Seite - 130 - in Hybrid Electric Vehicles

the properties of high quality steels, soft magnetic conductors and insulation materials, while at the same time user’s demands, machine’s specifications, design variables and problem con- straints are also determined. At the next level, the appropriate objective functions, taking into account the aforementioned, are constructed and an optimization method (e.g. genetic algo- rithm) is applied. At layer 3, an analytical evaluation of all the alternative derived solutions is conducted through FEA and post-processing analysis. Finally, the optimal motor configura- tion is selected (layer 4) and its application in HEV industry is thoroughly investigated. The above approach was enhanced and finally an overall PMSM design and HEV performance assessment procedure is introduced in order to be a useful tool in the HEV design industrial process. This methodology is based upon the efficient design of the in-wheel motors and the determination of their average driving cycle efficiency. Furthermore, an analytical HEV’s model, which has been developed in Matlab/Simulink, incorporates all the necessary subsys- tems of the vehicle. The internal combustion engine, the two identical SPMSMs coupled in the front wheels, the batteries pack, the dc-dc converter, the three-phase inverter, the power- split device and the control strategy are implemented in this model in order to permit a more realistic study of HEV’s behaviour. For instance, the batteries model would make it possible to define the maximum provided voltage dynamically, while the state of their charge, the effect of their internal resistance, the effect of the prevailing temperature and working conditions can also be studied. Thus, a more appropriate selection of each single subsystem can be made resulting to an optimal energy management and performance. The first step of the proposed methodology, which is presented in flowchart form in Figure 2, is the determination of motor’s rated parameters, such as output power, speed and torque. These features are defined based on vehicle’s speed and grade-ability along with the collaboration of in-wheel motors with the internal combustion engine. The outer motor diameter is fixed by the size of the wheel and the maximum dc-link voltage is also estimated by the battery pack and converter specifications. For the design of the SPMSMs a combination of classical design theory and meta-heuristic optimization techniques can be applied. The designer can choose among popular techniques based on swarm intelligence, such as genetic algorithm (GA), par- ticle swarm optimization (PSO), ant colony optimization (ACO), etc. In [32], it is outlined that another new method called“Grey Wolf Optimizer” (GWO) exhibits acceptable and satisfactory performance when implemented in similar machine design problems. Based on the results of authors’ previous works (i.e. [20, 21]), where different optimization methods were applied and compared, it was found out that all the adopted algorithms succeeded to converge to a (sub)-optimum design solution. Despite the fact that GA presents higher computational cost and complexity than PSO, fmincon and pattern search, its solutions have been proven the most attractive among others. The same conclusion was validated for all the examined case studies, in which different performance quantities were also of primary concern. Additionally, the main advantages of GA are its capacity of parallelism detection between different agents and its elit- ist selection. The first characteristic is crucial for the computation of Pareto solutions, whereas the latter one ensures that the best solutions are passed to the next iterative step without major changes. Following these, GA has been finally chosen for the specific optimization problem. Hybrid Electric Vehicles130