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

Deok Ha Kim, Dongun Lee, Yeongjin Kim, Sungjun Kim and Dongjun Shin
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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

Sustainability, 2021, vol. 13, issue 2, 1-18

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.

Keywords: electric bicycles (E-bikes); energy consumption; human analysis; leg kinematics; muscular dynamics; riding experience (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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