Advanced Control of Permanent Magnet Synchronous Motor for Hybrid Electric Vehicles

Abstract

Permanent-Magnet Synchronous-Motor (PMSM) is considered as one of the most preferable in the drive systems of Hybrid Electric Vehicle (HEV) as it covers its requirements of light weight and provides high output efficiency and reliability. There are many control strategies applied on HEVs motors which affect the performance of the drive. One of these is Direct Torque Control (DTC) which is advised as a control strategy to be used in motors drives. Meta-heuristic optimization techniques considered as one of the important tools to define the optimal solutions for many problems. Researchers use these techniques to produce the most effective solutions for the all-human faced problems. Latest developments in Artificial Intelligence (AI) based control have brought in to focus a possibility of optimizing all the control parameters for increased performance. Such AI control methods are developed and placed with DTC in electric machines control. In this thesis, new advanced AI based DTC speed drives are optimally designed and implemented in real time to achieve high performance with Alternating current (AC) drive for a PMSM used in HEVs, where Cuckoo Search (CS) and Grey Wolf (GW) algorithms are used with the standard DTC and with the DTC with Fuzzy Logic (FL) based speed controllers. For the real time implementation of the overall system, dSPACE DS1202 is used. and MATLAB-SIMULINK is used to perform the simulation model to offer simulation of dynamic and steady state response. For both practical