TY - JOUR

T1 - What is Slowing Me Down? Estimation of Rolling Resistances during Cycling

AU - Meyer, Daniel

AU - Kloss, Gideon

AU - Senner, Veit

N1 - Publisher Copyright:
© 2016 The Authors. Published by Elsevier Ltd.

PY - 2016

Y1 - 2016

N2 - In this paper, we present a method to estimate the current rolling resistance coefficient of a four-wheeled electric bicycle. We derived linear regression models between the velocity of the bicycle and the vibrations at the handlebars to be able to classify the current road surface and consequently the rolling resistance coefficient. To derive the models, we performed experiments on three different surfaces typical for cycling-asphalt, fine gravel and coarse gravel. A cyclist performed five test rides on each surface on different days at varying velocities. During the experiments power output at the pedals and velocity were measured. Additionally, vibrations at the handlebars were measured using a smartphone. Then, a curve consisting of the mathematical representation of rolling and air resistance was fitted to the experimental data and the rolling resistance coefficients of the surfaces and the effective frontal area of bicycle and cyclist were estimated. The magnitude of the vibrations at the handlebars was calculated for each test ride and each surface. From this data the linear regression models for each surface were derived using velocity as the predictor. Analyzing the data yielded rolling resistance coefficients of 0.01221, 0.01468 and 0.01832 for asphalt, fine gravel and coarse gravel, respectively, and showed significant difference. The magnitude of vibrations increases significantly with velocity and is higher for surfaces with higher rolling resistance. To validate the model the outdoor experiments were repeated with a similar prototype of a four-wheeled electric bicycle. The results can be used to classify the current surface and therefore estimate the rolling resistance coefficient. We believe that this system can help improve the estimation of the residual range of electric bicycles by providing more detailed information about the environment and consequently enhance their operating distance and the usage of the bicycle.

AB - In this paper, we present a method to estimate the current rolling resistance coefficient of a four-wheeled electric bicycle. We derived linear regression models between the velocity of the bicycle and the vibrations at the handlebars to be able to classify the current road surface and consequently the rolling resistance coefficient. To derive the models, we performed experiments on three different surfaces typical for cycling-asphalt, fine gravel and coarse gravel. A cyclist performed five test rides on each surface on different days at varying velocities. During the experiments power output at the pedals and velocity were measured. Additionally, vibrations at the handlebars were measured using a smartphone. Then, a curve consisting of the mathematical representation of rolling and air resistance was fitted to the experimental data and the rolling resistance coefficients of the surfaces and the effective frontal area of bicycle and cyclist were estimated. The magnitude of the vibrations at the handlebars was calculated for each test ride and each surface. From this data the linear regression models for each surface were derived using velocity as the predictor. Analyzing the data yielded rolling resistance coefficients of 0.01221, 0.01468 and 0.01832 for asphalt, fine gravel and coarse gravel, respectively, and showed significant difference. The magnitude of vibrations increases significantly with velocity and is higher for surfaces with higher rolling resistance. To validate the model the outdoor experiments were repeated with a similar prototype of a four-wheeled electric bicycle. The results can be used to classify the current surface and therefore estimate the rolling resistance coefficient. We believe that this system can help improve the estimation of the residual range of electric bicycles by providing more detailed information about the environment and consequently enhance their operating distance and the usage of the bicycle.

KW - cycling resistances

KW - electric bicycles

KW - road surface classification

KW - rolling resistance coefficient

UR - http://www.scopus.com/inward/record.url?scp=84982899045&partnerID=8YFLogxK

U2 - 10.1016/j.proeng.2016.06.232

DO - 10.1016/j.proeng.2016.06.232

M3 - Conference article

AN - SCOPUS:84982899045

SN - 1877-7058

VL - 147

SP - 526

EP - 531

JO - Procedia Engineering

JF - Procedia Engineering

T2 - 11th conference of the International Sports Engineering Association, ISEA 2016

Y2 - 11 July 2016 through 14 July 2016

ER -