Mapping whole-event drive losses: studying the impact of race profile and rider input on bicycle transmission efficiency
Keywords:transmission, efficiency, model, losses, bicycle, derailleur, chaindrive
Several studies have considered the factors influencing transmission efficiency in a bicycle. These conclude that the number of teeth in sprockets which are engaged with the chain, and the torque and cadence of the cyclist influence the frictional losses associated with transmission between rider and rear wheel.
These parameters may vary greatly during a bicycle race since a rider modifies gear, power, and cadence to maximise physiological efficiency for optimum bicycle velocity. Furthermore, gearing selection and power input varies between riders, riding group and course profile. However, power models used to estimate race outcomes tend to simplify efficiency to a single factor, describing losses which scale linearly with input power regardless of expected regime.
This study extends existing analytical descriptions of transmission losses to the context of a road bicycle with front and rear derailleurs. The calculated efficiency is considered within a cycling model to judge different regimes under which the chain will typically operate and maps overall performance during an event. Different event types are illustrated to judge the variability of transmission efficiency. The authors present a general tool to estimate drive efficiency across different regimes based on simple calibration calculations.
This method of power modelling can be further applied to judge the net effect of improvements to drive efficiency. Some changes to a drive, such as using large pulley wheels, are demonstrated to improve efficiency disproportionately across typical loads and so the expected loading must be considered to understand the effect on drive losses. Furthermore, changes which reduce transmission losses may be detrimental in other loss regimes, such as by increasing mass or aerodynamic drag, and so considering these within a holistic cycling model can be important.
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