switching losses are very high in DTC. Along with this, harmonics in the inverter output increases the electric motor losses. Switching frequency reduction can help to minimise inverter losses. But reduced switching frequency pushes the lower order harmonic component of the inverter output voltage to the high-frequency side and demands increased size of the filter to be employed. These two conflicting objectives addressed with the help of finite control set-model predictive control (FCS-MPC). Simulation results of both control strategies for constant velocity and drive cycle tests confirm the effectiveness of this proposal. The performance measures ascertain a strong argument for FCS-MPC as a future electric vehicle control strategy.

Cite this Research Publication : Asif P. N. and Mohanrajan S. R., “Performance Comparison of Model Predictive Control and Direct Torque Control for Induction Motor Based Electric Vehicle”, 2021 International Conference on System, Computation, Automation and Networking (ICSCAN). pp. 1-6, 2021.

Admissions Apply Now