IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 50, NO. 2, MARCH/APRIL 2014 1245
Parallel Power Processing Topology forSolar PV Applications
Mohamed O. Badawy, Ahmet S. Yilmaz, Member, IEEE, Yilmaz Sozer, Member, IEEE, andIqbal Husain, Fellow, IEEE
Abstract—An efficient converter topology for extracting maxi-mum power from a photovoltaic module is presented in this paper.The proposed topology is implemented on a stand-alone batterycharging system. A reversed buck–boost converter enabling paral-lel power processing (PPP) with a power switch referenced to thecommon return is the core of the charging system. Small-signalanalysis of the proposed charging system is carried out to facilitatethe design of a compensator for maximum power point (MPP)tracking. The simulation and experimental results confirm thevalidity of the model and verify the high efficient operation whiletracking the MPP. The use of the PPP with split loads is alsopresented which would improve the converter size, efficiency, andstep-down duty ratio.
Index Terms— Battery charging, maximum power point track-ing (MPPT), parallel power processing, photovoltaic (PV) stand-alone systems.
I. INTRODUCTION
IN RECENT YEARS, the use of renewable energy re-sources as a sustainable alternative solution for fossil fuels
is increasing rapidly. Solar energy is considered to be one ofthe most effective resources attracting much attention due toits ubiquity and sustainability. The most common applicationfor the use of solar energy is through the photovoltaic (PV)systems. PV systems have been gaining high reputation andwidespread usage due to their unique capability of directlytransferring solar energy into electrical energy without the needof complex auxiliary systems.
The main drawback of the renewable energy generationsystems is the lack of a stable and continuous electrical powerproduction due to variations in the supply and the operatingconditions. The electrical power generation in PV systems ishighly affected by the temperature and the insolation level
Manuscript received December 2, 2012; revised March 7, 2013 and July 4,2013; accepted July 7, 2013. Date of publication August 7, 2013; date ofcurrent version March 17, 2014. Paper 2012-SECSC-546.R2, presented at the2012 IEEE Energy Conversion Congress and Exposition, Raleigh, NC, USA,September 15–20, and approved for publication in the IEEE TRANSACTIONS
ON INDUSTRY APPLICATIONS by the Sustainable Energy Conversion SystemsCommittee of the IEEE Industry Applications Society.
M. O. Badawy and Y. Sozer are with the Department of Electrical andComputer Engineering, The University of Akron, Akron, OH 44325 USA(e-mail: [email protected]; [email protected]).
A. S. Yilmaz is with the Department of Electrical and Electronics En-gineering, Sutcu Imam University, Kahramanmaras 46100, Turkey (e-mail:[email protected]).
I. Husain is with the Department of Electrical and Computer Engineering,North Carolina State University, Raleigh, NC 27695 USA (e-mail: [email protected]).
Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIA.2013.2277546
Fig. 1. PPP general concept block.
which makes it impractical to directly connect them to the loadwithout any storage devices or utility grid interface. Studiesabout PV systems focused on the minimization of the instal-lation costs and the maximization of the energy capture andconversion efficiency. In order to maximize the conversionefficiency, the PV system should be operated at its maximumpower point (MPP) [1, 2]. In PV-based stand-alone chargingsystems, the dc–dc converter tunes the variable PV outputvoltage to charge the battery at the MPP conditions. Thereduction in converter size and cost could lead to a capability ofpractically using an individual MPP tracking (MPPT) inverterfor every panel rather than using a centralized MPPT for a stringof PV panels [3]. The parallel power processing (PPP) topologyhad been proposed to reduce the amount of power processedthrough the converter and, hence, decrease the converter sizeand increase the efficiency by means of direct energy transfer(DET) [4]. The concept of the PPP topology is shown in Fig. 1.The objective of the PPP topology is to process the majority ofthe power to the load (Pd) through a direct energy path withoutbeing exposed to the converter losses. The rest of the power issupplied to the load through the converter (Po) [5].
The main contribution of this paper is to propose an al-ternative configuration for the PV battery charging systemsbased on the PPP topology. A reversed buck–boost (BB) (RBB)converter with a power switch referenced to the common returnis presented in this paper to support the PPP charging sys-tem concept. Detailed modeling and analysis of the proposedtopology has been developed to design the MPPT algorithm.The proposed charging topology, along with control algorithms,has been implemented and demonstrated through simulationand experimental studies. The PPP concept is extended to acharging system for split loads, where the PV is powering twoseparate loads. This paper is organized as follows. In Section II,the main converter topology is introduced along with its MPPTalgorithm. The modeling of the individual subsystems and thesmall-signal analysis of the proposed topology are presented inSection III. Section IV observes and analyzes the simulation
1246 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 50, NO. 2, MARCH/APRIL 2014
Fig. 2. Circuit topology of the proposed charging system.
results. The corresponding split load circuit is presented inSection V. Experimental results are presented in Section VI.Section VII provides the summary and the conclusions.
II. PV SYSTEM FOR BATTERY CHARGING APPLICATIONS
A. Main Circuit Operation With PPP Topology
The dc/dc converter used for the PPP scheme should havethe reverse output voltage capability [5], [6] to support thepower processing. This restriction limits the selection of thenonisolated converters to BB and Cuk converters. Due to itssimplicity and higher conversion efficiency, the BB converteris chosen over the Cuk converter. An RBB converter is pro-posed in this paper to enable referring the power switch to thecommon return and, thus, decreasing the overall system cost forthe individual MPP tracker [4]. The PV panel in the proposedtopology is in parallel to a branch containing the battery packand the RBB converter. The voltage difference between the PVand the battery is fed into the input of the converter. The outputof the converter is fed into the battery providing the second pathfor charging. As the power transfer by the DET path increasescompared to the converter path, the overall efficiency would beincreasing. The voltage balancing equation is
Vpv = Vb + Vc1. (1)
Vpv is the PV voltage, Vb is the battery voltage, and Vc1
denotes the decoupling capacitor voltage which is referred asthe converter input voltage. The proposed system for batterycharging from the PV source is presented in Fig. 2.
The conventional BB converter does not provide DET duringboth ON and OFF states of the operation; on the other hand,the PPP topology enables a continuous DET from the PV tothe battery pack regardless of the switch state. Fig. 3 shows thecircuit configurations during ON and OFF states of the switchingdevices, respectively.
When the switch is ON and the diode is in OFF condition,the current of the solar panel charges the battery pack andthe inductor by means of DET. Meanwhile, the decouplingcapacitor (C1) discharges through the inductor, and the voltagebalancing capacitor (C2) discharges through the battery pack.
Fig. 3. Equivalent circuit and power flow for (a) switch ON state and(b) switch OFF state.
When the switch is OFF and the diode is in ON condition,the PV panel charges both the battery pack and the decouplingcapacitor (C1) through the DET path. The charge stored in theinductor during the ON state continues to charge the batterypack and the voltage balancing capacitor (C2).
Considering the battery voltage Vb across the capacitor C2
as an output voltage (Vo) and Vc1 as an input voltage (Vi), theratio of the Vo to Vi can be represented as
Vo
Vi=
Vb
Vpv − Vb=
D
1−D(2)
which can be simplified as
Vb
Vpv= D. (3)
It can be seen from (3) that the RBB converter with the PPPtopology is applicable for bucking circuits.
B. MPPT Methods
The insolation level and temperature are the two main factorsaffecting the V –I and V –P characteristic curves. It is thecontroller’s objective to extract maximum power at any possibleoperating conditions.
In order to reach the MPP rapidly, several algorithms andcontrol techniques have been studied. Current control looptechniques [7], perturbation and observation algorithms [8],[9], incremental conductance (IC) algorithms [9], [10], lookuptable-based algorithms [8], [9], and fuzzy logic controllers [11]are proposed and applied in reaching MPP operation. Digitalsignal processors, microprocessors, and field-programmablegate arrays [8], [12]–[15] are used for the experimental im-plementation of the MPPT algorithms. These techniques differfrom each other in several aspects such as accuracy, complexity,and convergence speed [9], [10], [16].
1) Open-Circuit Voltage Method: This is the simplestmethod which sets the MPP voltage as a fraction of the open-circuit voltage. The method does not require continuous mea-surement and is not able to take into account the variations inthe operating temperature and the insolation level.
2) Short-Circuit Current Pulse Method: This methodachieves the MPP through the magnitude of the operating cur-rent which is assumed to be a factor multiplied by the PV short-circuit current. This control algorithm requires a measurement
BADAWY et al.: PARALLEL POWER PROCESSING TOPOLOGY FOR SOLAR PV APPLICATIONS 1247
Fig. 4. MPP under various operating conditions.
of the short-circuit current. The portion of the short-circuitcurrent is applied as a command current to the converter.
3) Hill Climbing P&O Method: The perturb and observe(P&O) algorithms operate by periodically incrementing ordecrementing the array terminal voltage or current and compar-ing the PV output power with that of the previous perturbationcycle. If the change in the power output at the end of theperturbation cycle is positive, then the perturbation is appliedcontinuously in the same direction; otherwise, the direction ofthe perturbation is changed. The main drawback of this methodis the oscillation around the MPP. Power oscillation can bereduced by reducing the perturbation amount at the expense ofreducing the convergence speed. The P&O technique can failunder rapid atmospheric variations [16].
C. MPPT Algorithm for RBB Converter-Based PPP Charging
The MPPT algorithm used for the RBB converter for PPPcharging is basically driven from the IC method. MPP isachieved when the sum of the instantaneous conductance (I/V )and the IC (ΔI/ΔV ) reaches zero as described in
dIPV
dVPV+
IPV
VPV= 0. (4)
Therefore, at MPP
IPV + VPVdIPV
dVPV=
a(IPVVPV)
dVPV=
dPPV
dVPV= 0. (5)
Fig. 4 shows the MPP from the P−V characteristics undervarious insolation levels (0.5, 0.75, and 1 kW/m2). The rateof change of PV output power with respect to PV outputvoltage (dPpv/dVpv) is zero at MPP as seen from Fig. 4 andas demonstrated in (5). The closed-loop controller that usesthe (dPpv/dVpv) as a measure and tries to bring it to zeroby commanding a duty ratio for the RBB converter wouldachieve MPP operation. The approach presented in this paperis to use a compensator to achieve MPPT with an acceptabletransient response based on an overall system model. The stepsin modeling the system and its small-signal analysis along withthe compensator design process are presented in the followingtwo sections. The block diagram of the MPPT controller isshown in Fig. 5.
Fig. 5. Block diagram of the proposed closed-loop MPPT algorithm.
Fig. 6. Thevenin equivalent model of the Li-ion battery having three cells.
III. MODELING OF THE SUBSYSTEMS
IN THE PROPOSED CHARGER
Modeling different components in the PV system is essentialfor accurate simulation results and the controller design. ThePV, the battery, and the charging system are modeled accuratelyin order to develop the system transfer function.
The model that describes the (I–V ) characteristics of the PVmodel is represented by the following [17], [18]:
I =NpIph −NpIs
[e
(q(V +IRsNsTcKA)
)− 1
](6)
Iph = [ISC +K1(Tc − Tref ) ∗ λ (7)
Irs =Irs[
e(qVoc
NsKATc) − 1
] (8)
Is = Irs
(Tc
Tref
)3e
⎡⎣qEg
(1
Tref
)−( 1
Tc)
KA
⎤⎦. (9)
I is the PV current, V is the PV voltage, Iph is the pho-tocurrent, Is is the cell saturation of dark current, q is anelectron charge, K is a Boltzmann’s constant, Tc is the cell’sworking temperature, Tref is the cell’s reference temperature,A is an ideal factor, Rs is a series resistor, Irs is the cell’sreverse saturation current at a reference temperature and a solarirradiation, and λ is the insolation level (kW/m2).
Lithium-ion batteries are used to evaluate the performanceof the charging system. The equivalent circuit model of thelithium-ion-type batteries is essential to develop the systemmodel as well as the overall simulation platform. There aredifferent types of circuit-based models available in the literaturesuch as the Rint model, the resistor–capacitor model, theThevenin model, the PNGV model, and the dual-polarizationmodel [19], [20]. The Thevenin model approach as shown inFig. 6 provides a simple and reasonably accurate model for theLi-ion cells.
1248 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 50, NO. 2, MARCH/APRIL 2014
IV. SMALL-SIGNAL REPRESENTATION
OF THE CHARGER SYSTEM
The state-space averaging model allows us to find the powerconverter circuit transfer function [21]. This transfer functionis used for the design of the MPPT compensator. In order todetermine the variations in the PV panel voltage with respect tothe switch duty ratio in the converter, it is necessary to modelthe charger system dynamic behavior. The considered circuitmodel in this study is illustrated in Figs. 2 and 3. The PVand the battery models explained in the previous sections areintegrated into the charger system to obtain an overall systemtransfer function. In the state-space representation, the inputand output state vectors are Vin = [Ipv Voc]
T , Vout = [Vpv],and x = [IL Vc1 Vc2 VCB ]
T , respectively, where Ipv is thePV current, Voc is the battery open-circuit voltage, Vpv is thePV voltage, IL is the inductor current, VC1 is the converterdecoupling capacitor voltage, VC2 is the converter balancingcapacitor voltage, and VCB is the voltage of the capacitorused in the battery modeling. State-space models are developedduring ON and OFF states. Then, these two states are combinedusing (11)–(13) according to formulas given in (10). Equations(14) and (15) represent the combined state-space representationof the complete charging system
d =ton
ton + toff=
tonT
(10)
A = dA1 + (1− d)A2 (11)
B = dB1 + (1− d)B2 (12)
C = dC1 + (1− d)C2 (13)
d
dt
⎡⎢⎣
iL1
VC1
VC2
VCB
⎤⎥⎦ =
⎡⎢⎢⎢⎣
0 dL1
d−1L1
0−dC1
0 0 0−(d−1)
C20 −1
R0C2
1RoC2
0 0 1RoCB
−(RB+Ro)RBCBRo
⎤⎥⎥⎥⎦
⎡⎢⎣
iL1
VC1
VC2
VCB
⎤⎥⎦
+
⎡⎢⎢⎣
0 01C1
01C2
1RoC2
0 −1RoCB
⎤⎥⎥⎦[IPV
EOC
](14)
[VPV] = [0 1 1 0]
⎡⎢⎣
IL1
VC1
VC2
VCB
⎤⎥⎦ . (15)
Perturbation is achieved by the introduction of input voltageand duty cycle variations VIN and d, respectively
x =X + x (16)
vOUT =VOUT + vOUT (17)
d =D + d. (18)
X , D, and VOUT are dc quantities at the operating con-ditions, and x, d, and vOUT are superimposed perturbations.Therefore, the steady-state model can be separated from thedynamic model. When the perturbation is introduced into (14)
and (15), the state-space model including the ac and the dcmodel can be calculated as follows:
x =X + x (19)
x = [AX +BVIN] + [Ax+BvIN]
+ [(A1 −A2)X + (B1 −B2)VIN] d
+ [(A1 −A2)x+ (B1 −B2)v] d (20)
where [AX +BVIN] is the steady state (dc) term and theothers are the dynamic term. To calculate the small signal (ac)response, steady state (dc) variables should be calculated withd = 0 and D = d. Hence, steady-state variables and output (dcoperation point) can be calculated using
X = −A−1B VIN (21)
VOUT = − C A−1B VIN. (22)
Consequently, the small-signal averaged model can be obtainedas follows:
x =Ax+AvIN
+ [(A1 −A2)X + (B1 −B2)VIN] d (23)
VOUT =Cx+ (C1 − C2)Xd. (24)
In the small-signal analysis, the duty cycle is a new inputvariable in addition to the variables of PV panel current andthe open-circuit voltage of the battery. To find the small-signalresponse of the proposed topology, the other inputs should bekept constant at their steady-state operating conditions. Thetransfer function of the overall system model is obtained asfollows:
VPV
d=
as3 + bs2 + cs+ z
es4 + fs3 + gs2 + hs+ v(25)
where the parameters in the transfer function can be representedas the charging system parameters as
a = IL1
(C1− C2
C1C2
)(26)
b = IL1[RB(C1 − C2 − CB)]
C1C2CBRBRO
+ (VC1 − VC2)[(1− d)C1 + C2d]
C1C2L1(27)
c = IL1[CBRBRO(2d− 1)− L1]
C1C2CBL1RBRO+ (VC2 − VC1)
× [(RB +RO)(C1 − C1d+ C2d) +RBCBd]
C1C2CBL1RBRO(28)
z =IL1(2d− 1)(RB +RO) + (VC2 − VC1)d
C1C2CBL1RBRO(29)
e =1 (30)
f =C2RB + C2RO + CBRB
C2CBRBRO(31)
BADAWY et al.: PARALLEL POWER PROCESSING TOPOLOGY FOR SOLAR PV APPLICATIONS 1249
TABLE ICOEFFICIENTS OF THE TRANSFER FUNCTION
Fig. 7. System root locus before compensation.
Fig. 8. System root locus after compensation.
g =C1L1 + CBRBRO
[C1(1− d)2 + C2d
2]
C1C2CBL1RBRO(32)
h =C1(RB+RO)(1−d)2+d2(C2RB+C2RO+CBRB)
C1C2CBL1RBRO
(33)
m =d2
C1C2CBL1RBRO. (34)
The parameters of the transfer function for the converter designare L = 1mH, C1 = 660 μF, and C2 = 660 μF.
The dc operating conditions are given in Table I.The transfer function of the overall system is useful in
developing the closed-loop controller compensator. The MPPTcontrol system presented in Fig. 5 is designed by observing thesystem root locus. A notch compensator is designed to reducethe amplitude of the transient oscillations around the steady-state value caused by the complex poles. Fig. 7 shows the rootlocus plot of the transfer function without the compensator. Theroot locus plot after the compensator is presented in Fig. 8.
Fig. 9 shows the response of the PV output voltage for a 5%increase in the duty ratio after using a lag–lead compensatorbased on the root locus of the evaluated transfer function shownin (24).
Fig. 9. PV voltage response for a compensated system with 5% step change.
Fig. 10. MPPT capability under various insolation levels.
V. SIMULATION RESULTS
The purpose of the simulations is to validate the performanceof the PPP technique through efficiency and voltage stresscomparisons as well as the MPP operation.
A. MPPT Simulation Results
Fig. 10 validates the ability of the system to track theMPP under sudden changes. The insolation level is suddenlydecreased from 1 to 0.5 kW/m2 at 0.01 s and then increased to0.75 kW/m2 at 0.022 s as can be observed from Fig. 10. Themaximum powers that are available from the PV panel at thesethree different isolation levels can be observed from Fig. 4.
The simulation results shown in Fig. 10 along with the maxi-mum power that is extracted under the applied insolation levelsas shown in Fig. 4 demonstrate that the MPP is effectively beingtracked under different steady-state and transient conditions.The short transient time and the low oscillations around theMPP prove the competence of the presented MPPT algorithm.
B. Efficiency and Component Stress Comparison toConventional Methods
The PPP-based RBB is compared to the conventional BBconverter configuration shown in Fig. 11 to analyze the PPPeffect on enhancing the efficiency and reducing the voltagestress for the same converter type. Both converters have similarpart counts but experience different voltage and current stressesdue to the way that they are connected in the charging system.
1250 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 50, NO. 2, MARCH/APRIL 2014
Fig. 11. Conventional BB converter.
Fig. 12. Output power comparison of the conventional BB and the PPP-basedRBB converter.
Fig. 13. Voltage stress comparison of the conventional BB and the PPP-basedRBB converter.
The increase in the efficiency is due to the PPP topology whichprovides DET between the PV panel and the battery.
The output powers of the PPP-based RBB and the conven-tional BB for the same input power are presented for compar-ison in Fig. 12. It can be shown that the proposed PPP-basedRBB topology is more efficient than the conventional BB topol-ogy while maintaining a lower voltage stress on the convertercomponents observed in Fig. 13. The efficiency values for thetwo converters are as follows:
ηRBB =149
160≈ 93% (35)
ηBB =137
160≈ 85.6%. (36)
It can be observed that a relative increment of 7.5% efficiencyincrease can be achieved using the proposed topology due to thecontinuous DET transfer between the PV panel and the batteryas explained in Section II.
Fig. 14. Conventional buck converter.
Fig. 15. PPP-based RBB decoupling capacitor voltage and voltage balancingcapacitor current stresses.
Fig. 16. Conventional buck decoupling capacitor voltage and voltage balanc-ing capacitor current stresses.
Fig. 13 shows that the voltage stress on the MOSFET and thedecoupling capacitor (C1) is lower using the PPP-based RBBconfiguration.
According to (1) and the charging system applicability, itis practical to compare the proposed converter topology to theconventional buck converter shown in Fig. 14.
The comparison results show the same efficiency and volt-age stress for all components when the proposed topology iscompared with the buck converter except for the decouplingcapacitor voltage and the voltage balancing capacitor current.Figs. 15 and 16 present the voltage stress across the C1 andthe current stress through C2 for the RBB-based PPP andconventional BB topologies, respectively.
It can be concluded from Figs. 15 and 16 that lower level ofvoltage stress on the decoupling capacitor is achieved with theproposed PPP-based RBB converter compared to the conven-tional buck converter. The voltage balancing capacitor in the
BADAWY et al.: PARALLEL POWER PROCESSING TOPOLOGY FOR SOLAR PV APPLICATIONS 1251
Fig. 17. Proposed split load circuit topology.
Fig. 18. Duty ratio versus charged battery SOC% comparison.
proposed topology should be designed to absorb a relativelyhigher ripple current.
VI. PPP FOR SPLIT LOADS
The proposed PPP technique is applicable for the split loadsof the battery and a dc load as shown in Fig. 17. The buckconverter is chosen as the desired power tracker in the split loadsystem due to its high efficiency resulting from its high DETcapability [4], and the relatively low voltage stresses applied onits components. A corresponding modified circuit is discussedin this section using the same PPP topology providing power tothe two loads fed from the PV input.
In the example illustrated, a comparison is done for charginga battery pack of five Li-ion cells. The PPP topology is usedby connecting four Li-ion cells as dc load 1 and one cell as dcload 2 (see Fig. 17), while for the conventional buck converter,the whole battery pack of five cells is connected in series withthe MPP tracker (buck converter) as shown in Fig. 14. Fig. 18shows the wider range of duty ratio that can be achieved whiletracking the MPP for the same PV panel under various state ofcharge values for the battery pack.
Table II shows that connecting the converter output to asecond lower voltage battery pack provides significant decreasein the voltage stress of the converter components.
The proposed technique reduces the switching losses com-pared to the conventional topology due to reduced voltage stresson the active components of the converter according to [22]
Psw = 0.5VdId(ton + toff)f +1
2CossV
2d f (37)
TABLE IIVOLTAGE STRESS COMPARISON
Fig. 19. PV panel voltage change to a 10% increment in the duty ratio for thesplit load system.
where Psw is the switching power losses, Vd is the voltageapplied on the MOSFET during the OFF state, Id is the currentpassing through the MOSFET during the ON state, ton is thetime required to turn the MOSFET on, toff is the time requiredto turn the MOSFET off, Coss is the parasitic capacitance, and fis the switching frequency. As the voltage stress on the devicesgets reduced for the split loads, Vd gets lower. Also, as thevoltage rating of the devices gets reduced, the ton and toff timesof the devices go down.
The efficiency is compared by summing up the power deliv-ered to the two dc loads in the proposed split load topology (seeFig. 17) and comparing it to the power delivered to the dc loadin the conventional topology (see Fig. 14). The switching losseswith the proposed split load charging topology are found to be0.45 W; on the other hand, switching losses of the conventionalcharging topology is found to be 2.56 W for the input power of160 W. The proposed topology improves the overall efficiencyby 1.32% just while considering the reduction in the switchinglosses. The conduction losses would also be reduced as thevoltage rating and the ON-state resistance of the semiconductordevices are lower for the split load charging topology.
Fig. 19 observes the change of the PV panel voltage for a10% increment in the duty ratio. It can be concluded from thefigure that the PV operating point is less sensitive to the dutyratio changes in the PPP split load topology compared to theRBB or the conventional buck topologies.
Conclusively, the PPP-based split load topology applieslower voltage stress on the converter components compared tothe PPP-based RBB topology. On the other hand, the split loadtopology proved its competence to be applied only when twoseparate loads are required to be supplied from one source dueto the different charging currents expected at every one of thetwo loads. Applications for this topology can be extended toa PV charging station and can be implemented for the hybrid
1252 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 50, NO. 2, MARCH/APRIL 2014
Fig. 20. Experimental setup for the proposed battery charging system.
TABLE IIIEXPERIMENTAL RESULTS UNDER VARIOUS INPUT POWER LEVELS
Fig. 21. Emulated PV panel characteristics of the TerraSAS PV simulator at1 kW/m2 irradiation and 25 ◦C.
energy storage devices in the electric and hybrid vehicles wherebatteries and ultracapacitors are combined to achieve a betterperformance for the vehicle [23]. The choice between the PPP-based RBB topology and the PPP-based split load topologymainly depends on whether the load can be split into tworequiring different charging rates or not.
Fig. 22. MPPT performance of the charging system with steady-state solarPV emulation at 1 kW/m2 irradiation and 25 ◦C. (a) PV output voltage. (b) PVoutput current. (c) PV output power.
Fig. 23. Solar PV emulation with slowly varying irradiance at relativelyconstant operating temperature.
VII. HARDWARE IMPLEMENTATION AND
EXPERIMENTAL RESULTS
A PPP-based RBB converter provided in Fig. 2 is devel-oped for experimental validation as shown in Fig. 20. Theexperimental tests are carried to validate the high efficientcharacteristics of the system and to prove the MPPT capabilitywith the proposed charging topology using the algorithm shownin Fig. 5.
Table III provides the steady-state experimental test resultsfor 65- and 105-W operating conditions. The proposed chargingtopology is able to achieve 94%–96.2% conversion efficiencyas presented in Table III, which is considered to be a relativelyhigh efficiency for the given power rating [4].
The MPPT capability of the RBB converter is tested using aETS80X10.5C-PVE TerraSAS PV simulator. The emulated PVpanel characteristics are shown in Fig. 21 at an insolation levelof 1 kW/m2 and a temperature of 25 ◦C.
The MPPT performance of the experimental charging sys-tem is tested first, for the emulated PV characteristics shownin Fig. 21. Fig. 22(a)–(c) shows the output voltage, current,and power of the PV panel, respectively, under the steady-state operating conditions given in Fig. 21. It can be observed
BADAWY et al.: PARALLEL POWER PROCESSING TOPOLOGY FOR SOLAR PV APPLICATIONS 1253
Fig. 24. MPPT performance of the charging system with slowly varyingirradiance level at relatively constant operating temperature shown in Fig. 23.(a) PV output voltage. (b) PV output current. (c) PV output power.
Fig. 25. Solar PV emulation with fast varying irradiance at relatively constantoperating temperature.
from Fig. 25(c) that the charging system and associated MPPTalgorithm is able to capture 97.5%–100% of the available powerfrom the solar emulator.
The second MPPT test is implemented on the experimentalsystem using slowly varying irradiance level between 500 and1000 W/m2 at relatively constant operating temperature aspresented in Fig. 23. Fig. 24(a)–(c) shows the output voltage,current, and power of the PV panel, respectively, under varyingsun irradiance conditions given in Fig. 23. It can be observedfrom Fig. 24(c) that the charging system and associated MPPTalgorithm is able to capture 96.5%–100% of the available powerfrom the solar emulator.
The third MPPT test is implemented on the experimentalsystem using rapidly changing irradiance level between 200and 800 W/m2 at relatively constant operating temperature aspresented in Fig. 25.
Fig. 26. MPPT performance of the charging system with rapidly varyingirradiance level at relatively constant operating temperature shown in Fig. 25.(a) PV output voltage. (b) PV output current. (c) PV output power.
Fig. 27. Solar PV emulation with slowly varying temperature at relativelyconstant irradiance level.
Fig. 26(a)–(c) shows the output voltage, current, and powerof the PV panel, respectively, under varying sun irradianceconditions given in Fig. 25. It can be observed from Fig. 26(c)that the charging system and associated MPPT algorithm is ableto capture 94.3%–100% of the available power from the solaremulator under sudden irradiance level changes.
The fourth MPPT test is implemented on the experimen-tal system using slowly changing temperature level between25 ◦C and 75 ◦C at relatively constant irradiance as presentedin Fig. 27. Fig. 28(a)–(c) shows the output voltage, current,and power of the PV panel, respectively, under varying sunirradiance conditions given in Fig. 27. It can be observedfrom Fig. 28(c) that the charging system and associated MPPTalgorithm is able to capture 93.6%–100% of the available powerfrom the solar emulator under slowly varying temperatureconditions.
1254 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 50, NO. 2, MARCH/APRIL 2014
Fig. 28. MPPT performance of the charging system with slowly varyingtemperature level at relatively constant irradiance shown in Fig. 27. (a) PVoutput voltage. (b) PV output current. (c) PV output power.
VIII. CONCLUSION
The proposed PPP topology using an RBB converter pro-vided an efficient battery charging system. Additionally, thevoltage stress on the converter components is decreased withthe PPP topology compared to the conventional ones. A small-signal model of the topology has been developed to get thetransfer function which is evaluated to construct the compen-sator to be used for the MPPT. The performance of the proposedtopology and the control algorithms are verified by the simu-lations results. Experimental tests are carried out using a PVsimulator tested under various environmental conditions. Theexperimental results verified the high efficiency of the topologyand validated the MPP algorithms along with the closed-loopcontroller applied for MPPT.
The PPP topology is also presented with split loads using abuck converter. Analysis provided an improvement in the step-down duty ratio and the decrement in the voltage ratings forthe converter components which would decrease the switchinglosses in the system.
REFERENCES
[1] K. H. Hussein, I. Muta, T. Hoshino, and M. Osakada, “Maximum pho-tovoltaic power tracking: An algorithm for rapidly changing atmosphericconditions,” Proc. Inst. Elect. Eng.––Gen. Tranmiss. Distrib., vol. 142,no. 1, pp. 59–64, Jan. 1995.
[2] D. P. Hohm and M. E. Ropp, “Comparative study of maximum powerpoint tracking algorithms using an experimental, programmable, maxi-mum power point tracking test bed,” in Proc. 28th IEEE PhotovoltaicSpec. Conf., 2000, pp. 1699–1702.
[3] S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg, “A review of single-phasegrid-connected inverters for photovoltaic modules,” IEEE Trans. Ind.Appl., vol. 41, no. 5, pp. 1292–1306, Sep./Oct. 2005.
[4] H. Dehbonei, S. R. Lee, and H. Nehrir, “Direct energy transfer for highefficiency photovoltaic energy systems. Part I: Concepts and hypothesis,”IEEE Trans. Aerosp. Electron. Syst., vol. 45, no. 1, pp. 31–45, Jan. 2009.
[5] D. B. Synman and J. H. R. Enslin, “An experimental evaluation of MPPTconverter topologies for PV installations,” Renew. Energy, vol. 3, no. 8,pp. 841–848, Nov. 1993.
[6] D. C. Riawan and C. V. Nayar, “Analysis and design of a solar chargecontroller using Cuk converter,” in Proc. Aust. Univ. Power Eng. Conf.,2007, pp. 1–6.
[7] M. G. Wanzeller, R. N. C. Alves, J. V. da Fonseca Neto, andW. Ad. S. Fonseca, “Current control loop for tracking of maximumpower point supplied for photovoltaic array,” IEEE Trans. Instrum. Meas.,vol. 53, no. 4, pp. 1304–1310, Aug. 2004.
[8] V. Salas, E. Olías, A. Lázaro, and A. Barrado, “Evaluation of a newmaximum power point tracker (MPPT) applied to the photovoltaic standalone systems,” Sol. Energy Mater. Sol. Cells, vol. 87, no. 1–4, pp. 807–815, May 2005.
[9] T. Esram and P. L. Chapman, “Comparison of photovoltaic array max-imum power point tracking techniques,” IEEE Trans. Energy Convers.,vol. 22, no. 2, pp. 439–449, Jun. 2007.
[10] A. Safari and S. Mekhilef, “Simulation and hardware implementation ofincremental conductance MPPT with direct control method using Cukconverter,” IEEE Trans Ind. Electron., vol. 58, no. 4, pp. 1154–1161,Apr. 2011.
[11] I. H. Alta and A. M. Sharaf, “A novel maximum power fuzzy logiccontroller for photovoltaic solar energy systems,” Renew. Energy, vol. 33,no. 3, pp. 388–399, Mar. 2008.
[12] E. Koutroulis, K. Kalaitzakis, and N. C. voularis, “Development of amicrocontroller-based, photovoltaic maximum power point tracking con-trol system,” IEEE Trans. Power Electron., vol. 16, no. 1, pp. 46–54,Jan. 1995.
[13] C. Hua, J. Lin, and C. Shen, “Implementation of a DSP-controlled pho-tovoltaic system with peak power tracking,” IEEE Trans. Ind. Electron.,vol. 45, no. 1, pp. 99–107, Feb. 1998.
[14] E. Koutroulis, K. Kalaitzakis, and V. Tzitzilonis, “Development of anFPGA-based system for real-time simulation,” Microelectron. J., vol. 40,no. 7, pp. 1094–1102, Jul. 2009.
[15] A. Mellit, H. Rezzouk, A. Messai, and B. Medjahed, “FPGA-based realtime implementation of MPPT-controller for photovoltaic systems,” Re-new. Energy, vol. 36, no. 5, pp. 1652–1661, May 2011.
[16] R. Faranda and S. Leva, “Energy comparison of MPPT techniques forPV systems,” WSEAS Trans. Power Syst., vol. 3, no. 6, pp. 446–455,Jun. 2008.
[17] H.-L. Tsai, C.-S. Tu, and Y.-J. Su, “Development of generalized photo-voltaic model using MATLAB/SIMULINK,” in Proc. World Congr. Eng.Comput. Sci., 2008, pp. 846–854.
[18] C. H. Lin, W. L. Hsieh, C. S. Chen, C. T. Hsu, T. T. Ku, and C. T. Tsai,“Financial analysis of a large-scale photovoltaic system and its impact ondistribution feeders,” IEEE Trans. Ind. Appl., vol. 47, no. 4, pp. 1884–1891, Jul./Aug. 2011.
[19] H. Hongwen, X. Rui, and F. Jinxin, “Evaluation of lithium-ion batteryequivalent circuit models for state of charge estimation by an experimentalapproach,” J. Energies, vol. 4, no. 4, pp. 582–598, Mar. 2011.
[20] R. L. Hartmann, Jr., “An aging model for lithium-ion cells,” Ph.D. disser-tation, Dept. Elect. Eng., Univ. Akron, Akron, OH, USA, 2008.
[21] R. D. Middlebrook and S. Cuk, “A general unified approach to modellingswitching-converter power stages,” in Proc. IEEE Power Electron. Spec.Conf., 1976, vol. 1, pp. 73–86.
[22] Y. Xiong, S. Sun, H. Jia, S. Patrick, and S. Z. John, “New physical in-sights on power MOSFET switching losses,” IEEE Trans. Power Electron,vol. 24, no. 2, pp. 525–531, Feb. 2009.
[23] L. Shuai, K. A. Corzine, and M. Ferdowsi, “A new battery/ultracapacitorenergy storage system design and its motor drive integration for hybridelectric vehicles,” IEEE Trans. Veh. Technol., vol. 56, no. 4, pp. 1516–1523, Jul. 2007.
Mohamed O. Badawy received the B.Sc. degree inelectrical engineering from Cairo University, Cairo,Egypt, in 2007 and the M.Sc. degree in electricalengineering from The University of Akron, Akron,OH, USA, in 2012, where he is currently workingtoward the Ph.D. degree in the Center of AdvancedVehicles and Energy Systems.
His research interests include power switching con-verters for renewable energy applications and power-electronics-based energy management techniques.
BADAWY et al.: PARALLEL POWER PROCESSING TOPOLOGY FOR SOLAR PV APPLICATIONS 1255
Ahmet S. Yilmaz (S’96–M’03) received the B.S.,M.S., and Ph.D. degrees in electrical engineeringfrom Sakarya University, Sakarya, Turkey, in 1994,1997, and 2002, respectively.
In 2003, he joined the Department of Electricaland Electronics Engineering, Sutcu Imam Univer-sity, Kahramanmaras, Turkey, where he is currentlyan Associate Professor. He was a Visiting Scholarin the Department of Electrical and Computer En-gineering at The University of Akron, Akron, OH,USA, in 2011–2012. His research interests include
renewable energy conversion systems and power quality.
Yilmaz Sozer (M’05) received the B.S. degree inelectrical engineering from the Middle East Tech-nical University, Ankara, Turkey, and the M.S. andPh.D. degrees in electric power engineering fromRensselaer Polytechnic Institute, Troy, NY, USA.His master’s and doctoral work focused on powerelectronics and the development of control algo-rithms for electric machines.
He is currently an Assistant Professor with theDepartment of Electrical and Computer Engineering,The University of Akron, Akron, OH, USA, engaged
in teaching and research. Before joining The University of Akron, he workedat Advanced Energy Conversion, Schenectady, NY, USA. His research inter-ests are in the areas of control and modeling of electrical drives, alterna-tive energy systems, design of electric machines, integrated and belt-drivenstarter/alternator systems, high-power isolated dc/dc converter systems, andstatic power conversion systems that interface energy storage and distributedgeneration sources with the electric utility.
Dr. Sozer has been involved in IEEE activities which support power elec-tronics, electric machines, and alternative energy systems. He is serving as anAssociate Editor for the IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
and Secretary for the IEEE Industry Applications Society Sustainable EnergyConversion Systems Committee.
Iqbal Husain (S’89–M’89–SM’99–F’09) receivedthe B.Sc. degree from Bangladesh University ofEngineering and Technology, Dhaka, Bangladesh, in1987 and the M.S. and Ph.D. degrees from TexasA&M University, College Station, TX, USA, in 1989and 1993, respectively.
He is currently the ABB Distinguished Professorin the Department of Electrical and Computer En-gineering at North Carolina State University (NCState), Raleigh, NC, USA, engaged in teaching andresearch. He is the Codirector of the Advanced
Transportation Energy Center and a faculty member at the NSF FREEDM Engi-neering Research Center at NC State. He was at The University of Akron priorto joining NC State, where he built a successful electric and hybrid vehiclesprogram. He was a Visiting Professor at Oregon State University, Corvallis, OR,USA, in 2001. He also worked as a summer researcher for Wright PattersonAFB Laboratories in 1996 and 1997. Previously, he taught as a Lecturer atTexas A&M University and also worked for Delco Chassis in Dayton, OH,USA, as a Consulting Engineer. His research interests are in the areas of controland modeling of electrical drives, design of electric machines, development ofpower conditioning circuits, and design and modeling of electric and hybridvehicle systems. He has worked extensively in the development of switchedreluctance and permanent-magnet motor drives for various automotive andindustrial applications.
Dr. Husain was the recipient of the 2006 SAE Vincent Bendix AutomotiveElectronics Engineering Award, the 2004 College of Engineering OutstandingResearcher Award, the 2000 IEEE Third Millennium Medal, and the 1998IEEE Industry Applications Society (IAS) Outstanding Young Member Award.He was also the recipient of the 2006 IEEE Industry Applications Magazinepaper award and four IEEE-IAS Committee prize paper awards. He was aDistinguished Lecturer of the IAS for 2012–2013.
