Real-Time Implementation of BLDC Motor-Based Intelligent Tracking
Control Fed from PV-Array for E-Bike Applications
ESSAMUDIN ALI EBRAHIM
Power Electronics and Energy Conversion Department,
Electronics Research Institute,
Joseph Tito St., Huckstep, Qism El-Nozha, Cairo Governorate, Cairo,
EGYPT
Abstract: - The essential goal of this research is designing and modeling a speed and position tracking system
for driving an electric bike (e-bike) motordrive. This motor is a brushless DC (BLDC) motor as a high-
performance drive. It is supplied from twin electric sources to drive it and charge the storage elements (i.e.,
batteries, super-capacitors, etc.). The first one is a renewable, neat, and clean source photovoltaic (PV) module
and the second one is a pedal generator driven by the rider. The submitted design of the controllers is optimized
to improve the system's dynamic stability. The artificial bee colony (ABC) as an artificial intelligent (AI)
algorithm is suggested for searching the optimal gains of the proposed proportional-integral-derivative (PID)
controllers by reducing the error of its fitness function. The system behavior is studied with that controller
when directly feeding from the PV array with and without batteries. The response of the proposed technique -
against dynamic troubles and PV oscillations such as irradiance- is also verified. Other evolutionary
computational techniques - such as ant colony optimization (ACO) and genetic algorithm (GA)- have been
compared with the behavior of the proposed controller to ensure high efficiency in optimized tuning of PID
gains. Then, the proposed controller that gives a high performance will be executed in real-time by using
OPAL-RT 4510 RT-simulator and rapid control prototyping.
Key-Words: - Artificial Bee colony (ABC), Brushless DC motor (BLDC), Electric Bicycle (E-bike), Intelligent
Control, Photo-Voltaic (PV).
1 Introduction
The interest in various different types of vehicles
has grown over the last two decades, with the aim of
reducing environmental pollution, achieving a
smooth flow of traffic, and reducing the
transportation cost. Electric powertrains and trams
are environmentally friendly but are not economical
for long driving distances compared with fossil-fuel
engines. Despite the advantages of electric hybrid
vehicles, which overcome the environmental
problems, they cause traffic jams. Vehicles with
small size, which do not occupy a large space, are
preferably used to overcome the traffic congestion.
In recent years, people have returned once again to
the bicycle – especially electric bikes – as light
electric vehicles in most places for all countries, [1],
[2].
E-bikes are human-powered and require some
level of physical traction effort. The market of e-
bikes are rapidly growing, [3]. The electric bike has
many advantages such as: getting fit, saving money
and time, going faster, and further, having fun, and
being environmentally friendly, [4]. But, up to now,
in our Country (Egypt), there are some constraints
for e-bikes to be the first local transportation. Such
constraints are: high cost, limited speed, storage-
element charging, control techniques, etc., [5].
Several types of research are introduced to
develop the production of those bikes and reduce the
cost. Some researchers try to make them as smart
and intelligent as, [6], [7], [8]. Also, there are
several efforts to develop the charging processes for
the batteries. Some proposed wired charging as in,
[9], [10]. In addition, wireless or inductive power
transfer (IPT) for battery charging is introduced as a
more comfortable and safer method as in, [11], [12],
[13]. Others suggested a renewable and green
energy resource for the charging process such as,
[12], [14]. Other researchers are interested in
regenerative braking as in, [15]. But, on the other
hand, several efforts from researchers went hand in
hand with the most important part of the system
which is the electric-motor drive. A review of the
electric-bike driving systems concluded that the
following motors can be used such as: permanent-
magnet synchronous (brushless DC and AC), and
Received: September 27, 2022. Revised: September 18, 2023. Accepted: Ocotber 21, 2023. Published: November 20, 2023.
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DOI: 10.37394/232016.2023.18.28
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E-ISSN: 2224-350X
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switched reluctance motors (3 or 4- phases), [16],
[17], [18], [19], [20], [21].
Among all, the Brushless DC (BLDC) motor is
suitable because it has many features. Its
performance is high, more durable, and energy-
saving with high torque, [22]. So, many researchers
are introduced to optimize all variables of that host
drive by proposing several control techniques such
as in, [23], [24].
The gains of the classical PID controllers can be
designed by means of classical methods such as the
Ziegler-Nichols (ZN) formula, Root locus method,
pole placement technique, and Routh- Hurwitz
criterion. Most of these techniques failed to
optimize the performance of this drive. So,
intelligent control techniques are suitable for
achieving optimality.
Now, artificial intelligence (AI) algorithms such
as genetic algorithm (GA), Particle Swarm
Optimization (PSO), or germ of intelligence (as
bacteria foraging (BF) and ant colony optimization
(ACO)) algorithms have been considered new
techniques for optimizing PID-controller gains,
[25], [26], [27]. Another artificial intelligence
technique was proposed in 2005, known as the
Artificial Bee Colony (ABC), [28].
So, this research proposes an ABC algorithm to
compute the PID controllers’ gains for optimization.
This proposed controller is called ABC-PID. As a
result of repeated incidents of children riding
electric bicycles by themselves, this robust
controller will be proposed in a tracking system for
a bike-like robot and unmanned e-bike. This auto-
bike is driven by a BLDC motor fed from PV as a
neat renewable energy source and controlled by
using this proposed intelligent controller.
A multi-input single output DC/DC converter is
proposed for power management to the drive. Three
DC sources are saved to the motor: PV, DC human-
pedal generator, and storage elements (i.e., batteries
and/or supercapacitor).
The proposed system is modeled and simulated
with the help of the Matlab/Simulink and m-
functions are written as m-files for the ABC
algorithm. Simulation results are demonstrated with
the proposed rig for studying the behavior of the
system. Furthermore, several pre-scribed reference-
speed paths are selected for the robustness test of
that controller against dynamic fluctuation and PV-
irradiance variation. A comparison study is
implemented among other intelligent controllers
such as genetic and ACO algorithms. Then, the
optimal controller will be executed in real-time with
the help of the OPAL-RT 4510 simulator and rapid
control prototyping (RCP).
This manuscript is planned as: Section 1
includes an introduction and Section 2 introduces
the overall proposed system. Section 3 and Section
4 elaborate on the proposed intelligent algorithm
with the simulation results. The RT-implementation
is shown in section 5 followed by the final
conclusions with recommendations in the end
through section 6.
2 The System under Study
Figure 1 demonstrates the system under study. It
implies: a solar supply, converter for power
maximization, inverter, BLDC-host motor with the
controller, and e-bike – with the hybrid human
pedal.
2.1 PV-Array Mathematical Model
The mathematical model of the PV-array can be
computed from those equations, [29], [30].
(1)
The current module will be obtained as:
(2)
The cell current can be determined as:
(3)
Where,
irradiance current (A), the saturation current
of the diode (A), is the voltage of the cell,
module, and array voltages (V), (C),
=1.3806503 (J/K),
the resistance of series and parallel paths (Ω)
are series and parallel cell numbers, and T
is the temperature of the cell (ºK).
2.3 The Host-BLDC Motor Mathematical
Model
The state-space equation is used to represent the
mathematical model of the host BLDC-motor
as, [31]:
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DOI: 10.37394/232016.2023.18.28
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Fig. 1: The proposed system
=.
(4)
The motor's internal torque is determined as:
(5)
The motion dynamic equation is given by:
p (6)
Where, are back emf’s in (V), L is self-
inductance (H), are phase voltages (V),
are phase currents (A), is the load torque
in (N.m), J is the motor inertia (Kg.m²), B is the
friction coefficient (N.m.sec), and The motor
speed (rad/sec).
2.3 Multi-input single-output Boost
Converter
This research uses the boost converter with 3-
voltage supplies: battery and /or supercapacitor,
solar PV, and the DC-pedal generator, [32]:
(7)
Fig. 2: Multi-input single output boost converter
Where, are the converter input and output
respectively, are the duty cycle that maximizes
the output power of the PV generator. The Multi-
input single-output boost converter is presented in
Figure 2.
2.4 Position, Speed, and Current Controller
The position of the rotor for the drive can be
detected by using a position-shaft encoder or can be
estimated by using a sensor-less algorithm and the
position will be differentiated to give the angular
speed of the rotor (ωr). The block diagrams for both
speed and current controllers are demonstrated in
Figure 3 and Figure 4, respectively. The hall-effect
current sensors are used to sense the motor currents,
[33]. The error signals of the stator currents are used
as an input to the proposed optimal controller. Then,
a hysteresis-band current controller is used to
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generate the IGBT switching patterns. This is
clearly shown in Figure 4.
Fig. 3: The proposed speed PID-controller
Fig. 4: Hysteresis current controller of motor
3 ABC-PI Proposed Controller
For proper tracking control, the BLDC motor should
follow the preselected reference speed trajectory.
So, a suitable and robust controller should be used.
The artificial bee colony (ABC) algorithm is used to
optimize the PID-controller gains. This controller is
called ABC-PID. Integral time absolute error
(ITAE) for the speed is used as an objective
function for optimization, where:
dttetITAEJ ))((
0
*
(8)
rr
e
*
(9)
Where,
rr
,
*
are the reference and actual speed.
Minimize J: Subject to the constraint:
maxmin
. As,
represents
dip KKK ,
.
3.1 ABC-Optimization Algorithm
The ABC optimization algorithm is an evolutionary
computation that was proposed by, [34], [35]. It
depends on three groups for bees: employed,
onlookers, and scouts. All contribute towards
randomly searching for food sources with higher
amounts of nectars.
Initialization of ABC
Employed Bee Phase
On-looker Bee phase
Store The Best-Food Source Position
Is
There a Scout Bee in the
Colony?
Is The Termination
Criterion Met?
The Best Solution
Scout Bee phase
Yes
No
Yes
No
Terminate
Fig. 5: ABC-algorithm flowchart, [39], [40]
The flowchart for the ABC algorithm is shown
in Figure 5.
Through initialization, the size number of
population SN and no. of food sources FS are
randomly selected.
FSiXi,....2,1,
is the
solution vector with C-dimension. Where,
][ dip KKKC
. However, the following
equation computes the food source position i for
each cycle of one iteration:
,...),,( 321 iiii XXXX
. According to the
probability value
i
P
, the onlooker group moves
towards the food source
i
X
, where:
FS
kK
i
ifit
fit
P
1
, and the fitness value
i
fit
can
be computed as:
)(1
1
i
iXf
fit
, the value
)( i
Xf
equal to J where J is the objective function
of the system. In this work, J=ITAE can be
computed from Eqns. (8) and (9). The best food
source can be found by the onlooker in the region of
i
X
,
where:
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)(*)1,0( minmin
jjjj XXrandXX inewinew
If the new position achieves a fitness value
better than that obtained so far, the onlooker
moves to it, other, it stays on the old one. This
process is repeated according to the maximum
number of iterations. Finally, the optimization
is achieved.
For more details about ABC, please refer to, [36],
[37], [38], [39], [40].
4 Simulation Results
The MATLAB Software package with both
SIMULINK and M-function are used in simulating
the overall proposed system, [41]. The PV array
consists of two series modules , and a parallel
one for producing this power at 48 V-rated
voltage and rated current 5.3 A. The PV array
supplies 500-W to the BLDC by using Suntech
STP270S-24_S. The overall characteristics and
specifications are illustrated in, [42]. Also, the main
parameters for the e-bike host BLDC motor are
obtained from [43], and they are given in Table 1:
MATLAB/M-file was written to optimize the
gains of the proposed controller that can be defined
as:
S
d
S
i
S
p
dip
dip
KKK
KKK
KKK
CFS ......
),(
222
111
(10)
Where C and FS are the numbers of parameters
optimized and food sources. In this study, D and S
are selected equal to 3 and 10 respectively. Other
ABC- variables are tabulated in Table 2 (Appendix).
Also, the gain parameters for the proposed
algorithm are limited by these values:
}
max
,0{ },
max
,0{ },
max
,0{ d
K
i
K
p
K
And,
1.00 , 500 300,0 d
K
i
K
p
K
The value of the load torque is considered as
(
L
T
= 5 N.m) and the bike is supposed to move
forward, backward, hill-climbing with regeneration,
and in special tracks. So, the motor rotates in four-
quadrant operation and should be a high-
performance drive with robustness. To test the
controller robustness against the dynamic and PV
disturbances, more position and speed tracks
(sinusoidal, step-stairs, etc.) are selected. In
addition, the irradiance of the PV array is randomly
selected and its value ranges from 200 to 1000
(W/m²). The simulation results are firstly obtained
when the motor is directly fed from a solar supply
via a converter and then it is supplied only from the
storage battery.
Table 1. Nameplate data of the BLDC-Motor, [43]
Parameters of Motor
Nominal values
Motor Nominal Power (Watts)
485
Machine Poles
2
DC-Rated Voltage (V)
300
Motor Nominal (rpm)
1500
Constant of the Torque V/(rad/sec))
0.4
Resistance of one Phase (Ω)
0.4
Self-inductance (mH)
13
Figure 6 shows the PV- PV-insolation profile
with time, the PV output voltage, power generated
from the PV array, and finally the change of duty
cycle for the converter when insolation is changed
from 1000 to 200. Also, to show the movement of
the bees towards optimization in 3-D for PID gains,
Figure 7 is illustrated.
The robustness is tested and observed by
tracking several speed trajectories. Figure 8
demonstrates the real and locus speed (a) and torque
(b) for the trapezoidal two-quadrant speed track.
(a) The proposed Irradiance-profile for the PV
(b) Voltage profile of PV- output
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Volume 18, 2023
(c) The power emitted from PV arrays
(d) Converter duty-ratio
Fig. 6: PV- variables (insolation, voltage, power,
and duty ratio)
(a) Initial movement
(b) Through tuning
Fig. 7: PID-Tuning process with ABC
(a) Ref. & actual speed
(b) Ref. & actual torque
Fig. 8: Trapezoidal Track (Speed & Torque)
The speed trajectories or both actual and ref. are
approximately identical although the insolation
profile of the PV-array is changed from 1000 to 200
W/m².
Other speed trajectories are tested such as
square, sinusoidal, ramp, and up-down stairs as
shown in Figure 9, Figure 10, Figure 11, and Figure
12 (a for speed and b for motor internal torque). We
noticed that most tracks for both speed and torque
are approximately in phase (actual and reference)
with a very fast response to any disturbance. This
ensures the robustness of the system drive with that
proposed ABC-PI controller. All previous results
are obtained when the BLDC motor is directly
supplied from the solar arrays. The proposed
controller is also used when the motor is fed from
the battery. The voltage for the converter DC-link
(both actual and reference) is shown in Figure 13.
The response for the step-change is so high with a
very fast time. When the motor is fed directly from
the battery, the performance is improved.
Also, it should compare the proposed intelligent
controller with others such as (GA), (ACO), and
also with the traditional controller (PID).
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(a) Ref. & actual speed
(b) Ref. & actual torque
Fig. 9: Square Track (Speed & Torque)
(a) Ref. & actual speed
(b) Ref. & actual torque
Fig. 10: Sin Track (Speed & Torque)
(a) Ref. & actual speed
(b)
(b) Ref. & actual torque
Fig. 11: Ramp Track (Speed & Torque)
(a) Ref. & actual speed
(b) Ref. & actual torque
Fig. 12: Up-down stairs Track (Speed & Torque)
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Fig. 13: The converter DC-link voltage (act. & ref.)
Fig. 14: A comparison among ABC, ACO, GA, and
ZN-PID Controllers
The GA-PID controller algorithm data is
obtained from [44], and the overall algorithm
parameters are given in Table 3 (Appendix). Also,
the ACO-PID controller algorithm is designed,
modeled, and simulated with the help of, [5], and
the main ACO algorithm parameters are given in
Table 4 (Appendix). In addition, the comparison
includes the traditional ZN-PID controller and
Ziegler-Nichols criteria had the following gains:
02.0,22.13 ,1.30 d
K
i
K
p
K
As shown in Figure 14, the comparison among
all is verified only for square reference prescribed
speed trajectory only as a sample, wherever, this is
considered as a sharp and step-change complex test.
So, it can be easily noticed that both ABC-PID
and ACO-PID controllers follow this sharp speed
trajectory with fast response and minimum
overshoot. But, ABC-PI is the best one. On the other
hand, the GA-PID and traditional PID controllers
take more time to reach a steady state.
Because the ABC-PID controller contributed
towards improving and enhancing the tracking
control of the BLDC motor with an e-bike, it will be
implemented in real time.
5 Real-Time Implementation
The proposed system is implemented in real time to
ensure its ability to be constructed as a physical
system. The OPAL OP4510v is a digital real-time
hardware simulator, [45]. This platform is used with
the help of the HIL controller and data acquisition
OP8660 set, [46], – in the ERI lab. – to test the
proposed system in real time. The overall
experimental rig is shown in Figure 15. All the real
input/ output signals can be checked and recorded
by using a digital oscilloscope. The interface
language – used as compiler and interpreter –
between Matlab/ Simulink and OP4510 RT-
simulator is the RT-LAB software package. All
signals can be output on the analog ports as real.
Because the maximum output for these analog ports
is 16V, all signals should be scaled to reduce their
value to be seen on the oscilloscope. The RT results
will be recorded only for the proposed ABC-PID
controller. The square-wave speed trajectory is
tested to be tracked because this trajectory includes
both positive and negative speeds in both directions.
Also, in this trajectory, the speed is sharply step-
changed from zero to 200 rad/sec in a very short
time. Figure 16 (Ch. A) describes the proposed
insolation profile for the PV- array used to supply
the motor. In the Simulink model, the irradiance was
scaled by 200. So, as shown in Figure 16, the
oscilloscope is set to 200 mV/ div, and 2.5 divisions
means 1000 W/m². Figure 17 shows the proposed
reference and actual speed tracks. As shown, the
motor response is fast and takes a few milliseconds
to reach its steady state reference value with
minimum overshoot. Dependently, Figure 18
describes both reference and real motor torque. As
shown, both actual and reference signals are in
phase and approximately identical with some
harmonics, ripples, or noise.
Fig. 15: Real-time simulator and its controller
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Fig. 16: PV-Irradiance with motor torque
Fig. 17: RT-Speed tracks (Ref. & actual)
Fig. 18: RT-torque signals (actual (D) & ref. (A))
Fig. 19: RT Motor current (A), DC voltage (A)
This is due to the selection of a small sampling time,
the switching rate for the IGBT switching module,
and the hysteresis current controller of the proposed
technique. In addition, for the same speed trajectory,
the stator current of the motor is shown in Figure 19
(Ch. A) and the DC-link voltage is shown in Figure
19 (Ch. D). The current was scaled by 5 and the
voltage by 200.
6 Conclusion
This paper proposed intelligent speed and position
controllers for e-bikes driven by a BLDC motor.
This controller depends on the ABC algorithm as an
optimization technique. The main gains of that
controller are optimized.
A general-purpose software package was
introduced to be used in designing; modeling and
simulating any BLDC drive for an e-bike with
different artificial intelligence (AI) controllers using
the Matlab/Simulink and M-file routines. AI
controllers – that depend on evolutionary
computation such as ABC, GA, and ACO - are
robust and can depend on them especially, in speed
and position tracking of e-bikes. Among those
controllers, ABC and ACO gave good results in pre-
scribed speed-tracking with fast response rather than
GA and ZN-PID controllers. So, the system was
implemented in real time with the ABC-PID
proposed controller. The system performance and
response were improved.
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APPENDIX
Table 2. ABC algorithm parameters, [39], [40]
Parameters 0f ABC
Values
Colony Size numbers =Np
20
Food Sources number ( S=Np/2)
10
Cycles numbers (maximum)
50
Probability of the Threshold
0.75
Parameters Optimized number
3
Table 3. GA –algorithm parameters, [44]
GA-algorithm parameters
Values
Max. generation
100
Population size
50
Cross-over probability (CR)
0.75
Mutation rate
0.1
Table 4. ACO algorithm parameters, [5]
ACO-algorithm parameters
Values
K
5
α
0.2
β
0.6
ρ
0.3
o
q
0.5
o
0.2
Max. no. of iteration
50
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The author contributed in the present research, at all
stages from the formulation of the problem to the
final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The author has no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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WSEAS TRANSACTIONS on POWER SYSTEMS
DOI: 10.37394/232016.2023.18.28
Essamudin Ali Ebrahim
E-ISSN: 2224-350X
281
Volume 18, 2023
