Performance Enhancement of the IPMSM for HEV Applications Using Grain-Oriented Electrical Steel and Design Optimization

Son, Ji-Chang; Kim, Ji-Yeon; Choi, Jae-Wan; Lim, Dong-Kuk; Yeo, Han-Kyeol

doi:10.1109/access.2022.3170896

Cited by 12 publications

(6 citation statements)

References 32 publications

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“…Then, the more important design parameters are considered in the robust design process. For example, in [24], a multi-level robust optimization procedure is implemented. Firstly, using surrogate model an optimum design point is calculated.…”

Section: B Taguchi Optimizationmentioning

confidence: 99%

Robust Design of Induction Machines for High-Speed Electric Freight Locomotive Applications

Mahmouditabar,

Baker

2024

IEEE Access

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This paper showcases a systematic procedure to design the most efficient re design of a commercially available induction machine to be used as a traction motor in an Electric Freight Locomotive for the rail journey from Tehran to Mashhad. Using Taguchi optimization, copper and aluminium wound versions of the machine are optimized for an 1870-ton load locomotive with a peak power of 1203kW. The driving cycle is summarized using the K-means clustering method to obtain representative points. Full details of the drive cycle and guiding rail adhesion modelling are given and subsequently used in a sequential multiphysics and multi-operation mode robust procedure which optimizes the geometry and cooling parameters whilst taking manufacturing tolerances into account. The paper thus presents a thorough comparative study of aluminium-based and copper-based designs to meet a realistic Electric Freight Locomotive specification. Results indicated that the proposed approach is effective in optimum and robust design of conventional and aluminium induction machines and can be applied to other machine types operating on other rail journeys. Switching to aluminium is one way of improving the recyclability of motors, but in the scenario considered here, results in a 1.1% reduction in efficiency over the driving cycle.

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Section: B Taguchi Optimizationmentioning

confidence: 99%

Robust Design of Induction Machines for High-Speed Electric Freight Locomotive Applications

Mahmouditabar,

Baker

2024

IEEE Access

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“…This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2022.3232857 motor parameters described in Section III-A at the current conditions obtained in (6).…”

Section: A Problemmentioning

confidence: 99%

“…Farhadian et al [5] proposed an optimization algorithm based on improved particle swarm optimization to optimize the torque characteristics of a synchronous reluctance motor. Son et al [6] optimized the rotor shape of an IPMSM with grain-oriented electrical steel applied to the stator teeth using a modified genetic algorithm. Shi et al [7] proposed multi-objective optimization based on the sequential Taguchi method for a five-phase permanent magnet hub motor.…”

Section: Introductionmentioning

confidence: 99%

Efficiency Optimization Design That Considers Control of Interior Permanent Magnet Synchronous Motors Based on Machine Learning for Automotive Application

Shimizu

2023

IEEE Access

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Interior permanent magnet synchronous motors have become widely used as traction motors in environmentally friendly vehicles. Interior permanent magnet synchronous motors have a high degree of design freedom and time-consuming finite element analysis is required for their characteristics analysis, which results in a long design period. Here, we propose a method for fast efficiency maximization design that uses a machine-learning-based surrogate model. The surrogate model predicts motor parameters and iron loss with the same accuracy as that of finite element analysis but in a much shorter time. Furthermore, using the current and speed conditions in addition to geometry information as input to the surrogate model enables design optimization that considers motor control. The proposed method completed multi-objective multi-constraint optimization for multi-dimensional geometric parameters, which is prohibitively timeconsuming using finite element analysis, in a few hours. The proposed shapes reduced losses under a vehicle test cycle compared with the initial shape. The proposed method was applied to motors with three rotor topologies to verify its generality.INDEX TERMS finite element analysis, iron loss, machine learning, motor efficiency, multi-objective multi-constraint optimization, permanent magnet motors, XGBoost

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“…This combination results in high energy efficiency [5], [6]. Given the need for high efficiency and high-power density in HEVs, interior permanent magnet synchronous motors (IPMSMs) that use rare earth permanent magnets are predominantly employed [7], [8]. When designing an IPMSM used to drive an EV or HEV, various factors such as torque ripple, average torque, and efficiency must be considered [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of an IPMSM for HEVs Using Circular Area Movement Optimization With the Pattern Search Method

Lee,

Park,

Lim

2024

IEEE Access

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Performance Enhancement of the IPMSM for HEV Applications Using Grain-Oriented Electrical Steel and Design Optimization

Cited by 12 publications

References 32 publications

Robust Design of Induction Machines for High-Speed Electric Freight Locomotive Applications

Robust Design of Induction Machines for High-Speed Electric Freight Locomotive Applications

Efficiency Optimization Design That Considers Control of Interior Permanent Magnet Synchronous Motors Based on Machine Learning for Automotive Application

Optimal Design of an IPMSM for HEVs Using Circular Area Movement Optimization With the Pattern Search Method

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