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Effects of the Structure and Operating Parameters on the Performance of an Electric Scooter

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  • Le Trong Hieu

    (School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Republic of Korea)

  • Ock Taeck Lim

    (School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Republic of Korea)

Abstract

The research objective is to approach the dynamic and consumed electrical energy of an electric scooter by varying the key input parameters, including rider mass, electric scooter mass, wind speed, wheel radius, and slope grade. A simulation model of an electric scooter was applied in a MATLAB-Simulink environment to investigate the scooter velocity, required power, battery voltage, and propulsion torque of the e-scooter. It was established by employing mathematical equations during the of electric scooters. The study found that the scooter velocity and electricity consumption were optimized by 3.9% and 0.08%, respectively, when the scooter weight decreased from 26 to 10 kg. The scooter velocity, electricity consumption, and required power decreased by 23.2%, 0.55%, and 8.56%, respectively, when the slope grade decreased from 1.15% to 0%. Following a wind speed reduction from 4 to 0 m/s, the consumed electricity and required power were optimized by 0.2% and 5.5%, respectively. The consumed electricity increased by 0.2% and the scooter velocity and required power significantly increased by 36.5% and 34.3% when the wheel radius increased from 0.105 to 0.185 m. Furthermore, the e-scooter could achieve an effective performance with a weight of 10 kg, wheel radius of 0.185 m, wind speed of 0 km/h, slope grade of 0%, and minimal rider weight. The simulation results showed that the scooter’s effective performance range and consumed electrical energy could be optimized by suitably adjusting the key structures and operating parameters. To support this research, a concurrent experiment investigated the dynamic characteristics and electricity consumption of the electric scooter during operation. The experimental and simulated results had the same patterns in similar initial conditions.

Suggested Citation

  • Le Trong Hieu & Ock Taeck Lim, 2023. "Effects of the Structure and Operating Parameters on the Performance of an Electric Scooter," Sustainability, MDPI, vol. 15(11), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8976-:d:1162269
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    References listed on IDEAS

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    1. Cao, Zhejing & Zhang, Xiaohu & Chua, Kelman & Yu, Honghai & Zhao, Jinhua, 2021. "E-scooter sharing to serve short-distance transit trips: A Singapore case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 177-196.
    2. Le-Trong Hieu & Nguyen Xuan Khoa & Ocktaeck Lim, 2021. "An Investigation on the Effects of Input Parameters on the Dynamic and Electric Consumption of Electric Motorcycles," Sustainability, MDPI, vol. 13(13), pages 1-13, June.
    3. Aree Wangsupphaphol & Surachai Chaitusaney & Mohamed Salem, 2023. "A Techno-Economic Assessment of a Second-Life Battery and Photovoltaics Hybrid Power Source for Sustainable Electric Vehicle Home Charging," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    4. Himadry Shekhar Das & Md Nurunnabi & Mohamed Salem & Shuhui Li & Mohammad Mominur Rahman, 2022. "Utilization of Electric Vehicle Grid Integration System for Power Grid Ancillary Services," Energies, MDPI, vol. 15(22), pages 1-15, November.
    5. Yang, Hongtai & Huo, Jinghai & Bao, Yongxing & Li, Xuan & Yang, Linchuan & Cherry, Christopher R., 2021. "Impact of e-scooter sharing on bike sharing in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 23-36.
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    1. Le Trong Hieu & Ock Taeck Lim, 2024. "A Deep Learning Approach to Optimize the Performance and Power Demand of Electric Scooters under the Effect of Operating and Structure Parameters," Energies, MDPI, vol. 17(2), pages 1-19, January.
    2. Hieu, Le Trong & Lim, Ock Taeck, 2024. "Deep learning application in fuel cell electric bicycle to optimize bicycle performance and energy consumption under the effect of key input parameters," Applied Energy, Elsevier, vol. 369(C).

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