Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.770211
Title: Permanent magnet assisted synchronous reluctance machines for electric vehicle traction applications
Author: Lazari, Panagiotis
ISNI:       0000 0004 7651 6716
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2018
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Abstract:
The thesis is an investigation into the performance of Permanent Magnet Assisted Synchronous reluctance Machines (PMA SynRM) employing rare earth-free/less permanent magnets for electric vehicle traction applications. The design optimisation methodology is based on the performance of the traction machine over specific driving cycles instead of conventionally adopted practices relying on optimisations at a specific operating condition. Albeit, requirements such as the maximum torque, maximum speed and cooling are taken into consideration. Therefore, a method for the selection of a finite number of energy-representative operating points characterising a given driving cycle is proposed. The technique can predict the energy loss over a driving cycle with high accuracy (i.e., error < 6%), while only employing less than 5% of the total operating points of a driving cycle. This forms the basis upon which energy efficiency optimisation is undertaken. A rare-earth-less PMA SynRM employing bonded NdFeB permanent magnets is designed and manually optimised, using an engineer-in-the-loop approach, to meet the specification of a personal mobility electric vehicle. All the multi-physical aspects of the design, i.e., electromagnetic, thermal, and mechanical are considered in an iterative process with the target of maximising the system's (i.e., machine and drive) energy efficiency over the NEDC, while meeting the specified torque-speed operating range. Energy efficiency higher than 94% is achieved for the traction machine operating over the NEDC. Special attention is also given to the reduction of the electromagnetic torque ripple through the introduction of special features in the rotor laminations and the step skewing of the rotor. A computationally efficient modelling technique for the estimation of the effect of skew on the torque ripple and the performance of the machine is proposed and validated using 2.5D multi-slice Finite Element Analysis (FEA). Special consideration is given to the effects of the finite length of the machine, especially when step skewing is adopted. It is observed that the axial flux leakage at the boundaries between the skewed slices can significantly affect the torque ripple and hence the accuracy of the 2.5D multi-slice approach. Therefore, it is recommended that for PMA SynRM full 3D FEA may be required in order to accurately predict the effects of skew on torque ripple. Furthermore, attention is also given to the effects of laser cutting on the magnetic properties of steel laminations and a method, which enables the assessment of these effects on the performance of the machine is proposed. It is shown that a substantial proportion of the discrepancy between the measured and predicted results can be attributed to these effects. A multi-physics approach employing an algorithmic optimisation method is proposed. The technique employs sophisticated optimisation algorithms for efficient design space exploration combined with computationally efficient multi-physics models, thus considerably minimising the number of required FEA runs to reach the optimal designs. The method simultaneously takes into consideration of the main geometrical and topologic parameters of the PMA SynRM, as well as different permanent magnet materials properties. It is employed for the design optimisation of PMA SynRMs aiming to maximise energy efficiency against the NEDC, while meeting the specification requirements of the above-mentioned personal mobility EV. The resultant optimal designs are comprehensively compared against a set of indicative performance markers pertaining to EV traction. When compared to the manual optimisation method energy efficiency improvement in the region of 0.5% over a driving cycle is achieved.
Supervisor: Atallah, Kais ; Wang, Jiabin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.770211  DOI: Not available
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