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Analysis of Assistive Strategies for Electric Bikes That Include Rider’s Physiological Characteristics

[+] Author Affiliations
Andrea Zignoli, Lorenzo Beatrici, Francesco Biral

University of Trento, Trento, Italy

Paper No. DETC2018-85288, pp. V003T01A012; 8 pages
doi:10.1115/DETC2018-85288
From:
  • ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 20th International Conference on Advanced Vehicle Technologies; 15th International Conference on Design Education
  • Quebec City, Quebec, Canada, August 26–29, 2018
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5178-4
  • Copyright © 2018 by ASME

abstract

Control strategies of electric-bikes (e-bikes) do not take the physiological characteristics (e.g. aerobic fitness status) of the rider into account. By means of mathematical modelling, our aim was to analyse different assistive strategies that include these characteristics.

Particularly, we applied an Optimal Control (OC) algorithm to test whether an attentive control strategy could guarantee a sustainable effort for the rider throughout an entire climbing course with varying slope. We found that the contribution of the electric motor was pivotal during accelerations, so the effort for the kinetic energy conversion was shared between the electric motor and the cyclist.

OC seems to fit very well in a scenario where e-bikes are adopted on a daily basis for commuting or to increase the level of physical activity in a sedentary population. We suggest that intelligent control algorithms, like OC, could be embedded in the electric motors to improve e-bike experience, especially in sedentary adults.

Copyright © 2018 by ASME
Topics: Bicycles , Physiology

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