IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i22p7805-d684794.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/22/7805/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/22/7805/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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).
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.
    5. Gobbi, Massimiliano & Sattar, Aqeab & Palazzetti, Roberto & Mastinu, Gianpiero, 2024. "Traction motors for electric vehicles: Maximization of mechanical efficiency – A review," Applied Energy, Elsevier, vol. 357(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Shantanu Pardhi & Sajib Chakraborty & Dai-Duong Tran & Mohamed El Baghdadi & Steven Wilkins & Omar Hegazy, 2022. "A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions," Energies, MDPI, vol. 15(24), pages 1-55, December.
    3. 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.
    4. 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.
    5. Gao, Yan & Jiang, Chen & Yu, Dahai & Ahmad, Maiwand, 2023. "A novel electric differential and synchronization control method for 4WD/4WS electric vehicles based on fictitious master," Energy, Elsevier, vol. 274(C).
    6. Armin Norouzi & Hamed Heidarifar & Mahdi Shahbakhti & Charles Robert Koch & Hoseinali Borhan, 2021. "Model Predictive Control of Internal Combustion Engines: A Review and Future Directions," Energies, MDPI, vol. 14(19), pages 1-40, October.
    7. Maximilian Zähringer & Sebastian Wolff & Jakob Schneider & Georg Balke & Markus Lienkamp, 2022. "Time vs. Capacity—The Potential of Optimal Charging Stop Strategies for Battery Electric Trucks," Energies, MDPI, vol. 15(19), pages 1-18, September.
    8. Yuqing Yao & Chunhua Liu & Christopher H.T. Lee, 2018. "Quantitative Comparisons of Six-Phase Outer-Rotor Permanent-Magnet Brushless Machines for Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, August.
    9. Carlo Cunanan & Manh-Kien Tran & Youngwoo Lee & Shinghei Kwok & Vincent Leung & Michael Fowler, 2021. "A Review of Heavy-Duty Vehicle Powertrain Technologies: Diesel Engine Vehicles, Battery Electric Vehicles, and Hydrogen Fuel Cell Electric Vehicles," Clean Technol., MDPI, vol. 3(2), pages 1-16, June.
    10. Yi Li & Feng Chai & Zaixin Song & Zongyang Li, 2017. "Analysis of Vibrations in Interior Permanent Magnet Synchronous Motors Considering Air-Gap Deformation," Energies, MDPI, vol. 10(9), pages 1-18, August.
    11. Guoyu Chu & Rukmi Dutta & Alireza Pouramin & Muhammed Fazlur Rahman, 2020. "Analysis of Torque Ripple of a Spoke-Type Interior Permanent Magnet Machine," Energies, MDPI, vol. 13(11), pages 1-16, June.
    12. Thanh Anh Huynh & Min-Fu Hsieh, 2018. "Performance Analysis of Permanent Magnet Motors for Electric Vehicles (EV) Traction Considering Driving Cycles," Energies, MDPI, vol. 11(6), pages 1-24, May.
    13. Peixin Liang & Yulong Pei & Feng Chai & Kui Zhao, 2016. "Analytical Calculation of D - and Q -axis Inductance for Interior Permanent Magnet Motors Based on Winding Function Theory," Energies, MDPI, vol. 9(8), pages 1-11, July.
    14. Zeyang Fan & Hong Yi & Jian Xu & Kun Xie & Yue Qi & Sailin Ren & Hongdong Wang, 2021. "Performance Study and Optimization Design of High-Speed Amorphous Alloy Induction Motor," Energies, MDPI, vol. 14(9), pages 1-19, April.
    15. Zhengming Shu & Xiaoyong Zhu & Li Quan & Yi Du & Chang Liu, 2017. "Electromagnetic Performance Evaluation of an Outer-Rotor Flux-Switching Permanent Magnet Motor Based on Electrical-Thermal Two-Way Coupling Method," Energies, MDPI, vol. 10(5), pages 1-16, May.
    16. Chengming Zhang & Qingbo Guo & Liyi Li & Mingyi Wang & Tiecheng Wang, 2017. "System Efficiency Improvement for Electric Vehicles Adopting a Permanent Magnet Synchronous Motor Direct Drive System," Energies, MDPI, vol. 10(12), pages 1-27, December.
    17. Jyun-You Chen & Shih-Chin Yang & Kai-Hsiang Tu, 2018. "Comparative Evaluation of a Permanent Magnet Machine Saliency-Based Drive with Sine-Wave and Square-Wave Voltage Injection," Energies, MDPI, vol. 11(9), pages 1-15, August.
    18. Anselma, Pier Giuseppe & Belingardi, Giovanni, 2022. "Fuel cell electrified propulsion systems for long-haul heavy-duty trucks: present and future cost-oriented sizing," Applied Energy, Elsevier, vol. 321(C).
    19. Shantanu Pardhi & Mohamed El Baghdadi & Oswin Hulsebos & Omar Hegazy, 2022. "Optimal Powertrain Sizing of Series Hybrid Coach Running on Diesel and HVO for Lifetime Carbon Footprint and Total Cost Minimisation," Energies, MDPI, vol. 15(19), pages 1-28, September.
    20. Kai Zhou & Min Ai & Dongyang Sun & Ningzhi Jin & Xiaogang Wu, 2019. "Field Weakening Operation Control Strategies of PMSM Based on Feedback Linearization," Energies, MDPI, vol. 12(23), pages 1-18, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7805-:d:684794. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.