Abstract
In this paper a new topology of Permanent Magnet Synchronous Actuator (PMSA) is used for steer-by-wire application. The magnetic field patterns are determined from finite element modeling, for different rotor positions and supply currents, using FEMM software. The designed actuator geometric is, then, optimized using Genetic Algorithm in order to ameliorate its electromagnetic characteristics, and its resulting torque. Finally, a thermal analysis is achieved for the initial and the optimized actuators. The obtained results show a clear improvement of the actuator electromagnetic characteristics and heat distribution.
Highlights
Permanent Magnet Synchronous Actuators (PMSA) have become more attractive because they respond well to new technology requirements [1]
Genetic Algorithms (GA) are adaptive heuristic search algorithms based on the evolutionary ideas of natural selection and genetics
Computer Aided Design (CAD) systems with thermal analysis and computational modules become very powerful tools to investigate the thermal distribution within the electronics package, [8, 9]
Summary
Permanent Magnet Synchronous Actuators (PMSA) have become more attractive because they respond well to new technology requirements [1]. The renewed interest for these machines is due, in large part, to their excellent dynamic characteristics, low loss and their high specific torque, making them better suited to industrial applications requiring electrical drive control position or speed [2]. PMSA are suitable for low speed and high torque applications. Our contribution consists on the design of an actuating system for Steer-By-Wire (SBW) application; based on PMSA. The SBW drive system does not include gearbox, the electric machine being directly driven by the motion control system. The electric machine in case of high torque applications should operate at low rotational speeds and it should have a large number of magnetic poles. The finite element model of PMSA is analyzed. The finite element model of the actuator designed is optimized using a genetic algorithm. Electromagnetic performances of the actuator are analyzed and interpreted
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