A Power Assistant Algorithm Based on Human-Robot Interaction Analysis for Improving System Efficiency and Riding Experience of E-Bikes
- Authors
- Kim, Deok Ha; Lee, Dongun; Kim, Yeongjin; Kim, Sungjun; Shin, Dongjun
- Issue Date
- Jan-2021
- Publisher
- MDPI
- Keywords
- electric bicycles (E-bikes); energy consumption; human analysis; leg kinematics; muscular dynamics; riding experience
- Citation
- SUSTAINABILITY, v.13, no.2, pp 1 - 19
- Pages
- 19
- Journal Title
- SUSTAINABILITY
- Volume
- 13
- Number
- 2
- Start Page
- 1
- End Page
- 19
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47660
- DOI
- 10.3390/su13020768
- ISSN
- 2071-1050
2071-1050
- 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.
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Collections - College of Engineering > School of Mechanical Engineering > 1. Journal Articles
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