Premium
Real‐time cycling cadence estimation via wheel speed measurement
Author(s) -
Rallo Gianmarco,
Formentin Simone,
Corno Matteo,
Savaresi Sergio M
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2885
Subject(s) - cadence , reliability (semiconductor) , computer science , automotive engineering , torque , simulation , engineering , control theory (sociology) , electronic engineering , artificial intelligence , power (physics) , physics , quantum mechanics , control (management) , thermodynamics
Summary The need to reduce emissions and improve mobility in overcrowded cities is promoting the use of bicycles as transportation means. Bicycles have a small footprint, are easy to use, and cost effective. The introduction of modern electric bicycles has also widened the user base, extending the reach of bicycles as a commuter's option. Electric bicycles, in order to meet regulation standards, need sensors that are not usually available on muscular bicycles, like torque or cadence sensors. In this paper, we develop a cadence estimation strategy based on the wheel speed encoder only, thus allowing to remove the cadence sensor. Specifically, we propose 2 approaches, ie, a direct cadence estimate and an indirect one via gear ratio estimate. Both estimation problems are shown to be equivalent to a frequency tracking problem, which can be solved by Kalman filtering. The final algorithm embeds a logic supervisor that guarantees the reliability of the procedure in all working conditions, including freewheeling. The whole analysis and development are based upon a thorough experimental campaign using an instrumented bicycle.