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Efficiency Evaluation of Electric Bicycle Power Transmission Systems

Author

Listed:
  • Sheng-Peng Zhang

    (Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon City 24341, Kangwon-do, Korea)

  • Tae-Oh Tak

    (Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon City 24341, Kangwon-do, Korea)

Abstract

In this study, a method for estimating the efficiency of electric bicycle power train systems consisting of typical components, such as an electric motor, gears, sprockets, and chains is presented. In order to calculate the efficiency of a power train system, the relationship between the drive motor torque and the road-load that is exerted on the rear wheel was derived, considering kinematic inertia effects and friction losses between power transmission elements. Among the factors that influence efficiency, it was found that friction losses play a dominant role, while the effects of inertia are insignificant. The factors that influence the efficiency of electric bicycles due to friction losses, such as the transmission efficiency of the chain system and the bearing in the sprocket and wheel, were quantified. To validate the proposed efficiency calculation procedure, an experimental electric bicycle was used, in which the driving torque and road-load could be quantitatively assessed, and the actual efficiency was measured on a chassis dynamometer. It is shown that for a given motor torque, a measured and estimated dynamometer torque obtained by the proposed method exhibits a good correlation, and the transmission efficiency of each component was quantified. This method provides a practical and accurate means to calculate the drive train efficiency of electric bicycles at the design stage to improve the efficiency of electric bicycles.

Suggested Citation

  • Sheng-Peng Zhang & Tae-Oh Tak, 2021. "Efficiency Evaluation of Electric Bicycle Power Transmission Systems," Sustainability, MDPI, vol. 13(19), pages 1-12, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10988-:d:649443
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