A Stepwise Optimal Design Applied to an Interior Permanent Magnet Synchronous Motor for Electric Vehicle Traction Applications

Lee, Jae-Gil; Lim, Dong-Kuk

doi:10.1109/access.2021.3105119

Cited by 17 publications

(2 citation statements)

References 45 publications

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“…Owing to the high cost and low magnet utilization ratio of FSPM, the importance towards automotive and other applications was reduced. e authors in [57] proposed various aspects of power train system for EV applications.…”

Section: Types Of Electric Motors For Evsmentioning

confidence: 99%

An Extensive Critique on Electric Vehicle Components and Charging Systems

Iqubal

Sathiyan

Stonier³

et al. 2022

International Transactions on Electrical Energy Systems

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Nowadays the demand for electric vehicles has been increasing due to their dropping price range and zero emission of carbon. This article initially discusses the development of various electric vehicles from the past to the present. Since the electric motor plays a significant role in EVs, various motors suitable for EVs have been identified and surveyed in this work. The battery storage system is a critical component of EVs; hence, the article goes over all the different types of batteries, from lead acid to lithium ion. Additionally, the other vehicle components such as converters required for charging the batteries, intelligent controllers, electric vehicle charging process, power management, and battery energy management concepts that are available are discussed in detail. Moreover, we bring our work to a conclusion by outlining the research opportunities that remain for the academic and industrial groups. Therefore, the proposed work intends to assist as a state-of-the-art reference for researchers in the field of various electric vehicle configurations, storage systems, converter configurations, charging techniques, control methods, and modulation methods.

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Section: Types Of Electric Motors For Evsmentioning

confidence: 99%

An Extensive Critique on Electric Vehicle Components and Charging Systems

Iqubal

Sathiyan

Stonier³

et al. 2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

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“…Therefore the FEM analysis method, which is able to provide tabulated data on motor magnetic flux as a function of current and rotor angle, is mainly used in the motor design and optimization process. Numerous examples of this approach may be found in the huge bibliography of the subject-works [5][6][7][8] are typical of thousands of papers concerning interior permanent magnet machine designs published in the 21st century. Analytical calculation of d-and q-axis flux distributions is also possible [9,10], but it requires almost the same knowledge as numerical FEM analysis and the acceptance of rigorous assumptions.…”

Section: Introductionmentioning

confidence: 99%

Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

Jastrzębski

Kabziński

2021

Energies

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New results in the area of neural network modeling applied in electric drive automation are presented. Reliable models of permanent magnet motor flux as a function of current and rotor position are particularly useful in control synthesis — allowing one to minimize the losses, analyze motor performance (torque ripples etc.) and to identify motor parameters—and may be used in the control loop to compensate flux and torque variations. The effectiveness of extreme learning machine (ELM) neural networks used for approximation of permanent magnet motor flux distribution is evaluated. Two original network modifications, using preliminary information about the modeled relationship, are introduced. It is demonstrated that the proposed networks preserve all appealing features of a standard ELM (such as the universal approximation property and extremely short learning time), but also decrease the number of parameters and deal with numerical problems typical for ELMs. It is demonstrated that the proposed modified ELMs are suitable for modeling motor flux versus position and current, especially for interior permanent magnet motors. The modeling methodology is presented. It is shown that the proposed approach produces more accurate models and provides greater robustness against learning data noise. The execution times obtained experimentally from well-known DSP boards are short enough to enable application of derived models in modern algorithms of electric drive control.

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