READ ME File For 'Dataset for the paper: Efficient Energy Conversion in Electrically Assisted Bicycles Using a Switched Reluctance Machine under Torque Control'

Dataset DOI: 10.5258/SOTON/D1617


ReadMe Author: Bahareh Zaghari, University of Southampton [0000-0002-5600-4671]



This dataset supports the publication:

AUTHORS:Bahareh Zaghari, Aleksas Stuikys, Alex S. Weddell, Steve Beeby

TITLE:Efficient Energy Conversion in Electrically Assisted Bicycles Using a Switched Reluctance Machine under Torque Control

JOURNAL:IEEE ACCESS

PAPER DOI IF KNOWN:



ABSTRACT: The prospect of physical exertion commonly acts as a deterrent to the adoption of cycling for everyday transport. A battery powered assistance torque electric motor could alleviate such physical exertion by reducing the effort required by the cyclist. This study investigates the potential effectiveness, efficiency, and energy saving of electrically-assisted cycling when assistance torque of a switched reluctance motor is designed to vary in accord to the cyclist instantaneous torque at the pedal cranks. Specifically, the modulated motor assistance torque is delivered at the least efficient human input torque points on the cycle. For a representative short distance cycling schedule modulating the instantaneous torque of the on-board electric motor causes the electric energy expenditure to not exceed that of the assisted cycling mode of an identical constant-torque motor. Furthermore, for the same speed profile cycling journey with added road gradient and head wind resistance, the energy expenditure of the modulated torque motor is equal to the constant torque motor. These findings indicate significant improvements in the cycling experience.


This dataset contains:

Figures.xlsx that includes the data of figures mentioned below.



The figures are as follows:



Fig. 1 Human cycling effectiveness at 82 (cranks per minute) for both legs. Crank arm length taken as 0.19 (m). Data adopted from [26].

Fig. 2  (a) Instantaneous cycling torque and constant motor (motor 1) assistive torque. (b) Instantaneous cycling torque and time varying motor (motor 2) assistive torque.

Fig. 5  Flux-linkage versus phase current for aligned and unaligned positions of the designed 18/12 SR motor.

Fig. 6  (a) SR machine phase turn on angle look-up table as a function of torque and speed demand, including the selected constant mechanical power curves (kW) as functions of the speed. (b) Turn off angle look-up table as a function of torque and speed demand. (c) SR machine efficiencies look-up table as a function of torque and speed demand, including the selected constant mechanical power curves (kW) as functions of speed. Mechanical power is calculated from torque and speed. For these analyses the zero-degree angle is where the poles are fully aligned.

Fig. 8  Cyclist cadence rate and motor rotational speed demand according to the urban cycling schedule [37] and the model parameters from Table II.

Fig. 9  Bicycle propulsion torque demand according to the urban cycling schedule [37] and the model parameters from Table II and Eq. (6).
Fig. 10  Constant motor torque demand (+ sign) and modulated motor torque demand (o sign) for the urban cycling schedule as a function of the motor speed. The lines in the graph indicate the efficiency values.
Fig. 11  Model instantaneous cyclist torque as a function of the crank angle and cadence rate, assumed to decrease linearly with the increase in cadence rate [28].
Fig. 12  Instantaneous human torque, average instantaneous motor torque, and the synergetic total propulsion torque of those two sources.
Fig. 13  .  Motoring energy expenditure as a function of the cycling schedule for the constant torque and modulated torque modes.
Fig. 14  Phase currents and motor efficiencies as functions of the cyclist instantaneous torque for the constant and the modulated torque modes.
Fig. 15  Constant and modulated motor phase currents as functions of the cycling schedule.
Fig. 16  Constant and modulated motor efficiencies as functions of the cycling schedule.



Date of data collection: 29/10/2020

Information about geographic location of data collection: Southampton, UK

Licence: CC BY

Related projects: EP/P010164/1 Wearable and Autonomous Computing for Future Smart Cities


Date that the file was created: November, 2020