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Efficient Gear Ratio Selection of a Single-Speed Drivetrain for Improved Electric Vehicle Energy Consumption

Author

Listed:
  • Polychronis Spanoudakis

    (School of Production Engineering & Management, Technical University of Crete, 73100 Chania, Greece)

  • Gerasimos Moschopoulos

    (School of Production Engineering & Management, Technical University of Crete, 73100 Chania, Greece)

  • Theodoros Stefanoulis

    (School of Production Engineering & Management, Technical University of Crete, 73100 Chania, Greece)

  • Nikolaos Sarantinoudis

    (School of Production Engineering & Management, Technical University of Crete, 73100 Chania, Greece)

  • Eftichios Papadokokolakis

    (School of Production Engineering & Management, Technical University of Crete, 73100 Chania, Greece)

  • Ioannis Ioannou

    (School of Production Engineering & Management, Technical University of Crete, 73100 Chania, Greece)

  • Savvas Piperidis

    (School of Production Engineering & Management, Technical University of Crete, 73100 Chania, Greece)

  • Lefteris Doitsidis

    (School of Production Engineering & Management, Technical University of Crete, 73100 Chania, Greece)

  • Nikolaos C. Tsourveloudis

    (School of Production Engineering & Management, Technical University of Crete, 73100 Chania, Greece)

Abstract

The electric vehicle (EV) market has grown over the last few years and even though electric vehicles do not currently possess a high market segment, it is projected that they will do so by 2030. Currently, the electric vehicle industry is looking to resolve the issue of vehicle range, using higher battery capacities and fast charging. Energy consumption is a key issue which heavily effects charging frequency and infrastructure and, therefore, the widespread use of EVs. Although several factors that influence energy consumption of EVs have been identified, a key technology that can make electric vehicles more energy efficient is drivetrain design and development. Based on electric motors’ high torque capabilities, single-speed transmissions are preferred on many light and urban vehicles. In the context of this paper, a prototype electric vehicle is used as a test bed to evaluate energy consumption related to different gear ratio usage on single-speed transmission. For this purpose, real-time data are recorded from experimental road tests and a dynamic model of the vehicle is created and fine-tuned using dedicated software. Dynamic simulations are performed to compare and evaluate different gear ratio set-ups, providing valuable insights into their effect on energy consumption. The correlation of experimental and simulation data is used for the validation of the dynamic model and the evaluation of the results towards the selection of the optimal gear ratio. Based on the aforementioned data, we provide useful information from numerous experimental and simulation results that can be used to evaluate gear ratio effects on electric vehicles’ energy consumption and, at the same time, help to formulate evolving concepts of smart grid and EV integration.

Suggested Citation

  • Polychronis Spanoudakis & Gerasimos Moschopoulos & Theodoros Stefanoulis & Nikolaos Sarantinoudis & Eftichios Papadokokolakis & Ioannis Ioannou & Savvas Piperidis & Lefteris Doitsidis & Nikolaos C. Ts, 2020. "Efficient Gear Ratio Selection of a Single-Speed Drivetrain for Improved Electric Vehicle Energy Consumption," Sustainability, MDPI, vol. 12(21), pages 1-19, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:9254-:d:441361
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    References listed on IDEAS

    as
    1. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2016. "Modelling the multilevel structure and mixed effects of the factors influencing the energy consumption of electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1351-1360.
    2. Francesco Bottiglione & Stefano De Pinto & Giacomo Mantriota & Aldo Sorniotti, 2014. "Energy Consumption of a Battery Electric Vehicle with Infinitely Variable Transmission," Energies, MDPI, vol. 7(12), pages 1-21, December.
    3. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    4. Enjian Yao & Zhiqiang Yang & Yuanyuan Song & Ting Zuo, 2013. "Comparison of Electric Vehicle’s Energy Consumption Factors for Different Road Types," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-7, December.
    5. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
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    Cited by:

    1. Galvin, Ray, 2022. "Are electric vehicles getting too big and heavy? Modelling future vehicle journeying demand on a decarbonized US electricity grid," Energy Policy, Elsevier, vol. 161(C).

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