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Fuzzy Logic-based Maximum Power Point Tracking Solar Battery Charge

Controller With Backup Stand-by AC Generator

Article in Indonesian Journal of Electrical Engineering and Computer Science · October 2019
DOI: 10.11591/ijeecs.v16.i1.pp136-146

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Indonesian Journal of Electrical Engineering and Computer Science
Vol. 16, No. 1, October 2019, pp. 136~146
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v16.i1.pp136-146  136

Fuzzy logic-based maximum power point tracking solar battery

charge controller with backup stand-by AC generator

Gilfred Allen M. Madrigal1, Kristin Gail Cuevas2, Vivien Hora3,

Kristine Mae Jimenez4, John Niño Manato5, Mary Joy Porlaje6, Benedicto Fortaleza7
1,2,3,4,5,6
Electronics Engineering Department, Technological University of the Philippines, Philippines
7
Mechanical Engineering Department, Technological University of the Philippines, Philippines

Article Info ABSTRACT

Article history: This paper presents a Fuzzy-based Maximum Power Point Tracking Solar
Battery Charge Controller with backup stand-by AC generator. This study is
Received Jan 11, 2019 developed to provide a maximum power point tracking battery charge
Revised Apr 14, 2019 controller using fuzzy logic algorithm for isolated areas that uses solar panels
Accepted May 9, 2019 and AC generators. Fuzzy Logic Toolbox in MATLAB and Arduino IDE
were used in implementing fuzzy logic algorithm. Fuzzy logic is a
mathematical system where something can be represented in continuous
Keywords: values between 0 and 1. It basically represents systems based on human
reasoning. The hardware comprises of four components – the switched mode
Buck-boost converter power supply, the source switching circuit, buck-boost converter and the
Charge controller diversion load controller. The pre-testing conducted based on the
Fuzzy logic methodology indicates that the proposed charge controller is efficient in
Maximum power point tracking maximizing the input power that enters the charge controller under different
(MPPT) conditions. The current efficiency rate of the charge controller is 96.02%.
The average battery charging time for a fully-discharged 12V Lead-Acid
Battery using AC source, DC source and both AC and DC sources are 2
hours and 30 minutes, 8 hours and 15 minutes and 5 hours and 30 minutes,
respectively, while discharging took 3 hours and 40 minutes with two 30-
watt floodlight load.
Copyright © 2019 Institute of Advanced Engineering and Science.
All rights reserved.

Corresponding Author:
Gilfred Allen M. Madriga,
Electronics Engineering Department,
Technological University of the Philippines,
Ayala Boulevard, Ermita, Manila 1000, Philippines.
Email: gilfredallen_madrigal@tup.edu.ph

1. INTRODUCTION
Electricity is a basic need to every human. However, in some areas in the Philippines weren’t
provided with electricity due to its distance from the main grid. People from these isolated communities tend
to use renewable energy power plants and store the created electricity in batteries. The most common and
easily harvested renewable energy that produces electricity is the solar energy [1].
Charging batteries using available renewable energy will solve the problem of inconveniences
caused by living in isolated areas. However, if overcharging of the batteries occurs, battery performance or
even its lifespan can be reduced. To prevent this, a charge controller is used. To attain the most effective
charging and discharging status of the battery, a fuzzy control strategy is developed [2].
Maximum power point tracking (MPPT) is a method used to extract power from the solar panel
which should be matched with the load or battery for maximum power (the highest point in the Power-
Voltage Curve as shown in Figure 1 will be ensured. It is best used at different solar irradiances. Charging
without an MPPT controller will result to wasted power [3]. Charge control must supply the battery with a
higher voltage than the battery’s rated voltage to overcome the battery’s internal resistance [4].

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Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  137

Figure 1. Power-voltage P-V characteristic curve of the solar panel under varying irradiances (400, 600, 800,
and 1000 W/m2) at 250C

This research study is for the design of a charge controller using MPPT for battery charging. It
controls the electric current that flows from a hybrid AC and DC source to the battery where AC is supplied
by a generator while DC comes from the photovoltaic system. A battery charge controller of a hybrid
renewable energy sources and standby power source from the main grid was done [5]. In a hybrid system, a
solar panel is often included to make up for the erratic factors that affect the harvesting of electricity.
A PV charger using SEPIC converter was proved to be effective in MPPT [5]. This charger needs to
regulate the charging of the battery for better state-of-charge and for the battery to last.
The researchers used MPPT for buck-boost converter based on fuzzy logic. Fuzzy logic controller is
used to manage the output voltage of the buck-boost converter to obtain a desired output [6]. Also, stepping
up the input voltage can be done using Fuzzy algorithm [6]. Using fuzzy control [7], the dependability of the
converter and the power acquired can be enhanced, and its output can result to less overshoots.
Most studies about battery charge controller are implemented with MPPT system. One of these [8]
focused on extracting maximum output power deserting the changes in solar irradiation and atmospheric
temperature using MPPT in PV system. Incremental Conductance [9, 10-11], Perturb and Observe [8-9, 11],
and Ripple Correlation Control [9-10,12] method are the most common algorithms implementing MPPT but
the Fuzzy Logic Algorithm is much better than the said 3 algorithms in terms of stability and high response
rate. Also, MPPT can be integrated in buck-boost converter. MPPT-based buck boost converter that changes
a DC voltage into a higher or lower voltage level than the input voltage efficiently is presented in [13]. A
hybrid PV system and wind power system with Fuzzy controller was studied and proposed to obtain the
required SOC value designed for the charging and discharging state of the battery [14].

2. PROPOSED METHOD
This section details the theoretical knowledge used by the researchers in achieving maximum power.

2.1. Buck-Boost Converter

Numerous studies, journals and published papers are studied and consulted for the design of the
circuit. A DC-DC Buck Boost Converter was designed and implemented using Fuzzy Logic Controller, PI
controller and combined algorithm [15]. Figure 1 shows the schematic diagram of a buck boost converter.
When the switch is on, the current will pass through the MOSFET, but it will be blocked by the diode. This
will result for the current to flow through the inductor, resistor and out of the capacitor. When the switch is
off, the current will flow from the inductor through the resistor, capacitor and diode. This will discharge the
inductor and to be charged again in the next cycle [16-17].
Figure 2 illustrates the operation of the buck boost converter with its input coming from a 150W
monocrystalline solar panel. The voltage produced by the solar panel depends on the intensity of the sunlight
ranging from 0V – 22V. This voltage must be bucked or boosted to meet the charging voltage of the battery
which is 13.8V for their long term capacity [4]. Also, controlling the input voltage of the battery can increase
its lifespan for long term use to attain high SOC and prevent battery life from deteriorating, a proper
charge/discharge control approach must be considered [18].

Fuzzy logic-based maximum power point tracking solar battery charge… (Gilfred Allen M. Madrigal)
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Figure 2. Buck Boost Converter Inverting Topology [15-17]

2.2. Fuzzy Logic

The Fuzzy logic controller (FLC) is a mathematical system that converts a language control strategy
[19] and can be acquired using the values between 0 and 1. It can be very suitable for obtaining the linearity
and time invariance of a system or when the human understanding is different from its model [19].The fuzzy
logic system has four main parts, namely: fuzzification, rule base, inference engine, and defuzzification.
Fuzzification involves converting the input values to linguistic labels. To relate the input values to
the output model properties, if-then rules are formed in the rule base. The rules are combined to form output
control actions which are applied to the inference engine. The inference engine which is carried out by using
Mamdani’s method determines the degree of the fuzzy input with respect to each rule and decides which
rules are to be used according to the input field.
Defuzzification is performed according to the membership function of the output variable. It uses
the center of gravity to compute the output of this fuzzy logic system. This is the most popular
defuzzification method and is commonly used in actual applications. Buck-boost converter input flowchart as
shown in Figure 3.

Figure 3. Buck-Boost Converter Input Flowchart

3. RESEARCH METHOD
The block diagram of the charge controller is shown in Figure 4. It has two sources, the AC source
or the generator and the DC source or the solar panel. The output of the generator is converted into DC from
single-phase AC while the output of the solar panel is fed to the buck boost converter. The output voltage of
the buck boost converter is being monitored by the voltage sensor to become the input of the Arduino which
implements the Maximum Power Point Tracking system using the fuzzy logic algorithm that determines the
duty cycle that is needed for the switching of the MOSFETs in the buck boost converter. The outputs of the

Indonesian J Elec Eng & Comp Sci, Vol. 16, No. 1, October 2019 : 136 - 146
Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  139

AC/DC converter and the buck boost converter are fed to the Source Switching Circuit which controls
whichever output is fed to the load. This circuit is used to switch among the sources based on its availability.
This controller circuit can switch between the DC and AC source. Once the battery is fully charged, the
output of the switching circuit is diverted to a dummy load that prevents damage to the system and
the battery.

3.1. Hardware Development

The hardware of the system is composed of a Switched Mode Power Supply, regulator, Arduino
Mega 2560, Buck Boost Converter, source switching circuit, and diversion load controller. It also includes
fan and LCD. These parts of the system are discussed in this section.

Figure 1. Block diagram of the system

Figure 5 shows the circuits used in the project. (a) Switched Mode Power Supply converts AC
voltage of the generator used into 13.8V DC that is needed to charge the battery, (b) Regulator Circuit steps
down the produced voltage of the SMPS needed by Arduino Mega 2560, (c) Arduino Mega and Uno uploads
the code into the hardware for fuzzy logic and diversion load controller respectively, (d) Buck Boost
Converter Circuit steps down or steps up the input DC voltage into 13.8V, (e) Source Switching Circuit
switches the default DC Source into AC Source whenever the power produced by the Solar Panel is
insufficient for charging and (f) Diversion Load Controller Circuit prevents overcharging and damaging of
the battery.

Figure 5. Devices of the Project: (a) Switched Mode Power Supply, (b) Regulator Circuit, (c) Arduino, (d)
Buck Boost Converter Circuit, (e) Source Switching Circuit and (f) Diversion Load Controller Circuit

Fuzzy logic-based maximum power point tracking solar battery charge… (Gilfred Allen M. Madrigal)
140  ISSN: 2502-4752

3.2. Fuzzy Inference Model for MPPT

The system flowchart of the software is shown in Figure 6. It shows the process of obtaining the
inputs for the fuzzy logic controller. It starts from assigning the value of the reference voltage into that of the
desired voltage output. The output voltage of the buck boost converter is compared to the reference voltage
and if they are equal, it means that the output of the dc-dc converter is already the desired 13.8V to supply to
the battery. If the output voltage is lesser or greater than the reference voltage, the error and change of error is
determined and will be the inputs of the fuzzy logic controller, thus producing the duty cycle to determine the
switching pulses to be delivered to the gate of the MOSFET of the buck converter until the desired output
voltage is achieved.
a. Inputs
Two inputs will undergo fuzzification which are the Error and Change of Error. The Error is defined
as the difference between the reference voltage and the measured output voltage of the buck boost converter;
it is designed ranging from -13.8 to 13.8. The Change of Error is calculated from the difference of the error
and the previous value of error; the change of error does not produce very large values because the error
values are almost the same from its previous values, so it was limited in the range -5 to 5.
b. Output
The output is the duty cycle and is ranged from 0 to 1 representing the 0 to 100 percent duty cycle of
the system.
c. FIS Type
There are several types of membership functions but due to their simple formulas and computational
efficiency, both triangular MFs and trapezoidal MFs have been used extensively, especially in real-time
implementations [20]. Triangular membership function was used in the fuzzy logic toolbox for the inputs and
output and each is assigned to five linguistic variables, namely NB (Negative Big), NS (Negative Small), ZE
(Zero), PS (Positive Small) and PB (Positive Big). The triangular membership function is usually used in
fuzzy logic applications because of its simplicity compared to the other types of membership functions.
d. Defuzzification
The Center of Gravity (CoG) is used in defuzzification. It is also called Center of Area (CoA)
because it is similar in determining the center of gravity in physics and it is the method used to obtain the
duty cycle of the system.

Figure 6. Fuzzy Control input flowchart

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Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  141

The Fuzzy Logic Toolbox in Figure 7 provides functions to model, analyse and simulate systems
based on fuzzy logic. The toolbox lets users design the system using different functions and rules depending
on the needs of the system.
Triangular membership function was used in the fuzzy logic toolbox for the inputs and output and
each is assigned to five linguistic variables, namely NB (Negative Big), NS (Negative Small), ZE (Zero), PS
(Positive Small) and PB (Positive Big).

Figure 7. Fuzzy Logic Toolbox interface

Table 1 consists of the ranges of the five membership functions of Error, Change of Error and Duty
Cycle. Fuzzy rules and membership functions can be established when the weather changes to solve the
MPPT problem. From the five membership functions for each of the inputs, it produces twenty-five rules to
determine the fuzzy outputs as seen on Figure 8. These rules are presented in the MATLAB Fuzzy Logic
Designer as if-then statements.

Table 1. Membership Function Range

Variable NB NS ZE PS PB
Error (V) -13.8 to -6.9 -13.8 to 0 -6.9 to 6.9 0 13.8 6.9 to 13.8
Change of Error (V) -5 to -2.5 -5 to 0 -2.5 to 2.5 0 to 5 2.5 to 5
Duty Cycle (%) 0 to 0.25 0 to 0.5 0.25 to 0.75 0.5 to 1 0.75 to 1

Figure 8. Fuzzy Rule Table

3.3. Testing Procedure

a. Efficiency of the Fuzzy Logic Controller
The Fuzzy Logic Controller is tested by measuring the output of the buck boost converter to check if
the MOSFET will switch on or off using an LED when the Arduino sends a HIGH or LOW to the gate of
the MOSFET.
b. Battery Charge Controller Efficiency
The efficiency of the battery charge controller is measured by the following:
1. Record the values of Irradiance, VMPP, IMPP, VOUT and IOUT of the system.
2. The VOUT column is averaged and then divided by the desired output voltage 13.8 to get the efficiency
of the system.
c. Maximum Power Point Tracking Efficiency
The Maximum Power Point Tracking efficiency is measured by the following:

Fuzzy logic-based maximum power point tracking solar battery charge… (Gilfred Allen M. Madrigal)
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1. Record the expected maximum power and the measured power by multiplying the voltages and current
obtained.
2. Record the expected maximum power and the measured power and measure how big the power loss to
acquire the efficiency.
d. Charging and Discharging Time of the Battery
Measure the time it takes to charge the battery when the AC and DC source are used. The discharge
time is also recorded when the battery is used to power up the flood lights used as load.

3.4. Formulas
The following formulas are used in the project:
a. For Buck-Boost Converter
The most important for designing the Buck-Boost converter are total power loss (1), and its
efficiency (2).

(1)

(2)

where is the total power loss, is the MOSFET conduction loss, is the switching loss, is the
diode conduction loss, is the loss in the ESR of the inductor, is the loss in the ESR of the filter
capacitor, n is the efficiency of the Buck-Boost converter, and is the output power.
b. For Fuzzy Logic Algorithm System
For testing the efficiency of the system, the following formula is used:

( ) (3)

( ) (4)

c. For battery efficiency

The efficiency of the battery charge controller is computed using (5):

(5)

where is the output voltages and is the reference voltage, which is 13.8 V.
d. For Maximum Power Point Tracking Effeciency
The efficiency of the Maximum Power Point Tracking capability of the system is obtained using (6):

(6)

4. RESULTS AND DISCUSSION

This section presents the results of the methodologies done in section 4 of this paper. This includes
presentation of the results of the data gathered.

4.1. Efficiency of the Fuzzy Logic Controller

Table 2 specifies at which voltage does the Fuzzy Logic Algorithm starts the buck or boost mode.
The fuzzy logic controller has a high accuracy in giving switching pulses to the MOSFET of the buck boost
converter. Applying (3) and (4), where ( ) is the average error of the controller for buck,
( ) is the average error for boost, and to is the error on the first trial up to the
tenth trial, the computed average error of the controller is 0.105 for the Buck Mode and 0.215 for the Boost
Mode. Having this small error, means that the Fuzzy Logic Controller can give switching pulses to the buck
boost converter that can make it produce an output voltage close to the reference voltage which is needed by
the battery.

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Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  143

Table 2. Voltages at Buck and Boost Mode

Voltage at Error1 Voltage at Error2
Trial which Buck (|Vref – which Boost (|Vref –
Mode Starts Vbuck|) Mode starts Vboost |)
1 13.94V 0.14 13.45V 0.35
2 13.81V 0.01 13.71V 0.09
3 13.91V 0.11 13.67V 0.13
4 13.89V 0.09 13.76V 0.04
5 13.96V 0.16 13.52V 0.23
6 13.89V 0.09 13.62V 0.18
7 13.84V 0.04 13.64V 0.16
8 13.96V 0.16 13.52V 0.28
9 13.99V 0.19 13.32V 0.48
10 13.86V 0.06 13.59V 0.21
Average 0.105 Average 0.215

4.2. Battery Charge Controller Efficiency

Table 3 has six columns, the trial number, the Irradiance which is the amount of sunlight that strikes
the solar panel, the IMPP and VMPP are the current and voltage from the solar panel that enters the system,
the IOUT and VOUT are the current and voltage that the system produces.
In getting the efficiency of the battery charge controller, 10 trials were made, and using (5), an
efficiency of 94.85% was acquired.

Table 3. Efficiency of Battery Charge Controller

Trial Irradiance Impp (A) Vmpp (V) Iout (A) Vout (V)
1 300 1.44 14.26 1.37 13.1
2 400 1.62 14.57 1.52 12.9
3 550 2.05 14.3 1.84 13
4 880 1.93 12.32 1.75 12.8
5 1000 1.76 11.9 1.63 12.7
6 1200 1.74 15.02 1.69 13
7 1600 2.28 12.71 2.12 12.9
8 1800 0.71 11.9 0.58 12.7
9 1900 1.06 18 1.22 13.8
10 2000 0.98 18.47 1.14 13.9

4.3. Maximum Power Point Tracking Efficiency

The Maximum Power Point Tracking efficiency is computed by comparing the expected input
power and measured input power of the system to determine how big or how small the tracking error is. Each
efficiency is obtained by dividing the PMEASURED by PEXPECTED. In Table 4, 10 trials were only shown
but 28 trials were used in computing the efficiency of the Maximum Power Point capability of the system.
Using (6), efficiency of each trial was computed. After getting the efficiency for each trial, it is averaged to
acquire the total efficiency of the maximum power point tracking capability of the system. 88.235% is the
calculated efficiency of tracking the maximum power.

Table 4. Maximum Power Point Tracking Efficiency

Fuzzy logic-based maximum power point tracking solar battery charge… (Gilfred Allen M. Madrigal)
144  ISSN: 2502-4752

4.4. Charging and Discharging Time of the Battery

Figure 9 illustrates the battery voltage and charging time using different sources with a 30 minutes
interval per sample. In this, the charging duration per source varies greatly with AC source as the shortest,
then AC-DC source, and lastly the DC source. The battery voltage of the AC source rises abruptly while with
the DC source, it rises gradually. Using AC-DC source, there’s a sudden increase of the battery voltage of
when the source switched from DC to AC. The charging current and duration of the different sources is
shown in Figure 10 with samples taken every 30 minutes. Having the shortest time of charging, AC source is
observed that the charging current abruptly decreases at first then gradually decreases. Charging using DC
source took more time due to varying charging current which depends on the time of the day. Furthermore,
on AC-DC source, the charging current on the DC part is decreasing and abruptly increases when
switched to AC.

BATTERY VOLTAGE
13
VOLTAGE (V)

AC
12,5

12
DC
11,5

11
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480
TIME (min.)

DC AC AC-DC

Figure 9. Battery voltage while charging using the different sources

CHARGING CURRENT
5
CURRENT (A)

4
3 AC

2
1
0 DC
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480
TIME (min.)

DC AC AC-DC

Figure 10. Charging current of different sources

Figure 11 displays the battery voltage while discharging with the two 30-watt floodlight. It shows
that on the first 30 minutes, the battery voltage drastically decreases then gradually decreases over time. The
discharging of the battery took 3 hours and 30 minutes. Figure 12 shows the delivered current to the two 30-
watt load. It illustrates that the battery supplies an average current of 3.6475A. The battery took 3 hours and
30 minutes to fully discharge the battery.

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Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  145

BATTERY VOLTAGE
13

VOLTAGE (V)
12,5

12

11,5

11
0 30 60 90 120 150 180 210
Battery voltage TIME (min.)

Figure 11. Battery voltage while discharging

DISCHARGING CURRENT
3,9
3,8
CURRENT (A)

3,7
3,6
3,5
3,4
3,3
3,2
0 30 60 90 120 150 180 210
TIME (min.)
Output Current

Figure 12. Current delivered to the two 30-watt load

5. CONCLUSION
The effectiveness of the Fuzzy Logic Algorithm was observed and found out that it has a high
efficiency rate because it produces an output voltage close to the reference voltage by having a small amount
of error. In charging a 12V Lead-Acid battery using the system, it was determined that the average battery
charging time using AC source, DC source and both AC and DC sources are 2 hours and 30 minutes, 8 hours
and 15 minutes and 5 hours and 30 minutes, respectively, however, discharging it took 3 hours and 40
minutes with two 30-watt floodlight load.

ACKNOWLEDGEMENTS
This study is supported by the University Research Development Services Office of the
Technological University of the Philippines.

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