This paper presents a novel Simulink models with an evaluation study of more widely used OnLine Maximum Power Point tracking (MPPT) techniques for PhotoVoltaic based Battery Storage Systems (PVBSS). To have a full comparative study in terms of the dynamic response, battery state of charge (SOC), and oscillations around the Maximum Power Point (MPP) of the PVBSS to variations in climate conditions, these techniques are simulated in Matlab/Simulink. The introduced methodologies are classified into two types; the first type is conventional hillclimbing techniques which are based on instantaneous PV data measurements such as Perturb & Observe and Incremental Conductance techniques. The second type is a novel proposed methodology is based on using solar irradiance and cell temperature measurements with pre build Adaptive NeuroFuzzy Inference System (ANFIS) model to predict DC–DC converter optimum duty cycle to track MPP. Then evaluation study is introduced for conventional and proposed OnLine MPPT techniques. This comparative study can be useful in specifying the appropriateness of the MPPT techniques for PVBSS. Also the introduced model can be used as a valued reference model for future research related to Soft Computing (SC) MPPT techniques. A significant improvement of SOC is achieved by the proposed model and methodology with high accuracy and lower oscillations.
By the end of 2020, the renewable generation power capacity of worldwide has been increased by 261 GW, which is 10.3% higher than 2019’s. 64% Share of new renewable capacity installed in Asia. Solar energy continued to drive capacity expansion with an increase of 127 GW, which represents 22% as shown in
A Battery storage system is the most activity in 2020 as shown in
Besides, batteries ESS can be designed and produced for any rating, based on voltage and current availability [
There are 3 main types of conventional Batteries ESS include LithiumIon (Liion) batteries (92%), Sodiumsulphur (NaS) batteries (3.6%), and LeadAcid (PpA) batteries (3.4%) [
Although LeadAcid (PpA) batteries are simple to manufacture, easy to recycle, and capable of high discharge currents, besides have high efficiency (70%–80%), long calendar life (5–15 years), high specific power, and low cost per watthour, but they are limited using due to poor weighttoenergy ratio, slow charging (14–16 h) and its environmental risk (Pb is a poisonous metal) [
Also, Sodiumsulphur (NaS) batteries are of limited use because they must be operated at high temperatures (between 300°C and 350°C) which makes their operation dangerous and high costs [
Where LithiumIon (Liion) batteries, are the most extensively used technology in electrochemical ESS [
After using Battery ESS, PV systems still suffer from nonlinear behavior or maximum power point variation with the climatic conditions, besides low conversion efficiency which is less than 17%. So, it becomes necessary to use the MPPT system to ensure more efficient energy management operation of PVbased Battery Storage Systems [
Offline methods or Modelbased methods, which are based on the parameters of the PV panel to generate the control signals, such as Computational methods (OCV method and SCC method) [
Online methods or Modelfree methods or hill climbing techniques (Perturb and Observe (P&O) and Incremental Conductance (IncCond)) which are based on instantaneous measurements of PV data. Despite climate conditions changes being rapidly detected, oscillations around the MPP are found [
The Hybrid methods which are based on two algorithms, first an offline method to get the initial operating point close to the MPP of PV, and the second online method to track the MPP faster but with more complexity and cost [
Due to cost, ease of implementation, faster dynamic response, modelfree method and efficiency, online methods are more suitable for ESS although they suffer from oscillations around the MPP, which it's not an essential point to ESS.
This paper presents an evaluation study of conventional and novel SC online MPPT techniques for PVBSS which can predict and track the MPP under rapidly changing environmental conditions in a short time with minimum error and low oscillations. Besides, a valued reference model is also introduced to be able to properly simulate the PVBSS and study the different effects conditions on the battery SOC.
The proposed PVBSS introduced in this work mainly consists of a PV module, a DCDC converter, and a Liion battery as shown in
The PV array has nonlinear voltage current characteristic that depends on solar radiation, cell temperature and cells connected pattern as shown below [
The electrical specification (at STC) of Siemens’s SM55 PV solar module given by manufacturer and mentioned in Appendix can be used to find the five unknown parameters; reverse saturation current per cell
According to the short circuit conditions;
According to the open circuit conditions;
According to the Maximum power conditions;
According to the derivative of PV module current at MPP;
According to the derivative of PV module current at SC;
By solving
The comparison between the simulation results with the specification data given by the manufacturer as shown in
Specification data  55  3.15  17.4  3.45  21.7 
Simulation results  54.811  3.155  17.361  3.455  21.691 
Error (%)  0.344  0.159  0.224  0.145  0.042 
Due to high sensitivity to changes in duty cycle
So the duty cycle
There are many equivalent circuit models for lithiumion batteries. The onetime constant model (
The OTC model mainly consists of three items including the voltage source
By using the above two equations besides the parameters of the Liion battery cell mentioned in the Appendix; the simulation model is built in Matlab/Simulink as shown in
The proposed PVBSS with MPPT techniques was simulated in MATLAB/SIMULINK software environment as shown in
The variation of PV voltage, Battery voltage, Battery power, and SOC of battery without MPPT technique will be illustrated below from
These figures show the effects of the irradiance (
Where
In this part; three different online MPPT techniques are discussed. P&O and IncCond techniques are conventional hillclimbing techniques that are based on measurements of PV voltage and currents then the duty cycle is varied until the MPP is reached. The third technique is a novel methodology based on using solar irradiance and cell temperature measurements with prebuild ANFIS model to predict the optimum duty cycle to reach MPP.
P&O, IncCond and ANFISbased techniques are used as an online MPPT controller to investigate the proposed study under different environmental conditions, so intensive works are conducted.
The P&O technique is based on periodical measurement of PV current
Perturb; varying the power converter duty cycle
Observe; determining the output power variation
If
For more accuracy, lower oscillation, and fast reaching to the MPP, variable step size is used [
The previous steps can be summarized in the flow chart of the P&O technique, which is shown in
The IncCond technique is based on periodical measurement of PV current
Varying the power converter duty cycle
Determining the PV current variation (
If
If
The previous steps can be summarized in the IncCond technique flow chart, which is shown in
For every previous change in PV current and voltage due to irradiance and temperature change the optimum Duty cycle obtained by the IncCond technique is shown in
The ANFISbased algorithm uses a periodical measurement of the radiation level and cell temperature as inputs of a network; and the optimal Duty cycle
The optimal Duty cycle
So the ANFIS model can be built by using actual data of
For every previous change in PV current and voltage due to irradiance and temperature change the optimum Duty cycle obtained by ANFISbased technique is shown in
The operation of PVbased battery storage system using IncCond, P&O, and ANFIS based techniques under different environmental conditions is done below. Illustrating the performance characteristics of PVBSS and evaluating the validity and the accuracy of proposed models under variable climates conditions are achieved by simulation works.
For every previous change in irradiance and temperature change, the PV and Battery voltage and current obtained by IncCond, P&O, and ANFISbased techniques are shown in
PV voltage  Battery voltage  

IncCond MPPT  P&O 
ANFIS MPPT  MPP LOCUS  IncCond MPPT  P&O MPPT  ANFIS MPPT  MPP LOCUS  
Mean  16.73  16.74  17.5  16.733  23.69  23.72  23.74  23.99 
Median  17  17.15  17.88    23.79  23.82  23.89   
Mode  20.65  15.23  16.1    15.09  21.54  22.41   
Std  0.9286  0.7744  0.7126    0.8103  0.7775  0.7741   
Range  21.69  5.423  4.467  1.868  10.61  8.262  3.862  1.9889 
PV current  Battery current  

IncCond 
P&O 
ANFIS MPPT  MPP 
IncCond 
P&O 
ANFIS MPPT  MPP 

Mean  38.42  36.96  38.76  38.889  25  24.76  25.23  26.91 
Median  38.27  42.38  37.88    25.11  22.58  24.96   
Mode  41.37  21.84  23.04    0  13.1  13.25   
Std  10.3  9.486  8.885    7.681  7.486  7.149   
Range  52.26  25.25  23.78  5.1  47.62  25.83  24.58  16.512 
For Voltage waveform; ANFISbased technique has a lower range and standard deviation which means lower oscillation as noticed in
Also for the current waveform; ANFISbased technique has a slight lower range and standard deviation which means lower oscillation as noticed in
IncCond MPPT  P&O MPPT  ANFIS MPPT  MPP LOCUS  

Mean  697  591.2  608  649.66 
Median  599.9  562.1  600.4   
Mode  0  121  298.7   
Std  196.9  182.5  192   
Range  1196  830  618.3  431.623 
where,
A novel approach for online MPPT for PVBSS operation based on ANFIS Technique is discussed, implemented and assessed. Besides conventional hillclimbing techniques P& O, and IncCond techniques are also introduced and evaluated.
The main results present in this article can be summarized as shown below;
A valued reference model of PVBSS is introduced with full analyses
Evaluation study of OnLine MPPT Algorithms of PVBSS is introduced with full analyses
P&O, IncCond and ANFIS based techniques are presented
Significant increase of SOC for proposed methodology to 147.3% from (84.7% without MPPT to 71.73% with MPPT).
Get a novel SC method to predict the optimal duty cycle to track the MPP with variable climate conditions.
Approximately equal duty cycle prediction from SC MPPT model and analytical model with MSSE of 0.000083 for the worst case.
Then modeling of PVBSS operation with P&O, IncCond and ANFISbased techniques is discussed.
Also evaluation of P&O, IncCond and ANFISbased techniques is introduced and ANFIS based technique is proved as the best online MPPT technique with lower oscillation.
This model may be very useful for more works such as studying the partial shading effects, different types of batteries, and discussing the effect of SC MPPT techniques on SOC.
Voltage of PV generator, in Volt.
Current of PV generator, in Ampere.
Output power of PV generator output power, in Watt.
Cell insolation photo current, in Ampere.
PV generator insolation photo current, in Ampere.
Cell reverse saturation current, in Ampere.
Reverse saturation current of PV generator, in Ampere.
Series resistance per cell, in Ohm.
Shunt resistance per cell, in Ohm.
Series resistance of PV generator, in Ohm.
Numbers of series connected solar cells
Number of parallelconnected solar cells
Electron charge, 1.602 × 10^{−19} C
Boltzmann constant, 1.38 × 10^{−23} J/k
Cell working temperature in, °C
Completion factor.
PV cell constant in, (1/V)
PV generator constant in, (1/V)
Solar insolation (radiation) level in W/m^{2}.
Maximum output power of PV module, in Watt
Maximum current of PV module, in Ampere.
Maximum voltage of PV module, in Volt.
Short circuit current of PV module, in Ampere
Open circuit voltage of PV module, in Volt
Temperature coefficient of
Temperature coefficient of
Battery open circuit voltage, in Volt
Battery parallel RC resistance, in Ohm
Battery parallel RC capacitor, in Farad
Battery internal resistance, in Ohm
Battery parallel RC voltage, in Volt
Battery voltage, in Volt
Battery current, in Ampere
Optimum duty cycle of DCDC Converter
Current ratio of DCDC Converter
Sampling period in Sec
The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the General Research Funding program grant code (NU//SERC/10/650).