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Performance Gains of Load Sensing Brake Force Distribution in Motorcycles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published November 21, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: NuGen Summit
Commercial motorcycles and scooters incorporate independent circuits for front and rear brake actuation, thus precluding load-dependent brake force distribution. In all cases of manual brake force modulation between the front and rear wheels, there is poor compensation for the changes in wheel loads on the account of longitudinal weight transfer, thus making it challenging to provide an adequate braking force to each wheel.
The ratio in which the braking force should be distributed between the front and the rear wheels is dependent on the motorcycle’s geometry, weight distribution, mechanical sizing of braking system components, and is a variable based on the instantaneous deceleration. This connotes that a fixed bias of front and rear braking forces can be optimized only for a narrow range of motorcycle’s deceleration.
Maximum braking performance occurs just prior to wheel lock-up, as a sliding tire provides less grip than a rolling tire. This is also the scenario when both the tires are doing the maximum work in decelerating the motorcycle. Therefore an optimal brake force distribution is one that locks both the wheels at the same instant. In practice, however, a rider would avoid a front wheel lock-up as it would make the motorcycle challenging to steer.
In theory, an apt distribution of the braking forces between the front and rear wheels maximizes the overall braking efficiency of the motorcycle whilst reducing its stopping distance. This paper examines the plausible performance gains of load sensing brake force distribution in a motorcycle.
CitationChakraborty, A., "Performance Gains of Load Sensing Brake Force Distribution in Motorcycles," SAE Technical Paper 2019-28-2426, 2019, https://doi.org/10.4271/2019-28-2426.
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- Ball , J.K. and Stone , R. Automotive Engineering Fundamentals Society of Automotive Engineers 2004 9780768009873
- Dukkipati , R.V. , Pang , J. , Qatu , M.S. , Sheng , G. , and Shuguang , Z. Road Vehicle Dynamics Society of Automotive Engineers 2008 9780768016437
- Gillespie , T.D. Fundamentals of Vehicle Dynamics Society of Automotive Engineers 1992 9781560911999
- Lieh , J. Closed-form Method to Evaluate Bike Braking Performance Human Power eJournal 19 7 April 24, 2013
- Forester , J. Effective Cycling MIT Press 2012 9780262516945
- Milliken , W.F. and Milliken , D.L. Race Car Vehicle Dynamics Society of Automotive Engineers 1994 9781560915263
- Wong , J.Y. Theory of Ground Vehicles John Wiley & Sons 2008 9780470170380
- Walker , J. The Physics of Braking Systems StopTech LLC 2005