Particle swarm optimization approach to optimal design of an AFPM tractionmachine for different driving conditions

Abstract

Axial flux permanent magnet (AFPM) machines can be employed as the traction motor of electric vehicles due to their high torque capability, high efficiency, modular and compact construction, and capability of integration with other mechanical components in integrated systems. Besides, the system efficiency can be further improved by optimal design of the selected electric machine. In this paper, an AFPM machine is optimized against two well-known driving cycles called the New European Drive Cycle (NEDC) and US06 and the influence of the driving cycle on the obtained machine parameters is evaluated. US06 is the more demanding driving cycle and thus the machine designed for this driving cycle demands more electrical loading compared to the machine designed for NEDC. Therefore, the copper loss minimization becomes more important for the US06-optimized machine compared to the NEDC-optimized machine.Consequently, the machine design parameters optimized for different driving cycles would be quite different. Compared to the NEDC-optimized machine, the US06-optimized machine has a lower number of coil turns, lower height of teeth, and lower diameter ratio to limit the copper losses. Furthermore, fewer magnets are needed for the motor optimized for the NEDC compared to the motor optimized for US06. A quasi-3D approach and particle swarm optimization algorithm are used in the semianalytical design optimization process. Additionally, computationally efficient 3D finite-element analysis and measurements made for the prototype AFPM machine are carried out to validate the accuracy of the quasi-3D approach.

A Review of Axial-Flux Permanent-Magnet Motors: Topological Structures, Design, Optimization and Control Techniques

“…With the further development of efficient computer system and optimization theory, new multi-objective optimization algorithms such as particle swarm optimization (PSO) [89,90], bat optimization (BO) [91] and the Taguchi algorithm [92] have also been applied to the optimization design of AFPM motors. Rostami optimized an AFPM motor applied in electric vehicles by using multi-objective optimization algorithms of the quasi-3D approach and PSO algorithm and evaluated the influence of the different driving cycles (NEDC and US06) on the obtained machine parameters [93]. Obviously, the AFPM motor design parameters optimized for different driving cycles would be quite different, and the optimized parameters were imported into the 3D finite element model to verify the accuracy.…”

An improved memetic genetic algorithm based on a complex network as a solution to the traveling salesman problem

“…examples of heuristic and metaheuristic algorithms [2][3][4][5][6][7][8]. These algorithms can change the process of algorithm implementation and solution selection using different parameters or operators and have been used to solve optimization problems such as the TSP.…”