the purduetracer: an energy-efficient human-powered hydraulic bicycle with flexible operation and software aids

Clicks: 172
ID: 157833
2018
Hydrostatic transmissions (HT) are widely applied to heavy-duty mobile applications because of the advantages of layout flexibility, power to weight ratio, and ease of control. Though applications of fluid power in light-duty vehicles face challenges, including the unavailability of off-the-shelf components suitable to the power scale, there are potential advantages for HTs in human-powered vehicles, such as bicycles, the most important one being the energy-saving advantage achievable through regenerative braking in a hybrid HT. This paper describes an innovative design for a hydraulic hybrid bicycle, i.e., the PurdueTracer. The PurdueTracer is an energy-efficient human-powered hydraulic bicycle with flexible operation and software aids. An open-circuit hydraulic hybrid transmission allows PurdueTracer to operate in four modes: Pedaling, Charging, Boost, and Regeneration, to satisfy users’ need for different riding occasions. An aluminum chassis that also functions as a system reservoir was customized for the PurdueTracer to optimize the durability, riding comfort, and space for components. The selection of the hydraulic components was performed by creating a model of the bicycle in AMESim simulation software and conducting a numerical optimization based on the model. The electronic system equipped users with informative feedback showing the bicycle performance, intuitive execution of functions, and comprehensive guidance for operation. This paper describes the design approach and the main results of the PurdueTracer, which also won the 2017 National Fluid Power Association Fluid Power Vehicle Challenge. This championship serves to prove the excellence of this vehicle in terms of effectiveness, efficiency, durability, and novelty.
Reference Key
marinaro2018energiesthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Gianluca Marinaro;Zhuangying Xu;Zhengpu Chen;Chenxi Li;Yizhou Mao;Andrea Vacca
Journal acs combinatorial science
Year 2018
DOI 10.3390/en11020305
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.