Page - 110 - in Hybrid Electric Vehicles

The most used optimization algorithms in design of all types of electrical machines are as follows: genetics algorithms (GA), differential evolution algorithm (DEA), estimation of dis- tribution algorithms (EDAs), particle swarm optimization (PSO) and multi-objective genetic algorithms (MOGA, Pareto, etc.) [15]. A comprehensive evaluation of optimization algorithms was performed in Refs. [16–18]. The authors of these studies state that any such classification of different optimization algorithms is not truly appropriate since the performance is an objective closely related to the specifics of each application. Nevertheless, in the optimization of the electrical machine, the authors mostly agree that DEA achieves the best fitness values, i.e. the minimum objective function value, usually with a smaller number of evaluation steps. Considering the important step in the development of cycle of PMSM presented above, a general design procedure of PMSMOR for BSAB applications is proposed and presented in Figure 4. 2.1.2. SynRM machine for ISAB applications Variable reluctance synchronous machines have received little attention in various comparative studies approaching the selection of the most appropriate electric-propulsion system for either HEV or EV. Malan [19, 20] showed that the SynRM drive has major advantages in electrical Figure 4. General design procedure of PMSMOR. Hybrid Electric Vehicles110