experimental study of battery state of charge effect

VOL. 13, NO. 2, JANUARY 2018
ISSN 1819-6608
ARPN Journal of Engineering and Applied Sciences
©2006-2018 Asian Research Publishing Network (ARPN). All rights reserved.
K. A. Abed, A. Bahgat, M. A. Badr, M. El-Bayoumi and A. A. Ragheb
Mechanical Engineering Department, National Research Centre, Cairo University, Egypt
E-Mail: Mahmoud.A.El.Bayoumi@gmail.com
The effect of battery characteristics on the performance of PV Energy System is experimentally investigated. The
employed system consists of a number of lead acid storage batteries, a set of photovoltaic (PV) panels, a charge controller,
DC/AC inverter and an electrical load. The system also employs a monitoring unit (MU) that consists of voltage and
current sensors and data acquisition cards. The MU was specially developed to monitor energy system performance
parameters; such as PV voltage and current, battery voltage, battery charging and discharging currents and inverter current.
The monitoring unit is controlled through a specially developed, LABVIEW® based, computer program. In the study, the
effect of battery SOC on charging and charging efficiency was investigated. Also, the effect of battery’s loading conditions
on its useful charge was investigated. Finally, the effect of battery SOC and supported load on battery performance was
investigated. Results of the study showed battery’s SOC had a minimal effect on charging efficiency. It also showed that
higher discharge current (load requirements) leads to significant decrease in energy system performance caused by
decrease of battery discharge efficiency by up to 50%. On the other hand, low batteries SOC proved to have insignificant
effect on meeting load demand. The results emphasize the importance of proper selection of batteries size as well as
characteristics including cost, to match load requirements, as those would highly affect renewable energy system’s
performance and feasibility.
Keywords: battery, battery SOC, battery efficiency, renewable energy, PV.
Significant developments in the design, analysis
and installation of PV based renewable energy systems
have been achieved, offering a solution to power problems
of remote sites. Renewable energy systems are either
stand-alone or grid connected; in the case of grid
connection they are considered as fuel saver. A range of
renewable energy systems are mainly based on
Photovoltaic combined with battery storage and supported
by auxiliary petrol generator. Many of those are in
operation for decades.
Although different types of energy storage are
investigated for incorporation with renewable energy
systems, batteries are still the most common storage media
for PV electricity till now. Small and medium PV-battery
stand–alone systems are widely functional for different
remote applications. In these systems, batteries are the
most sensitive equipment, often operating under severe
conditions such as successive charge/discharge and long
periods under deep discharge, which affect its lifetime, [1].
Lead-acid batteries are the most common battery type for
renewable energy storage. The selection of proper number
and size of batteries comprising the battery bank requires
analysis of the battery’s charge and discharge
requirements, the load pattern and the pattern of solar
irradiation in this specific region. As the average daily
energy demand is approximately constant, when solar
radiation decreases due to the weather conditions, less
energy gets supplied by the PV array. Accordingly, the
battery bank would support the load, get discharged and its
SOC will be decreased, which would affect the system
performance. Some believe that, energy losses occur
mostly during battery bank charging, [2] however results
of the current research proved different.
The SOC reflects the balance between energy
stored and energy supplied at any instance and is a good
parameter to evaluate the suitability of the storage size for
the intended application involved. For optimum balance
between daily charge and discharge, the SOC should be
close to the upper limit (SOC = 0.97) at mid-day, and
close to its lower limit (SOC = 0.3) at the end of most
days, reflecting an optimum PV energy time-shift and
demand management, [3].
Wen-Yeau Chang presented a review of
developments in battery SOC estimating methods,
focusing on both mathematical calculations and practical
implementations, [4]. As battery SOC is an important
factor that affects the battery performance, accurate
estimation of SOC can protect battery, prevent over charge
or discharge, and improve battery life. The paper
presented an over view of the four categories of
mathematical methods of estimating SOC such as; Opencircuit voltage, Terminal voltage, Impedance method, and
Coulomb counting method.
Battery operating temperature and efficiency of
charger and inverter also affects the PV system
charge/discharge cycles would significantly affect
batteries performance and lifetime, [5]. As expected,
battery efficiency drops as it ages and it would age faster
when not operated correctly.
A modeling and optimization study of a standalone hybrid energy system was presented by Ghada
Merei et al., [6]. The system consists of a wind turbine,
VOL. 13, NO. 2, JANUARY 2018
ISSN 1819-6608
ARPN Journal of Engineering and Applied Sciences
©2006-2018 Asian Research Publishing Network (ARPN). All rights reserved.
PV modules, batteries and a diesel generator for back-up.
Three types of storage batteries were used in the
simulation study, lithium-ion, lead-acid, and vanadium
redox-flow individually or combined together. In case of
using different battery technologies at the same time, a
battery management system (BMS) is used. The developed
BMS takes into account different battery operating points
and ageing mechanisms. Battery ageing depends on two
main processes: cyclic ageing and float ageing. Cyclic
ageing takes place, while the battery is being charged or
discharged. In this case thermal and mechanical stresses
lead to a deterioration of battery properties. To estimate
the effect of cycling aging, information about the average
SOC is required. Float ageing generally depends on the
state of charge (SOC) and the temperature the battery is
operating at.
Batteries used in PV applications are working in
harsh conditions compared with batteries used in more
traditional applications. The PV charges battery randomly
as the charge level is affected by the varying irradiation in
addition to the intrinsically fluctuating loading conditions.
This usually leads to charging the battery starting at any
SOC and ending abruptly at any battery SOC. Also, the
system would start withdrawing varying levels of energy
from the battery to support the fluctuating load. Hence, the
current work investigates, experimentally, the effect of
battery SOC on charging efficiency and it also investigates
the effect of discharging rate on the battery efficiency.
Also, the effect of SOC on power losses and capacity of
the battery to withstand different loading conditions was
Battery state of charge SOC is defined as the ratio
between the difference of the rated capacity and the charge
balance divided by the rated capacity. SOC can be
expressed by equation (1), [7]:
 C  Q Bat 
SOC   nom
 Cnom 
Nominal capacity of the battery (Ah)
Ah-balance i.e. net Ah discharged or charged
since the last full state of charge
When the battery SOC is low and the system
attempts to supply the demand, daily charge and discharge
would cycle close to the deep discharge threshold. More
intelligent management system would monitor the SOC
and gradually reduce the energy taken from the battery to
help prevent continuous operation at a low state of charge.
The minimum SOC or deep discharge protection (DDP) is
often implemented by measuring the battery voltage. In
the winter months the battery may experience a low SOC
for extended periods due to the seasonal variation in solar
irradiation. A sever low SOC affects system performance
and reduces the life of the battery.
The system in the current research consists of PV
modules, batteries, charge controller, DC/AC inverter and
a dedicated adjustable load. The system is monitored
through a specially developed monitoring unit (MU). The
MU employs a number of sensors and signal conditioning
devices and USB data acquisition cards (DAQ), connected
to a PC to monitor and record the system performance,
using a specially built LABVIEW software program.
Figure 1, shows a schematic diagram of the employed
energy system.
3.1. PV modules
The employed energy source is a number of
twelve PVs from BIC Company, of Model: GP-75. The
PVs were installed on top of a building (22 m height) in a
totally sunny spot in Giza, Egypt. These PVs have a rated
voltage of 12 volt, 21 open circuit volt, 4.8 short-circuit
Amp and a maximum power of 75 watt each. Every pair of
PVs are connected in series to supply 24 voltage required
by the inverter, and then the pairs are connected in
parallel, to supply a net of 24 voltage and 450 watt of
VOL. 13, NO. 2, JANUARY 2018
ISSN 1819-6608
ARPN Journal of Engineering and Applied Sciences
©2006-2018 Asian Research Publishing Network (ARPN). All rights reserved.
Charge controller
+ -
+ -
+ -
+ -
+ -
+ -
Figure-1. Schematic diagram of the employed energy system.
3.2. Lead-acid storage battery
Different battery types are used with PV system
such as lead-acid and nickel cadmium batteries. The most
commonly used is the lead-acid battery with its different
technological solutions (e.g. flooded-vented, valveregulated (VRLA) in adsorbed gel form. The 12-volt and
6-volt batteries are the most common blocks [6]. In the
current research a set of six batteries, are employed. The
batteries are Lead Acid based Gel type modules from
Dyno EUROPE ® Company of Model DGY12-100EV.
Each battery is 12 volts and 100 Ah/ 20 hrs. Batteries were
connected in pairs to provide 24 volts required by the
inverter then the pairs were connected in parallel.
3.3. Charge controller
The employed charge controller is from Steca ®
Company and it is Model PR3030. The charge controller
has the capacity to supply the PV energy to the load and
charge the batteries using any excess energy until it is
fully charged. In case of shortage it would withdraw
energy from batteries to feed the load until SOC of 35%,
where it cut off the battery current to protect it from deep
discharging, which would destroy it.
3.4. DC/AC inverter
The employed DC/AC inverter is from IFONIX
® Company and its Model No. is IC-1200 It receives an
input of 24 DC volts and employs switching mode
technology to convert it to 220 V AC, to match domestic
load requirements. It has the capacity to support
continuously a load of 1200 watts and a peak of 2400
watts of energy.
During the test these posts were filled with tungsten
lambs, either 100 watts lambs or by four 100 watts lambs
and one 50 watts lamb. In the first case a maximum load
of 500 watts with a step of 100 watts was attainable, while
in the second case a maximum load of 450 watts with a
step of 50 watts was attainable.
3.6. Current measurements
Current is measured, by MU unit, through a
number of current sensors; signals are converted to digital
data using the data acquisition cards (DAQ) and fed to the
PC. For the MU system, the same transducer measures
both batteries charge and discharge currents. The
discharge current has a positive sign and the charging
current has a negative sign.
3.7. Voltage measurements
The voltages of different system components are
measured in order to evaluate the system’s energy flow
and to batteries output energy. The voltage by nature is
detectable and easy to convert to digital data by the means
of DAQ. The employed DAQ measures the voltage and
converts it to digital data and feed it to the PC. The
batteries are arranged to provide a voltage of 24 volts as
required for inverter input voltage. As the measuring range
of employed DAQ, and most available DAQs, is ±10
volts, a special arrangement of resistors was used in
voltage divider configuration to adapt the measuring
levels. To measure inverter output voltage; which is in the
range of 220 AC volts, a step-down transformer was used
to reduce the level of the signal to 12 AC volts, and then a
voltage divider was employed to adjust the level to the
DAQ range.
3.5. Adjustable load
The dedicated adjustable load is comprised from
five posts each would accept a domestic tungsten lamb.
VOL. 13, NO. 2, JANUARY 2018
ISSN 1819-6608
ARPN Journal of Engineering and Applied Sciences
©2006-2018 Asian Research Publishing Network (ARPN). All rights reserved.
3.8. Data acquisition cards (DAQ)
Two identical DAQ cards from National
Instrument ® Company, Model USB 6008 were employed
in the current system. Each has eight channels and can
deliver up to 12 bits A/D conversion.
(a) Current monitoring screen
3.9. LABVIEW® software program
The program was specially developed to
communicate with the monitoring unit to display and
record renewable energy system performance parameters.
Figure-2 shows sample of system performance screens.
(b) Voltage monitoring screen
Figure-2. Sample of system performance screens, a. Current, b. Voltage.
Through the employed system, PV, inverter and
battery’s voltages and currents are monitored, displayed
and recorded.
4.1. System calibration
Performing the first test run, the results of battery
voltage; showed a high level of signal noise that was
attributed to the noise generated by the switching mode
technology of the inverter unit. To suppress this noise a
small capacitor (0.1 µf, 50 V) was connected to DAQ port
parallel to the voltage sensor.
For the purpose of system calibration, a set of
measuring devices were employed and the test was
conducted at fixed and known conditions. The test results
were compared to measured levels and the monitoring
system was adjusted to reflect the true energy system
signals. The control scheme was calibrated for different
values of; PV current, battery current, inverter current and
4.2 System configuration
To carry out the different tests, the system was
configured differently for each test. This configuration
changes involved the number of batteries constituting the
storage battery bank and the connection pattern
(serial/parallel). The PVs were employed as the only
means of battery charging. After the batteries were
charged the PVs were covered with thick cloth to ensure
that PV would not supply any current to the system.
A set of experiments were performed using the
developed experimental setup. The results are presented
and discussed in the following sections.
5.1. Effect of SOC on battery’s charging efficiency
In this test PV modules were employed to charge
a single battery from 35 % SOC to 100% SOC and
charging current was recorded. The total Ah consumed
during charging was calculated as the integration
(summation) of charging current measured multiplied by
charging time, using equations (2 & 3). The results were
collected using the MU and the software, where the
current is measured every time interval (Δt) that is equal to
two second.
Battery Charging Ah = ʃ I dt
Battery Charging Ah =
 I.t
Where; I Charge current of interval
Δt Time of interval
The results, shown in Figure-3 and Table-1, show
the linear relation between SOC and charging Ah, which
indicates that SOC has no effect on the charging Ah. This
result proves that equal Ah diverted to the battery at
different SOC will contribute equally to battery’s charge.
This result shows that frequent charging of battery at
different SOCs will not affect the charging efficiency,
which is a positive outcome. Also, the result showed that
charging efficiency was higher than 90%, as less than 70
Ah were consumed to charge the 100 Ah' battery from
SOC 35% to SOC 100%, that corresponds to 65 Ah
battery charge.
VOL. 13, NO. 2, JANUARY 2018
ISSN 1819-6608
ARPN Journal of Engineering and Applied Sciences
©2006-2018 Asian Research Publishing Network (ARPN). All rights reserved.
deliver the 24 volts required for the AC/DC inverter. Then
the inverter AC output was connected to adjustable load.
Each 100 Ah battery would theoretically deliver 65 Ah
when discharged from 100% SOC to 35% SOC at 5 Ah
discharge current. The theoretical 130 Ah for the bank of
two batteries was considered the base for discharge
efficiency calculation.
The PV modules were employed to charge
batteries to 100% SOC then the PV modules were covered
ensure zero PV current and the batteries were discharged
to 35% SOC, as measured on the charge controller, using
different load for each run. During these runs the
summation of the discharged current (Actual Ah) were
calculated for the batteries. At 50 watts of load (around 2
Amps of discharge current) the two batteries delivered 90
Ah that when compared with theoretical capacity of the
batteries translates to 70 % of battery discharge efficiency.
At 400 watts of load (around 17 Amps of discharge
current) the two batteries delivered around 66 Ah, which
translates to around 50% of battery discharge efficiency.
Figure-4 shows the significant decrease in batteries useful
capacity at higher load demand (discharge current). It also
shows the linear relation between the load and the batteries
useful capacity. This linear relation would facilitate
simulation studies.
The poor battery performance when discharged at
higher rates emphasizes on the need to properly consider
adding extra batteries to support higher load requirements.
Although more batteries translate to more system cost, the
higher running efficiency of the system translates to higher
system capacity. The results show importance of
simulation study of the load pattern and its effect of
system performance as means to decide on the proper size
of storage batteries.
Figure-3. Battery charging energy at different SOC.
Table-1. Charging Ah used to charge battery from
35% SOC to 100% SOC.
Battery current × Δt
[Alarm (Ah)]
SOC (%)
5.2. Effect of discharge current on battery’s efficiency
To evaluate the effect of discharge currents on
batteries discharge efficiency, a special test was carried
out. In this test two batteries were connected serially to
Battery bank capacity (AHr)
Load (Watt)
Figure-4. Variation of battery bank capacity at different consumption rates.
VOL. 13, NO. 2, JANUARY 2018
ISSN 1819-6608
ARPN Journal of Engineering and Applied Sciences
©2006-2018 Asian Research Publishing Network (ARPN). All rights reserved.
To further investigate the system performance,
consumed power losses was derived and plotted against
loads, as shown in Figure-6. The results showed a
nonlinear trend of losses that features a characteristic bend
at the recommended maximum load. This load is
calculated as the product of nominal Amber hour of the
battery (100Ah), no. of batteries (2) and the voltage of the
battery (12V) divided by the recommended hours of
discharge (20hrs). For the current system, recommended
load could be evaluated as following:
5.3. Effect of battery state of charge on battery’s
output power
The effect of SOC (%) of batteries on the
capacity of the system to supply a load was evaluated. A
special test was carried out in which two serially
connected batteries (to supply 24V) were charged to 60%,
the PV modules were covered by an opaque sheet to
ensure zero PV current and the system was loaded; load
was supported only by the batteries. The power delivered
to the load was evaluated at different loads and different
battery states of charge. The results of this test, as shown
in Figure-5 shows that battery SOC doesn’t significantly
affect the capacity of the battery to support the load, down
to 35% SOC. Figure-5, also, showed increase of consumed
power in a rate higher than the load increases, leading to
decreased efficiency.
Battery’s recommended load = Battery Ah ×No. of
Batteries × Battery voltage / Discharge hours
= 100 × 2 × 12 /20 = 125 Watt
SOC 35%
SOC 40%
Consumed Power (W)
SOC 50%
SOC 60%
Load (W)
Figure-5. Power consumed by different loads at different battery’s SOC.
Finally, the decreased loss rates at higher loads
should be viewed as decrease in batteries useful capacity
at high load current. The results show that battery bank
selection should favor useful capacity unless other
economical goals such as reducing number of batteries to
reduce cost are pressing.
SOC 35%
SOC 40%
SOC 50%
Losses (W)
SOC 60%
Load (W)
Figure-6. Power losses at different battery’s SOC and different loads.
VOL. 13, NO. 2, JANUARY 2018
ISSN 1819-6608
ARPN Journal of Engineering and Applied Sciences
©2006-2018 Asian Research Publishing Network (ARPN). All rights reserved.
The current study investigated the battery’s effect
on fitted PV energy system. The effects caused by
battery’s capacity and performance under different SOC
and load demand could be concluded as following:-
[7] Ibrahim M.I. 2002. Decentralized Hybrid Renewable
Energy Systems: Control Optimization and Battery
Ageing Estimation Based on Fuzzy Logic. Ph. D.
Thesis, Kassel University, Germany.
Battery SOC does not affect battery charging
efficiency, consequently does not reduce energy
system performance.
[8] Duryea Shane, Islam Sayed and Lawrance William.
2001. A Battery Management System for Stand-Alone
Photovoltaic Energy Systems. IEEE Industry
Applications Magazine.
Batteries suffer from significant decrease in its useful
capacity at higher discharge current (load
requirements). This indicates a poor efficiency of the
batteries when supporting larger loads, leading to
degraded energy system performance.
Battery losses is nonlinear that increases by increase
of batteries SOC and load.
Lower battery’s SOC has insignificant effect on the
capacity of battery to supply the load demand.
[1] Lambert Doudlas W.H. 2001. Batteries for Small PV
and Solar Home Systems. Renewable Energy World.
[2] Wei Zhou, Chengzhi Lou, Zhongshi Li, Lin Lu,
Hongxing Yang. 2010. Current Status of Research on
Optimum Sizing Of Stand-Alone Hybrid Solar-Wind
Power Generation Systems. Applied Energy. 87(380389).
[3] David Parra, Gavin S. Walker, Mark Gillott. 2014.
Modeling of PV Generation, Battery and Hydrogen
Storage to Investigate the Benefits of Energy Storage
for Single Dwelling. Sustainable Cities and Society.
[4] Wen-Yeau Chang. 2013. The State of Charge
Estimating Methods for Battery: A Review. Hindawi
Publishing Corporation, ISRN Applied Mathematics,
Article ID 953792, p. 7.
[5] A.J. Ruddell, A.G. Dutton, H. Wenzl, C. Ropeter,
D.U. Sauer, J. Merten, C. Orfanogiannis, J.W.
Twidell, P. Vezin. 2002. Analysis of Battery Current
Microcycles in Autonomous Renewable Energy
Systems. Journal of Power Sources 112: 531-546.
[6] Ghada Merei, Cornelius Berger, Dirk Uwe Sauer.
2013. Optimization of an Off-Grid Hybrid PV-WindDiesel System with Different Battery Technologies
Using Genetic Algorithm. Solar Energy. 97(460-473).