학술논문

Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain
Document Type
Conference
Source
2017 IEEE Conference on Control Technology and Applications (CCTA) Control Technology and Applications (CCTA), 2017 IEEE Conference on. :853-858 Aug, 2017
Subject
Robotics and Control Systems
Torque
Mechanical power transmission
Traction motors
Rotors
Robustness
Genetic algorithms
Stators
Language
Abstract
In electrified powertrain control, meeting the torque demands and ensuring efficient Electrical Machine (EM) operations are two essential but conflicting demands. A multi-objective Linear Parameters Varying (LPV) controller is proposed to address the problem of these conflicting objectives. The synthesis of multi-objective controller is based on the selection of optimal weighting functions optimized by Genetic Algorithm (GA). The effectiveness of the proposed controller is tested and evaluated for an electrified powertrain operating in a standard urban driving cycles. The stability of the proposed Multi-Objective Controller (MOC) is established. The nonlinear simulation of the proposed controller delivers the robust performance and better efficiency of an EV Induction Machine (IM) based electric drive over the entire driving cycle.