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Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model

Author

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  • Oğuz Mısır

    (Department of Electronics and Automation, Turhal Vocational School, Tokat Gaziosmanpaşa University, Tokat 60150, Turkey)

  • Mehmet Akar

    (Department of Electric-Electronic Engineering, Faculty of Engineering and Architecture, Tokat Gaziosmanpaşa University, Tokat 60150, Turkey)

Abstract

Efficiency mapping has an important place in examining the maximum efficiency distribution as well as the energy consumption of designed electric motors at maximum torque and speed. Performing analysis at all operating points with FEM analysis in the motor design process requires high processing costs and time. In this article, a machine learning-based multivariate polynomial regression estimation model was developed to overcome these costly processes from FEM analysis. With the proposed method, the operating points of the motors in different conditions during the design process can be predicted in advance with high accuracy. In the study, two different models are developed for efficiency map and core loss estimation of interior permanent magnet synchronous motor design. The developed models use few parameters and predict with high accuracy. Estimation models shorten the design process and offer a less complex model. Obtained results are validated by comparison with FEM analysis.

Suggested Citation

  • Oğuz Mısır & Mehmet Akar, 2022. "Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3691-:d:936719
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    References listed on IDEAS

    as
    1. Paul Waide & Conrad U. Brunner, 2011. "Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems," IEA Energy Papers 2011/7, OECD Publishing.
    2. Emad Roshandel & Amin Mahmoudi & Solmaz Kahourzade & Amirmehdi Yazdani & GM Shafiullah, 2021. "Losses in Efficiency Maps of Electric Vehicles: An Overview," Energies, MDPI, vol. 14(22), pages 1-27, November.
    3. Miriam Steurer & Robert J. Hill & Norbert Pfeifer, 2021. "Metrics for evaluating the performance of machine learning based automated valuation models," Journal of Property Research, Taylor & Francis Journals, vol. 38(2), pages 99-129, April.
    4. Weiwei Gu & Xiaoyong Zhu & Li Quan & Yi Du, 2015. "Design and Optimization of Permanent Magnet Brushless Machines for Electric Vehicle Applications," Energies, MDPI, vol. 8(12), pages 1-13, December.
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