IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i12p3536-d191778.html
   My bibliography  Save this article

Experimental Study for the Assessment of the Measurement Uncertainty Associated with Electric Powertrain Efficiency Using the Back-to-Back Direct Method

Author

Listed:
  • Michele De Santis

    (Department of Engineering, University of Rome Niccolò Cusano, via Don Carlo Gnocchi 3, 00166 Rome, Italy)

  • Sandro Agnelli

    (OPV Solutions S.r.l., via Etna 9, 00141 Rome, Italy)

  • Fabrizio Patanè

    (Department of Engineering, University of Rome Niccolò Cusano, via Don Carlo Gnocchi 3, 00166 Rome, Italy)

  • Oliviero Giannini

    (Department of Engineering, University of Rome Niccolò Cusano, via Don Carlo Gnocchi 3, 00166 Rome, Italy)

  • Gino Bella

    (Department of Enterprise Engineering, University of Tor Vergata, via del Politecnico 1, 00133 Rome, Italy)

Abstract

Brushless electric motors are used intensively in the industrial automation sector due to the motors low inertia and fast response. According to the International Electrotechnical Commission, IEC 60034-2-1, the efficiency of a three-phase electric machine (excluding machines for traction vehicles) can be determined by direct or indirect techniques. In the case of small traction motors (<10 kW), direct methods are used extensively by manufacturers, even if no standard has been published or scheduled by the IEC. In this paper, we evaluated the accuracy of the (direct) back-to-back method for the estimation of the energy performance of a 3 kW brushless AC electric motor used in a light electric vehicle. We measured the efficiencies of a pair of motors and inverters, as well as the overall efficiency of the entire power train. The results showed that the methodology was sufficiently accurate and comparable with other indirect methods available in existing literature. Moreover, we developed a Simulink model that used the powertrain efficiency map as the input to perform the simulation of a standard urban driving cycle. The simulation was run 500 times to calculate the probability density function associated with the total range of the vehicle, considering the uncertainty of the efficiency that was determined experimentally. The simulation results confirmed the low deviation of the distribution standard compared to the average value of the range of the vehicle.

Suggested Citation

  • Michele De Santis & Sandro Agnelli & Fabrizio Patanè & Oliviero Giannini & Gino Bella, 2018. "Experimental Study for the Assessment of the Measurement Uncertainty Associated with Electric Powertrain Efficiency Using the Back-to-Back Direct Method," Energies, MDPI, vol. 11(12), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3536-:d:191778
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/12/3536/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/12/3536/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rachana Vidhi & Prasanna Shrivastava, 2018. "A Review of Electric Vehicle Lifecycle Emissions and Policy Recommendations to Increase EV Penetration in India," Energies, MDPI, vol. 11(3), pages 1-15, February.
    2. Bonges, Henry A. & Lusk, Anne C., 2016. "Addressing electric vehicle (EV) sales and range anxiety through parking layout, policy and regulation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 83(C), pages 63-73.
    3. Seiho Kim & Jaesik Lee & Chulung Lee, 2017. "Does Driving Range of Electric Vehicles Influence Electric Vehicle Adoption?," Sustainability, MDPI, vol. 9(10), pages 1-15, October.
    4. Tsiakmakis, Stefanos & Fontaras, Georgios & Ciuffo, Biagio & Samaras, Zissis, 2017. "A simulation-based methodology for quantifying European passenger car fleet CO2 emissions," Applied Energy, Elsevier, vol. 199(C), pages 447-465.
    5. Wang, Hewu & Zhang, Xiaobin & Ouyang, Minggao, 2015. "Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing," Applied Energy, Elsevier, vol. 157(C), pages 710-719.
    6. 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.
    7. Trancho, E. & Ibarra, E. & Arias, A. & Kortabarria, I. & Prieto, P. & Martínez de Alegría, I. & Andreu, J. & López, I., 2018. "Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles," Applied Energy, Elsevier, vol. 224(C), pages 647-658.
    8. Tsokolis, D. & Tsiakmakis, S. & Dimaratos, A. & Fontaras, G. & Pistikopoulos, P. & Ciuffo, B. & Samaras, Z., 2016. "Fuel consumption and CO2 emissions of passenger cars over the New Worldwide Harmonized Test Protocol," Applied Energy, Elsevier, vol. 179(C), pages 1152-1165.
    9. Ko, Jinyoung & Jin, Dongyoung & Jang, Wonwook & Myung, Cha-Lee & Kwon, Sangil & Park, Simsoo, 2017. "Comparative investigation of NOx emission characteristics from a Euro 6-compliant diesel passenger car over the NEDC and WLTC at various ambient temperatures," Applied Energy, Elsevier, vol. 187(C), pages 652-662.
    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. Kunyu Wang & Rong Yang & Yongjian Zhou & Wei Huang & Song Zhang, 2022. "Design and Improvement of SD3-Based Energy Management Strategy for a Hybrid Electric Urban Bus," Energies, MDPI, vol. 15(16), pages 1-21, August.
    2. Paweł Piskur & Piotr Szymak & Michał Przybylski & Krzysztof Naus & Krzysztof Jaskólski & Mariusz Żokowski, 2021. "Innovative Energy-Saving Propulsion System for Low-Speed Biomimetic Underwater Vehicles," Energies, MDPI, vol. 14(24), pages 1-15, December.
    3. Wenich Vattanapuripakorn & Sathapon Sonsupap & Khomson Khannam & Natthakrit Bamrungwong & Prachakon Kaewkhiaw & Jiradanai Sarasamkan & Bopit Bubphachot, 2022. "Advanced Electric Battery Power Storage for Motors through the Use of Differential Gears and High Torque for Recirculating Power Generation," Clean Technol., MDPI, vol. 4(4), pages 1-14, October.
    4. Yang Yang & Jinlong Cui & Xin Cui, 2020. "Design and Analysis of Magnetic Coils for Optimizing the Coupling Coefficient in an Electric Vehicle Wireless Power Transfer System," Energies, MDPI, vol. 13(16), pages 1-15, August.
    5. Burin Yodwong & Phatiphat Thounthong & Damien Guilbert & Nicu Bizon, 2020. "Differential Flatness-Based Cascade Energy/Current Control of Battery/Supercapacitor Hybrid Source for Modern e–Vehicle Applications," Mathematics, MDPI, vol. 8(5), pages 1-18, May.
    6. Rachana Vidhi & Prasanna Shrivastava & Abhishek Parikh, 2021. "Social and Technological Impact of Businesses Surrounding Electric Vehicles," Clean Technol., MDPI, vol. 3(1), pages 1-17, February.

    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. Trancho, E. & Ibarra, E. & Arias, A. & Kortabarria, I. & Prieto, P. & Martínez de Alegría, I. & Andreu, J. & López, I., 2018. "Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles," Applied Energy, Elsevier, vol. 224(C), pages 647-658.
    2. Pirjola, Liisa & Kuuluvainen, Heino & Timonen, Hilkka & Saarikoski, Sanna & Teinilä, Kimmo & Salo, Laura & Datta, Arindam & Simonen, Pauli & Karjalainen, Panu & Kulmala, Kari & Rönkkö, Topi, 2019. "Potential of renewable fuel to reduce diesel exhaust particle emissions," Applied Energy, Elsevier, vol. 254(C).
    3. Song, Jingeun & Cha, Junepyo, 2022. "Development of prediction methodology for CO2 emissions and fuel economy of light duty vehicle," Energy, Elsevier, vol. 244(PB).
    4. Sarmad Zaman Rajper & Johan Albrecht, 2020. "Prospects of Electric Vehicles in the Developing Countries: A Literature Review," Sustainability, MDPI, vol. 12(5), pages 1-19, March.
    5. Fontaras, Georgios & Valverde, Víctor & Arcidiacono, Vincenzo & Tsiakmakis, Stefanos & Anagnostopoulos, Konstantinos & Komnos, Dimitrios & Pavlovic, Jelica & Ciuffo, Biagio, 2018. "The development and validation of a vehicle simulator for the introduction of Worldwide Harmonized test protocol in the European light duty vehicle CO2 certification process," Applied Energy, Elsevier, vol. 226(C), pages 784-796.
    6. Tsiakmakis, Stefanos & Fontaras, Georgios & Dornoff, Jan & Valverde, Victor & Komnos, Dimitrios & Ciuffo, Biagio & Mock, Peter & Samaras, Zissis, 2019. "From lab-to-road & vice-versa: Using a simulation-based approach for predicting real-world CO2 emissions," Energy, Elsevier, vol. 169(C), pages 1153-1165.
    7. He, Liqiang & Hu, Jingnan & Zhang, Shaojun & Wu, Ye & Zhu, Rencheng & Zu, Lei & Bao, Xiaofeng & Lai, Yitu & Su, Sheng, 2018. "The impact from the direct injection and multi-port fuel injection technologies for gasoline vehicles on solid particle number and black carbon emissions," Applied Energy, Elsevier, vol. 226(C), pages 819-826.
    8. Triantafyllopoulos, Georgios & Kontses, Anastasios & Tsokolis, Dimitrios & Ntziachristos, Leonidas & Samaras, Zissis, 2017. "Potential of energy efficiency technologies in reducing vehicle consumption under type approval and real world conditions," Energy, Elsevier, vol. 140(P1), pages 365-373.
    9. Zhengwei Xia & Dongming Wu & Langlang Zhang, 2022. "Economic, Functional, and Social Factors Influencing Electric Vehicles’ Adoption: An Empirical Study Based on the Diffusion of Innovation Theory," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    10. Ciuffo, B. & Fontaras, G., 2017. "Models and scientific tools for regulatory purposes: The case of CO2 emissions from light duty vehicles in Europe," Energy Policy, Elsevier, vol. 109(C), pages 76-81.
    11. Pavlovic, J. & Ciuffo, B. & Fontaras, G. & Valverde, V. & Marotta, A., 2018. "How much difference in type-approval CO2 emissions from passenger cars in Europe can be expected from changing to the new test procedure (NEDC vs. WLTP)?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 136-147.
    12. Sousa, Nuno & Almeida, Arminda & Coutinho-Rodrigues, João, 2020. "A multicriteria methodology for estimating consumer acceptance of alternative powertrain technologies," Transport Policy, Elsevier, vol. 85(C), pages 18-32.
    13. Karol Tucki & Remigiusz Mruk & Olga Orynycz & Katarzyna Botwińska & Arkadiusz Gola & Anna Bączyk, 2019. "Toxicity of Exhaust Fumes (CO, NO x ) of the Compression-Ignition (Diesel) Engine with the Use of Simulation," Sustainability, MDPI, vol. 11(8), pages 1-15, April.
    14. Xiong, Siqin & Yuan, Yi & Yao, Jia & Bai, Bo & Ma, Xiaoming, 2023. "Exploring consumer preferences for electric vehicles based on the random coefficient logit model," Energy, Elsevier, vol. 263(PA).
    15. Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    16. Christian Wankmüller & Maximilian Kunovjanek & Robert Gennaro Sposato & Gerald Reiner, 2020. "Selecting E-Mobility Transport Solutions for Mountain Rescue Operations," Energies, MDPI, vol. 13(24), pages 1-19, December.
    17. Shi, Xiao & Pan, Jian & Wang, Hewu & Cai, Hua, 2019. "Battery electric vehicles: What is the minimum range required?," Energy, Elsevier, vol. 166(C), pages 352-358.
    18. Anne Christine Lusk & Xin Li & Qiming Liu, 2023. "If the Government Pays for Full Home-Charger Installation, Would Affordable-Housing and Middle-Income Residents Buy Electric Vehicles?," Sustainability, MDPI, vol. 15(5), pages 1-26, March.
    19. Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.
    20. Felix Hinnüber & Marek Szarucki & Katarzyna Szopik-Depczyńska, 2019. "The Effects of a First-Time Experience on the Evaluation of Battery Electric Vehicles by Potential Consumers," Sustainability, MDPI, vol. 11(24), pages 1-25, December.

    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:11:y:2018:i:12:p:3536-:d:191778. 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.