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Losses in Efficiency Maps of Electric Vehicles: An Overview

Author

Listed:
  • Emad Roshandel

    (College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia)

  • Amin Mahmoudi

    (College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia)

  • Solmaz Kahourzade

    (STEM, University of South Australia, Adelaide, SA 5095, Australia)

  • Amirmehdi Yazdani

    (Discipline of Engineering and Energy, College of Science, Health, Engineering and Education, Murdoch University, Perth, WA 6150, Australia
    Centre for Water, Energy and Waste, Harry Butler Institute, Murdoch University, Perth, WA 6150, Australia)

  • GM Shafiullah

    (Discipline of Engineering and Energy, College of Science, Health, Engineering and Education, Murdoch University, Perth, WA 6150, Australia
    Centre for Water, Energy and Waste, Harry Butler Institute, Murdoch University, Perth, WA 6150, Australia)

Abstract

In some applications such as electric vehicles, electric motors should operate in a wide torque and speed ranges. An efficiency map is the contour plot of the maximum efficiency of an electric machine in torque-speed plane. It is used to provide an overview on the performance of an electric machine when operates in different operating points. The electric machine losses in different torque and speed operating points play a prominent role in the efficiency of the machines. In this paper, an overview about the change of various loss components in torque-speed envelope of the electric machines is rendered to show the role and significance of each loss component in a wide range of torque and speeds. The research gaps and future research subjects based on the conducted review are reported. The role and possibility of the utilization of the computational intelligence-based modeling of the losses in improvement of the loss estimation is discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7805-:d:684794
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    References listed on IDEAS

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    1. Frans J. R. Verbruggen & Emilia Silvas & Theo Hofman, 2020. "Electric Powertrain Topology Analysis and Design for Heavy-Duty Trucks," Energies, MDPI, vol. 13(10), pages 1-30, May.
    2. Baodi Zhang & Sheng Guo & Xin Zhang & Qicheng Xue & Lan Teng, 2020. "Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 13(8), pages 1-25, April.
    3. Yuan Wan & Shumei Cui & Shaopeng Wu & Liwei Song, 2018. "Electromagnetic Design and Losses Analysis of a High-Speed Permanent Magnet Synchronous Motor with Toroidal Windings for Pulsed Alternator," Energies, MDPI, vol. 11(3), pages 1-21, March.
    4. Kahourzade, Solmaz & Mahmoudi, Amin & Roshandel, Emad & Cao, Zhi, 2021. "Optimal design of Axial-Flux Induction Motors based on an improved analytical model," Energy, Elsevier, vol. 237(C).
    5. Sebastian Wolff & Svenja Kalt & Manuel Bstieler & Markus Lienkamp, 2021. "Influence of Powertrain Topology and Electric Machine Design on Efficiency of Battery Electric Trucks—A Simulative Case-Study," Energies, MDPI, vol. 14(2), pages 1-15, January.
    6. 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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    Cited by:

    1. Aissam Riad Meddour & Nassim Rizoug & Patrick Leserf & Christopher Vagg & Richard Burke & Cherif Larouci, 2023. "Optimization Approaches for Cost and Lifetime Improvements of Lithium-Ion Batteries in Electric Vehicle Powertrains," Energies, MDPI, vol. 16(18), pages 1-29, September.
    2. Konstantina Bitsi & Sjoerd G. Bosga & Oskar Wallmark, 2022. "Design Aspects and Performance Evaluation of Pole-Phase Changing Induction Machines," Energies, MDPI, vol. 15(19), pages 1-18, September.
    3. 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.
    4. Mahdi Tousizadeh & Amirmehdi Yazdani & Hang Seng Che & Hai Wang & Amin Mahmoudi & Nasrudin Abd Rahim, 2022. "A Generalized Fault Tolerant Control Based on Back EMF Feedforward Compensation: Derivation and Application on Induction Motors Drives," Energies, MDPI, vol. 16(1), pages 1-17, December.

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