Multi-source traction drive for axial flux permanent magnet in-wheel synchronous motor

Attaianese, C.; Monaco, Mauro Di; Tomasso, G.

doi:10.1109/iemdc.2011.5994922

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…This approximation will not affect torque availability, efficiency, and fault modes. In addition, it is worth mentioning that the considered d-q model neglects field saturation, eddy currents and hysteresis losses of EM, considers sinusoidal induced ElectroMotive Force (EMF), no field current dynamics and damper winding [10]. The ESC voltage dynamic due to the modulation technique is ignored as well due to the fast time constant when compared to the overall vehicle simulation.…”

Section: E Electric Propulsion Systems Modelingmentioning

confidence: 99%

Modeling and simulation of Novel Electric/Hybrid Electric Multicopter Architectures for Urban Air Mobility

Bouchard

Verberne

D’Arpino

et al. 2021

AIAA Propulsion and Energy 2021 Forum

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This paper introduces a dynamic simulation environment developed for novel multi-copter aircraft architectures. The development is motivated by the need to better understand the safety implications of architectural design choices and to provide a formal reliability assessment framework for new Vertical Take-Off and Landing (VTOL) concepts able to consider various airframe and subsystems dynamic behavior. The concepts of interests are different multi-copters configurations investigated by NASA and featuring either electric, hybrid electric, or turboshaft driven powertrains. The simulation environment is a timemarching dynamic simulator formulated using physics-based subsystem models for the batteries, electric motors, turboshaft engines and electric generators. Identified fault modes are integrated into the subsystem models for subsequent use during reliability assessments. The impacts of subsystem faults are propagated to the vehicle flight dynamic response for analysis of their impact on the ability of the vehicle to sustain safe operations. Detailed features of the electric quadrotor model are provided to illustrate the simulation capabilities. Some faults are inserted on the different aircraft in hover and the subsystems behavior is successfully propagated at the vehicle level.

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Section: E Electric Propulsion Systems Modelingmentioning

confidence: 99%

Modeling and simulation of Novel Electric/Hybrid Electric Multicopter Architectures for Urban Air Mobility

Bouchard

Verberne

D’Arpino

et al. 2021

AIAA Propulsion and Energy 2021 Forum

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“…Sinusoidally fed PMSMs are also compatible with adjustable speed ac inverter drives since they are compatible with advanced control techniques thus transferring them of enhanced controllability when compared to rectangular-fed brushless dc permanent magnet motors (BLDC) [5]. Due to the abovementioned appealing features, sinusoidally fed PMSMs are being applied as traction motors in EVs.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of a three-phase AFPM for in-wheel electrical traction

Saavedra

Romeral

Riba

2014

2014 IEEE International Electric Vehicle Conference (IEVC)

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Abstract-Sinusoidally fed permanent magnet synchronous motors (PMSM) fulfill the special features required for traction motors to be applied in electric vehicles (EV). Among them, axial flux permanent magnet (AFPM) synchronous motors are especially suited for in-wheel applications. Electric motors used in such applications must meet two main requirements, i.e. high power density and fault tolerance. This paper deals with the optimal design of an AFPM for in-wheel applications used to drive an electrical scooter. The single-objective optimization process carried out in this paper is based on designing the AFPM to obtain an optimized power density while ensuring appropriate fault tolerance requirements. For this purpose a set of analytical equations are applied to obtain the geometrical, electric and mechanical parameters of the optimized AFPM and several design restrictions are applied to ensure fault tolerance capability. The optimization process is based on a genetic algorithm and two more constrained nonlinear optimization algorithms in which the objective function is the power density. Comparisons with available data found in the technical bibliography show the appropriateness of the approach developed in this work.

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