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A Power Assistant Algorithm Based on Human–Robot Interaction Analysis for Improving System Efficiency and Riding Experience of E-Bikes

Author

Listed:
  • Deok Ha Kim

    (Human-Centered Robotics Lab, Department of Mechanical Engineering, Chung-Ang University, Seoul 06974, Korea)

  • Dongun Lee

    (Human-Centered Robotics Lab, Department of Mechanical Engineering, Chung-Ang University, Seoul 06974, Korea)

  • Yeongjin Kim

    (Human-Centered Robotics Lab, Department of Mechanical Engineering, Chung-Ang University, Seoul 06974, Korea)

  • Sungjun Kim

    (Human-Centered Robotics Lab, Department of Mechanical Engineering, Chung-Ang University, Seoul 06974, Korea)

  • Dongjun Shin

    (Human-Centered Robotics Lab, Department of Mechanical Engineering, Chung-Ang University, Seoul 06974, Korea)

Abstract

As robots are becoming more accessible in our daily lives, the interest in physical human–robot interaction (HRI) is rapidly increasing. An electric bicycle (E-bike) is one of the best examples of HRI, because a rider simultaneously actuates the rear wheel of the E-bike in close proximity. Most commercially available E-bikes employ a control methodology known as a power assistant system (PAS). However, this type of system cannot offer fully efficient power assistance for E-bikes since it does not account for the biomechanics of riders. In order to address this issue, we propose a control algorithm to increase the efficiency and enhance the riding experience of E-bikes by implementing the control parameters acquired from analyses of human leg kinematics and muscular dynamics. To validate the proposed algorithm, we have evaluated and compared the performance of E-bikes in three different conditions: (1) without power assistance, (2) assistance with a PAS algorithm, and (3) assistance with the proposed algorithm. Our algorithm required 5.09% less human energy consumption than the PAS algorithm and 11.01% less energy consumption than a bicycle operated without power assistance. Our algorithm also increased velocity stability by 11.89% and acceleration stability by 27.28%, and decreased jerk by 12.36% in comparison to the PAS algorithm.

Suggested Citation

  • Deok Ha Kim & Dongun Lee & Yeongjin Kim & Sungjun Kim & Dongjun Shin, 2021. "A Power Assistant Algorithm Based on Human–Robot Interaction Analysis for Improving System Efficiency and Riding Experience of E-Bikes," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:768-:d:480390
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    References listed on IDEAS

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    1. Hansen, Karsten Bruun & Nielsen, Thomas Alexander Sick, 2014. "Exploring characteristics and motives of long distance commuter cyclists," Transport Policy, Elsevier, vol. 35(C), pages 57-63.
    2. Ton, Danique & Duives, Dorine C. & Cats, Oded & Hoogendoorn-Lanser, Sascha & Hoogendoorn, Serge P., 2019. "Cycling or walking? Determinants of mode choice in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 123(C), pages 7-23.
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