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In this study, a test setup was designed for three- and/or four-wheeled electric mobility scooter models used by disabled or old people. This system is composed of a test platform that enables measurement of vehicular velocity, a data acquisition card, and an interface prepared in the C# program. Using the data acquisition card that was designed, seven quantities, namely, battery and motor currents, battery and motor terminal voltages, wheel speed in revolution per minute, and ambient and motor temperatures, were measured instantaneously during the test procedure and transferred to a computer via a USB device. Using these data that were transferred, motor speed in revolution per minute, torque generated by the motor, motor shaft power, motor and driver efficiency, instantaneous velocity of the vehicle, and total distance covered information obtained from the moment the vehicle began to be used were computed in real time throughout the experiment in the interface prepared in the C#, and their graphs were drawn and recorded. Thus, the faults in the battery, motor, or driver of electric mobility scooters became easily detectable in tests conducted under various conditions such as different ambient temperatures, different user weights, and roads with different slopes.
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Original Paper
Measurement and Control
2019, Vol. 52(9-10) 1434–1444
ÓThe Author(s) 2019
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0020294019865756
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Design and implementation of a test
setup for electric mobility scooter for
the disabled
Ramazan Akkaya
1
and Fatih Alpaslan Kazan
2
Abstract
In this study, a test setup was designed for three- and/or four-wheeled electric mobility scooter models used by disabled
or old people. This system is composed of a test platform that enables measurement of vehicular velocity, a data acquisi-
tion card, and an interface prepared in the C# program. Using the data acquisition card that was designed, seven quanti-
ties, namely, battery and motor currents, battery and motor terminal voltages, wheel speed in revolution per minute,
and ambient and motor temperatures, were measured instantaneously during the test procedure and transferred to a
computer via a USB device. Using these data that were transferred, motor speed in revolution per minute, torque gener-
ated by the motor, motor shaft power, motor and driver efficiency, instantaneous velocity of the vehicle, and total dis-
tance covered information obtained from the moment the vehicle began to be used were computed in real time
throughout the experiment in the interface prepared in the C#, and their graphs were drawn and recorded. Thus, the
faults in the battery, motor, or driver of electric mobility scooters became easily detectable in tests conducted under var-
ious conditions such as different ambient temperatures, different user weights, and roads with different slopes.
Keywords
Electric mobility scooter, wheelchair, data acquisition card, fault detection, measurement, microcontroller
Date received: 2 January 2019; accepted: 10 June 2019
Introduction
Many people encounter diseases or accidents in their
daily lives that occur beyond their control and confine
them to a wheelchair throughout their lives. Some peo-
ple, however, are walking-disabled congenitally.
Therefore, wheelchairs, whether battery-operated or
not, are indispensable components of their lives.
Today, a wide range of power wheelchair models are
produced for disabled people who want to use power
wheelchairs as long as they can afford. Those with dou-
ble motor are called battery-operated wheelchair. The
ones with a single motor are called electric mobility
scooters. But, the basic components in both are the
same.
Different power choices are offered depending on
conditions of use, and there are battery-operated wheel-
chair models that can bring their users to an upright
position on the vehicle.
1
However, no matter in what
models and powers these wheelchairs are produced, the
fundamental components of these vehicles are batteries,
motors, and drivers. When producers of such vehicles
are investigated, it is seen that they generally obtain
batteries, motors, and drivers from different firms, then
mount them on a chassis and offer them for sale.
However, when a fault occurs in the vehicle, they try to
detect the fault in ways that are not much technical.
Faults that arise in such vehicles are not always in
the form of a failure of the motor to revolve or the bat-
tery’s failure to be charged up. The decreases observed
in motor and driver efficiency in the course of time,
very quick heating of the motor, quicker-than-normal
discharging of the battery, and accordingly the vehicle’s
covering a shorter distance at full charge are among
possible faults. It is not possible to determine driver
efficiency by measuring only motor current or battery
current, nor is it possible to determine at only room
temperatures the faults that present themselves at very
high or very low temperatures. Neither is it an
1
Department of Electrical and Electronics Engineering, Faculty of
Engineering and Natural Sciences, Konya Technical University, Konya,
Turkey
2
Department of Electronic and Automation, Vocational School of
Technical Sciences, Konya Technical University, Konya, Turkey
Corresponding author:
Fatih Alpaslan Kazan, Department of Electronic and Automation,
Vocational School of Technical Sciences, Konya Technical University,
Ardıclı Mah. Rauf Orbay Cad. 42250, Selcuklu, Konya, Turkey.
Email: fakazan@ktun.edu.tr
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License
(http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without
further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/
open-access-at-sage).
appropriate method of fault detection to take the
motor thought to be faulty and test it on another vehi-
cle. When all these are taken into consideration, one
needs to ensure that all the components of the vehicle
are in interaction with one another, and they need to
be evaluated as a whole. In order to ensure this, battery
and motor currents, battery and motor terminal vol-
tages, motor speed, motor and ambient temperatures,
the torque generated by the motor, the instantaneous
velocity of the motor, and the route information col-
lected from the moment the vehicle began to be used
need to be collected, transferred to a computer environ-
ment, and analyzed so that the true source of the fault
can be determined.
There is a study in which some of these data were
taken in electric vehicles and transferred to a database.
2
However, measurement of velocity using magnets and
reed switch placed within the wheel is not a proper
method of measurement for velocities where the num-
ber of revolutions per second is way below one because
this will extend the calculation range of other para-
meters computed relative to velocity and hence lead to
different frequencies of data collection at different velo-
city stages. This will in turn give rise to an error in the
measurement of energy transferred by the battery to
the motor.
No study has been encountered among the publica-
tions concerning these vehicles about inclusion of
wheelchair parameters such as center of gravity,
moment of inertia, and velocity and torque constants
of the motor.
3
Likewise, when publications in this
regard are examined, it is seen that attempts have been
made to find various solutions for amputated people
(people without arms and legs). For example, control
of the wheelchair with a movement of the head,
4–7
con-
trol with the eyes,
8–12
control with voice command,
13–16
and control with movements of the mouth
17
are among
some of these. There are also studies where passenger
characteristics have been evaluated,
18
human factor has
been investigated,
19
the moment of inertia of the vehicle
has been measured experimentally,
20
vibration has been
investigated under different road conditions via nine
axes sensor placed under the seat,
21
and mechanical
and electrical impedance of the vehicle has been mea-
sured using sensors.
22
However, no study has been
found where a complete system has been designed to
detect faults in battery-operated wheelchairs or to test
these vehicles.
In this study, which was conducted to meet this
demand, a test setup was designed for electric mobility
scooter models used by disabled or old people. In this
setup, seven quantities, namely, battery and motor cur-
rents, battery and motor terminal voltages, wheel speed,
and ambient and motor temperatures, were measured
instantaneously during the procedure and transferred
to a computer via a USB. Then, using these data that
were transferred, motor speed, torque generated by the
motor, motor shaft power, motor and driver efficiency,
instantaneous velocity of the vehicle, and total distance
covered information obtained from the moment the
vehicle began to be used were computed in real time
throughout the experiment in the interface prepared in
the C#, and their graphs were drawn and recorded. All
the data that were measured and calculated can also be
monitored instantaneously at the same time. If the
motor current and consequently the motor temperature
increase abnormally, the system generates an audible
error warning. When the graphs of the measured para-
meters are evaluated by an experienced technical staff,
the cause of the fault can be determined in detail.
The designed system and its components
First, a platform was designed in the setup to determine
the velocity of the vehicle. Then, sensors required for
the measurement of currents and voltages and a card
for reading them were designed. After the designing of
the data acquisition card, which would read the quanti-
ties that were planned to be measured and send them
to the computer, an interface was designed that would
enable them to be displayed, and their graphs to be
drawn and recorded on the computer. All these compo-
nents of the system are presented in detail below.
Preparation of the test platform to be used in velocity
measurement
First of all, it is necessary to design a mechanical setup
that will allow to measure the speed of all battery-
powered vehicles in the laboratory. Therefore, a very
simple and efficient test platform to be used for speed
measurement was designed (Figure 1).
To prepare the test platform, two cylinders were
mounted on an iron shape using roller bearings. Then,
an incremental encoder that generates 1000 pulses per
revolution was mounted onto this setup to measure
velocity. The gap between the wheels was designed as
to be adjustable in order for it to fit in with different
wheel diameters. Thus, it was ensured that the velocity
Figure 1. Test platform designed to test the electric vehicle in
the laboratory environment.
Akkaya and Kazan 1435
information of the vehicle and the motor was accessible
in vehicles that have different wheel diameters.
A simple circuit board was designed for use in testing
the designed test platform and speed measurement algo-
rithm. In this board, in which PIC18F4550 was used,
the frequency of the signal, velocity of the motor and
the wheel, instantaneous velocity information of the
vehicle, and the distance covered until the end of the
test were calculated using the signals in the encoder out-
put, and they were written on the LCD screen. The cir-
cumference of the cylinder to which the encoder was
attached, the circumference of the wheel, and the gear-
box rate were taken into consideration while the afore-
mentioned information was being calculated.
Some experiments were carried out at different
speeds by changing the reference speed of the motor in
the vehicle. In the experiments, the frequency read from
the oscilloscope and the frequency read from the circuit
board were observed to overlap. The values read from
the board and the oscilloscope in one of these tests are
shown in Figure 2. When the frequency values of
Figure 2 are examined, it is seen that the frequencies
read from the oscilloscope and the designed card are
8.81 kHz.
Measurement of battery and motor currents
To measure the current, the current sensor produced
by the company named Allegro was selected. This sen-
sor (ACS712) is a current sensor that can take measure-
ments bi-directionally up to 30 A without any problems
and is operated at 5 V. These sensors yield analog out-
puts between 0 and 5V in proportion with the current.
This value is within the range of 2.5–5V in positive cur-
rents, whereas it is within the range of 2.5–0 V in nega-
tive currents. Since it can be obtained as a complete
module, it will suffice to connect the output of this sen-
sor directly to any analog input of the card where all
the data will be read. Because battery and motor cur-
rents will be measured, two sensor modules of this type
will be used.
Measurement of battery and motor voltages
To measure the voltage, the voltage sensor produced by
the company named LEM was selected. Due to this sen-
sor, both insulation is provided and the effect of
temperature is minimized. This sensor, which can mea-
sure AC and DC voltages and require symmetrical feed-
ing, yields an output current that varies by the
magnitude of the voltage in its input. The voltage at the
resistor terminals connected to the undervoltage side of
this sensor can be connected to any analog input of the
microcontroller to be used so that the voltage at the
sensor input can be easily measured. Typical connection
diagram of this sensor is shown in Figure 3.
As can be understood from Figure 3, it is necessary to
determine the measuring range in the voltage sensor and
to design a circuit appropriate for it. Since the voltage
level that was intended to be measured in this study would
never exceed 30 V, the resistance value to be connected to
the high voltage side of the sensor was determined accord-
ingly. In addition, since the voltage sensor needed symme-
trical feeding, it was deemed a better choice to design the
circuit to be used for measuring voltage as a separate card.
Two such sensors were used to read the voltages on the
battery and motor terminals. The electronic card designed
and produced taking into account the aforementioned
considerations is shown in Figure 4.
The temperature sensors produced by the company,
Dallas Semiconductor, were used to measure the test
environment temperature and motor surface
temperature.
Data acquisition card
An electronic card is designed to read two voltages, two
currents, two temperatures, and one speed information.
Figure 2. A view of the velocity measurement test conducted
using the incremental encoder.
Figure 3. Typical connection diagram of the LV 25-P voltage
sensor.
Figure 4. The circuit card designed and implemented to
measure voltage.
1436 Measurement and Control 52(9-10)
The card will allow the data to be viewed both on the
LCD screen and transferred to the computer via USB
in real time. PIC18F4550 was chosen as the microcon-
troller, and it is operated at 48 MHz. The current and
voltage values were determined by sampling 1000 times.
The temperature measurement was made instanta-
neously outside the current and voltage loops. Timer
interrupt of the microcontroller was used to measure
velocity. According to this number of sampling, the
time needed for all the data to be read, evaluated, and
transferred to the computer was 375 ms. The electronic
card that was designed and implemented to perform
these procedures and the flow diagram that summariz-
ing the measurement algorithm of the microcontroller
are given in Figures 5 and 6, respectively.
Data reading interface program
An interface was designed on Visual C# program to
receive the data to be sent by the data acquisition card,
display them on the computer, store them, and get some
necessary data to be drawn on the screen graphically
and simultaneously. This system to be implemented
needs to be designed in accordance with models that
have different powers, different wheel circumferences,
or different gearbox rates. The interface section involv-
ing the parameters that need to be entered in the design
is given in Figure 7.
In the meantime, battery voltage warning level can
be determined to protect the battery from deep dis-
charge or to observe the process until the battery vol-
tage drops to a certain value. When the battery voltage
drops to this value that was entered, the operator is
warned with a voice warning. In addition, a permissible
maximum motor temperature section was added in
order to prevent the motor from overheating or to
observe the process until the temperature reaches a cer-
tain value. Moreover, it was enabled to enter the weight
information of the person who sat on the vehicle during
Figure 5. The data acquisition card designed and implemented
to read and transfer the data to the computer.
Figure 6. The flow diagram summarizing the measurement
algorithm of the microcontroller.
Figure 7. The interface section involving the parameters that
need to be entered in the design.
Akkaya and Kazan 1437
the experiment so as to investigate the effect of user
weight on the parameters measured. A torque sensor
needs to be used to be able to calculate the torque of
the motor and some parameters connected with it.
However, the motor is directly connected to the gear-
box in these vehicles. Therefore, placing a torque sensor
between the motor and the gearbox is quite a tiresome
and unnecessary operation. Using the torque constant
in place of it is much more practical method. Because
permanent magnet direct current (PMDC) motors are
used in these vehicles. The torque generated by these
motors is equal to the result of the multiplication of
armature current by torque constant. Therefore, it will
suffice to multiply the instantaneously measured motor
current by the torque constant known beforehand to
calculate the torque that the motor will generate. For
this reason, the torque constant value of the motor on
the vehicle was added to the parameters to be entered.
Knowing where the current and voltage sensors are
connected in the designed system will be useful in
explaining the method of calculating some parameters
in the interface. An explanatory schema concerning this
is shown in Figure 8.
In the interface designed, the motor speed, the tor-
que generated by the motor, the shaft power of the
motor, motor and driver efficiency, the instantaneous
velocity of the motor, and the total distance covered
information from the moment the vehicle began to be
used are calculated, as it was stated before, by seven
quantities measured instantaneously throughout the
test process. Since the motor speed plays a key role in
the calculation of the other parameters, first this quan-
tity needs to be calculated. However, in order to be able
to calculate the motor speed, firstly wheel speed needs
to be calculated. The wheel speed is:
Nw=pu60 Cs
Cw
ð1Þ
In the equation, Nwindicates the wheel speed (r/min),
puindicates the number of pulses generated by the enco-
der in 1 s, Csindicates the circumference of the cylinder
to which the encoder is attached (cm), and Cwindicates
the circumference of the wheel (cm). When the gearbox
rate (pg) is taken into consideration, the motor speed
(Nm)is
Nm=Nwpgð2Þ
If it is remembered that the encoder used generates
1000 pulses at one revolution, the one-thousandth of
the total number of pulses that the encoder will gener-
ate in a second will be equal to the number of revolu-
tions in a second of the cylinder to which the encoder is
attached (Ns). Therefore, in order to calculate the total
current distance covered by the vehicle (Xt), first the
distance it covers in a second (Xs) needs to be calcu-
lated. According to this, the distance that the vehicle
covers in meters in 1 s is calculated as in equation (3).
Xs=NsCs
100 ð3Þ
Current total distance covered is obtained by adding
the distance covered in a second to the total distance
covered until then (X(t1)). The total distance updated
every second is calculated as in equation (4).
Xt=X(t1) +Xsð4Þ
Consequently, the instantaneous velocity of the vehi-
cle in km/h is
V=Xs3:6ð5Þ
Since the motors used in such vehicles are PMDC
motors, torque constant (Kt) and motor current (Imot)
are enough to calculate the torque generated by the
motor (T). According to this, the torque is
T=KtImot ð6Þ
The power flow diagram for situations when electric
equipment other than the motor and the driver (head-
lights, horn, right/left indicators, etc.) on the wheelchair
is not used is shown in Figure 9.
As can be understood from the power flow diagram,
in order to be able to calculate motor efficiency (hmot)
and driver efficiency (hd) in percentages, first the power
drawn from the battery (Pb), the power drawn by the
motor (Pmot), the power consumed by the driver (Pd),
and shaft power of the motor (Pshf) need to be calcu-
lated. These calculations, which are made ignoring the
power loss arising from friction in the gearbox are given
in equations (7)–(10).
Pb=IbVbð7Þ
Pmot =Imot Vmot ð8Þ
Pd=PbPmot ð9Þ
Pshf =TNm2p
60 ð10Þ
In the equation, Ibindicates battery current (A), Vb
battery terminal voltage (V), and Vmot motor terminal
voltage (V). According to this, motor and driver effi-
ciencies are calculated as below.
Figure 8. Positions of current and voltage sensors in the
designed system.
1438 Measurement and Control 52(9-10)
hmot =100 Pshf
Pmot
ð11Þ
hd=100 Pmot
Pb
ð12Þ
All these data measured and calculated are both
recorded instantaneously in the relevant section in the
interface and at the same time registered in a table cre-
ated in the interface so as to allow examining in the
future. All the data in this table can be automatically
transferred to Excel if desired. The flow diagram of the
algorithm used in the designed interface is given in
Figure 10.
All the data that were measured and calculated are
shown in the interface until the next data arrive. The
screenshot received from this section of the interface
during a sample test is given in Figure 11.
Graphic demonstration was also resorted in order to
better see the change in the same quantity during the
test and examine it. Ten of the 14 quantities measured
and calculated were grouped among themselves and
Figure 9. Power flow diagram for cases when electric equipment other than the motor and the driver is not used.
Figure 10. The flow diagram of the algorithm used in the designed interface.
Akkaya and Kazan 1439
presented in six graphs. In these graphs, which were
drawn simultaneously, the data are on the yaxis
whereas the data numbers are on the xaxis. The
screenshot of the designed interface program before it
is connected to the data acquisition card is given in
Figure 12. Since graphs begin to be drawn after the
data acquisition card is connected to the computer, no
graphs can be seen in Figure 12.
Testing of the designed system on an
electric mobility scooter
The efficiency of the designed system was tested on an
electric mobility scooter. The scooter used in the test
meets the needed electric energy from two batteries
connected in series with 12 V-33 Ah values. The PMDC
motor on the scooter is 24 V and has a shaft power of
750 W. The operating voltage of the driver that enables
control of the scooter is within the range of 16–28 V.
The driver drives the motor with a PWM signal of
20 kHz. The velocity of the scooter can be adjusted as
desired with a potentiometer under the control of the
user. Pictures taken during the testing of the system are
shown in Figure 13.
Loads having different weights were placed on the
vehicle seat to be able to better see the changes in the
current and other parameters during the test. The
weight of each load was between 20 and 35 kg. These
loads were loaded using different numbers and combi-
nations. The maximum value of the load on the vehicle
was 230 kg. Placement of the loads on the seat was per-
formed while the vehicle on the test platform was run-
ning at maximum speed.
The screenshot taken during the test procedure is
given in Figure 14. However, the screenshot after the
data were transferred to Excel is given in Figure 15.
Graphs can be drawn using different programs for a
detailed examination and analysis after the data have
been transferred to Excel. Since all the data are trans-
ferred to Excel in different columns according to data
number (Figure 15), the graphs of the desired data can
be drawn differently from the groupings in the
Figure 11. The section where all the values that were measured and calculated are shown until the next data arrive.
Figure 12. The screenshot of the designed interface program before it is connected to the data acquisition card.
1440 Measurement and Control 52(9-10)
interface. To demonstrate this, various graphs were
drawn in MATLAB using the data in Figure 15. For
example, the curve belonging to the battery and motor
currents drawn using the transferred data are shown in
Figure 16. When this curve is examined, it is seen that
it is exactly the same as the current graph seen in the
interface in Figure 14.
A close look at Figure 14 indicates that the currents
and the voltages are given in different graphs in the
designed interface. However, changes in the currents
and voltages can be drawn in the same graph using
the transferred data. Therefore, the effect of sharp
increases or decreases in the current drawn from the
battery on the battery terminal voltage can be easily
examined. The graph drawn to this end is given in
Figure 17.
Dimensions of the graphs were kept constant in
order to render the designed interface as clear and use-
ful as possible. However, graphic change in any quan-
tity at a certain phase of the test can be easily examined
thanks to the transferred data. To illustrate this, the
variation of the motor shaft power curve in Figure 14 is
shown in Figure 18 by redrawing the variation between
the data 300 and 1200.
Figure 13. Pictures of the designed system taken during its testing on an electric mobility scooter: (a) the cards designed and the
computer to which the data were transferred and (b) the vehicle on the test platform.
Figure 14. The screenshot of the designed interface program during a sample test procedure.
Akkaya and Kazan 1441
Quantities that were not calculated in the interface
or whose graphs were not drawn can be easily calcu-
lated using the data that were transferred. For example,
the relationship between motor voltage and motor
velocity during the acceleration of the vehicle can be
observed easily in the graphs drawn using the same
data number range. Or, when the load on the vehicle is
suddenly changed, if one is to make a detailed analysis
of the reaction of the vehicle velocity to that load, it
will be sufficient to have graphs of user weight and
velocity belonging to the relevant data range. For
example, power losses on the motor and the driver were
not calculated in the interface. Therefore, no graph of
it was drawn concerning their change during the test.
However, this is possible thanks to the data transferred
to Excel. By subtracting the motor shaft power from
the motor input power, the lost power in the motor can
be calculated. If the power lost in the driver is to be cal-
culated, it can be calculated by subtracting the power
drawn by the motor from the power drawn from the
battery. If total loss is desired, it is sufficient to sum
motor and driver losses. The graph of these losses is
given in Figure 19.
Conclusion
In this study, a test setup was designed aimed at electric
mobility scooter models used by disabled or old people.
Figure 15. The screenshot of the designed interface program after the data were transferred to Excel.
Figure 16. The changes in battery and motor currents
throughout the test (drawn using the data transferred to Excel).
Figure 17. The changes in the battery current and voltage
throughout the test (drawn using the data transferred to Excel).
1442 Measurement and Control 52(9-10)
In this system, which was implemented at a very low
cost, seven quantities, namely, battery and motor cur-
rents, battery and motor terminal voltages, wheel speed,
and ambient and motor temperatures, were measured
instantaneously using the designed data acquisition
card and transferred to computer environment via a
USB. Using these data that were transferred, motor
speed, torque generated by the motor, motor shaft
power, motor and driver efficiency, instantaneous velo-
city of the vehicle, and total distance covered informa-
tion obtained from the moment the vehicle began to be
used were computed in real time throughout the experi-
ment, and their graphs were drawn and recorded. Using
this design, the following can be examined in the electric
mobility scooter models for disabled people:
Current and voltage changes in the battery and
the motor at different loads and speeds.
The effect of user weight on the distance to be
covered by the vehicle.
The behavior of the vehicle on roads with differ-
ent slopes by bringing the test platform to
desired slope (current, shaft power, power losses,
torque, velocity, distance covered).
The effect of user weight on the heating of the
motor and motor/driver efficiency.
The effect of ambient temperature on motor
heating.
The effects on currents of user’s bending forth or
back or changes in center of gravity due to vari-
ous reasons.
In the event that the motor current and consequently
the motor temperature increase abnormally, the system
gives an audible fault warning. The graphs of the mea-
sured parameters can be evaluated by an experienced
technician and the cause of the fault can be determined
in more detail. In addition, in battery-operated wheel-
chair, each motor can be tested in turn and fault detec-
tion tests can be conducted concerning the motors of
those models.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article: This study was part of the project supported by Selcxuk
University Scientific Research Projects Coordination Office.
The project number is 17101008.
ORCID iD
Fatih Alpaslan Kazan https://orcid.org/0000-0002-5461-
0117
References
1. Putra RH, Rahman AGW, Ningrum ES, et al. Design
and stress analysis on electric standing wheelchair. In:
International electronics symposium on (IEEE) engineer-
ing technology and applications (IES-ETA), Surabaya,
Indonesia, 26–27 September 2017.
2. Doruk A, BulusxHN, Moralar A, et al. Tracing of velo-
city, battery and temperature values of electric vehicles
using physical programing platforms. Electron J Voc Coll
2015; 5(4): 48–56.
3. Chen X, Chase JG, Wolm P, et al. System identification
and modelling of front wheel drive electric wheelchairs.
IFAC P Vol 2008; 41(2): 3076–3081.
4. Yokota S, Hashimoto H, Ohyama Y, et al. Study on
human body motion interface. Paper presented at the
ICCAS-SICE, Fukuoka, Japan, 18–21 August 2009.
5. Marins G, Carvalho D, Marcato A, et al. Development
of a control system for electric wheelchairs based on head
movements. Paper presented at the intelligent systems
conference (Intellisys), London, 7–8 September 2017.
Figure 18. The change in the motor shaft power between the
300th and 1200th data (drawn using the data transferred to
Excel).
Figure 19. The changes of power losses on the motor and the
driver throughout the test (drawn using the data transferred to
Excel).
Akkaya and Kazan 1443
6. Deniz O
¨,Su
¨zen AA and Cxetin A. Wheelchair controlled
by head movements. Paper presented at the 4th national
vocational schools social and technical sciences congress,
Burdur, Turkey, 11–13 May 2017.
7. Nasif S and Khan MAG. Wireless head gesture con-
trolled wheel chair for disable persons. Paper presented
at the 2017 IEEE region 10 humanitarian technology con-
ference (R10-HTC), Dhaka, Bangladesh, 21–23 Decem-
ber 2017.
8. Gajwani PS and Chhabria SA. Eye motion tracking
for wheelchair control. Int J Inf Tech Decis 2010; 2(2):
185–187.
9. Arai K and Mardiyanto R. Eyes based eletric wheel chair
control system. Int J Adv Computer Sci and Appl
(IJACSA) 2011; 2(12): 98–105.
10. Nguyen QX and Jo S. Electric wheelchair control using
head pose free eye-gaze tracker. Electron Lett 2012;
48(13): 750–752.
11. Jain M, Puri S and Unishree S. Eyeball motion controlled
wheelchair using ir sensors. World Acad Sci Eng Technol
Int J Comput Electr Autom Control Inf Eng 2015; 9(4):
906–909.
12. Sharma J, Anbarasu M, Chakraborty C, et al. Iris move-
ment based wheel chair control using raspberry pi-a state
of art. Paper presented at the 2017 innovations in power
and advanced computing technologies (I-PACT), Vellore,
India, 21–22 April 2017.
13. S
ˇkraba A, Stojanovic
´R, Zupan A, et al. Speech-con-
trolled cloud-based wheelchair platform for disabled per-
sons. Microprocess Microsy 2015; 39(8): 819–828.
14. Ghule P, Bhalerao M, Chile R, et al. Wheelchair control
using speech recognition. Paper presented at the 9th inter-
national conference on contemporary computing (IC3),
Noida, India, 11–13 August 2016.
15. Chauhan R, Jain Y, Agarwal H, et al. Study of imple-
mentation of voice controlled wheelchair. Paper presented
at the 3rd international advanced computing and communi-
cation systems (ICACCS), Coimbatore, India, 22–23 Jan-
uary 2016.
16. Wang D and Yu H. Development of the control system
of a voice-operated wheelchair with multi-posture charac-
teristics. Paper presented at the 2017 2nd Asia-Pacific con-
ference on intelligent robot systems (ACIRS), Wuhan,
China, 16–18 June 2017.
17. Ju J, Shin Y and Kim E. Intelligent wheelchair using head
tilt and mouth shape. Electron Lett 2009; 45(17): 873–
875.
18. Sawabe T, Okajima T, Kanbara M, et al. Evaluating pas-
senger characteristics for ride comfort in autonomous
wheelchairs. Paper presented at 2017 IEEE 20th interna-
tional conference on the intelligent transportation systems
(ITSC), Yokohama, Japan, 16–19 October 2017.
19. Hashimoto N, Tomita K, Boyali A, et al. Experimental
study of the human factors when riding an automated
wheelchair: supervision and acceptability of the auto-
mated system. IET Intell Transp Sy 2018; 12(3): 236–241.
20. Wang H, Grindle GG, Connor S, et al. An experimental
method for measuring the moment of inertia of an electric
power wheelchair. Paper presented at the 2007 29th annual
international conference of the IEEE engineering in medi-
cine and biology society, Lyon, 22–26 August 2007.
21. Wang T, Kaneko J and Kojima K. Study on relevance
between electric wheelchair riding comfort and user expo-
sure to whole-body vibration. Paper presented at the 2017
IEEE 6th global conference on consumer electronics
(GCCE), Nagoya, Japan, 24–27 October 2017.
22. Hondori HM, Trung PQ and Shih-Fu L. Simultaneous
sensing and actuating for path condition monitoring of a
power wheel chair. Paper presented at the 2013 first RSI/
ISM international conference on robotics and mechatronics
(ICRoM), Tehran, Iran, 13–15 February 2013.
1444 Measurement and Control 52(9-10)
... A system that enablers such disabled vehicles to be tested in the laboratory was previously introduced by the authors [46]. In this test system, battery's and motor's currents (Ib and Im), motor's and battery's voltages (Vm and Vb), temperatures of motor and ambient (Tm and Ta), and vehicle speed (v) were measured instantly and then sent to the computer. ...
... In addition, using these data, the unmeasured quantities such as the speed of the motor (Nm), the power drawn from the battery (Pb), the motor's shaft power (Ps), the torque produced by the motor (Τ), and the motor and driver efficiencies (Effm and Effd) were also calculated instantly and plotted. Details of all these calculations were given in detail in [46]. The principle diagram of the new system with PMDC motor is given in Fig. 1. ...
... These are parameters section to be entered, measured and calculated values section, table section, and graphic section. How other quantities obtained using the measured data are calculated, is explained in detail in the study previously introduced by the authors in [46]. Therefore, it will not be explained here again. ...
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