Multi-objective optimal design of a five-phase fault-tolerant axial flux PM motor

Electric motors used for traction purposes in electric vehicles (EVs) must meet several requirements, including high efficiency, high power density and faulttolerance. Among them, permanent magnet synchronous motors (PMSMs) highlight. Especially, five-phase axial flux permanent magnet (AFPM) synchro...

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Detalles Bibliográficos
Autores: Saavedra Ordóñez, Harold, Riba Ruiz, Jordi-Roger|||0000-0001-8774-2389, Romeral Martínez, José Luis|||0000-0001-8112-8038
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/27685
Acceso en línea:https://hdl.handle.net/2117/27685
https://dx.doi.org/10.4316/AECE.2015.01010
Access Level:acceso abierto
Palabra clave:Electric motors.
Fault-tolerance
Optimization
Permanent magnet machines
Sizing equations
Terms-Motor design
Motors elèctrics
Àrees temàtiques de la UPC::Enginyeria electrònica
Àrees temàtiques de la UPC::Enginyeria mecànica::Motors::Motors elèctrics
Descripción
Sumario:Electric motors used for traction purposes in electric vehicles (EVs) must meet several requirements, including high efficiency, high power density and faulttolerance. Among them, permanent magnet synchronous motors (PMSMs) highlight. Especially, five-phase axial flux permanent magnet (AFPM) synchronous motors are particularly suitable for in-wheel applications with enhanced fault-tolerant capabilities. This paper is devoted to optimally design an AFPM for in-wheel applications. The main geometric, electric and mechanical parameters of the designed AFPM are calculated by applying an iterative method based on a set of analytical equations, which is assisted by means of a reduced number of three-dimensional finite element method (3D-FEM) simulations to limit the computational burden. To optimally design the AFPM, a constrained multi-objective optimization process based on a genetic algorithm is applied, in which two objective functions are considered, i.e. the power density and the efficiency. Several fault-tolerance constraints are settled during the optimization process to ensure enhanced fault-tolerance in the resulting motor design. The accuracy of the best solution attained is validated by means of 3D-FEM simulations.