Comparison of Charging Algorithms for Stand Alone Photovolatian Systems

7/27/2019 Comparison of Charging Algorithms for Stand Alone Photovolatian Systems

1/7

1469

Comparison of Battery Charging Algorithms forStand Alone Photovoltaic Systems

S. Armstrong*, M.E. Glavin*, W.G. Hurley** Power and Energy Research Center, National University of Ireland, Galway

Abstract The battery is the most common method ofenergy storage in stand alone solar systems; the most

popular being the valve regulated lead acid battery

(VRLA) due to its low cost and ease of availability.

Photovoltaics are not an ideal source for charging

batteries as their output is heavily dependent on weather

conditions. Therefore, when batteries are used in

photovoltaic systems, the performance characteristics

differ significantly from batteries used in more

traditional applications and the battery life is usually

shortened. In conditions of varying solar radiation and

load profile the battery may experience a low State of

Charge (SOC). A low SOC for extended periods of time

will cause increased sulphation, which severely reduces

the life of the battery. Typically, steps are carried out to

protect the battery and to charge the battery more

effectively. Such methods include Intermittent Charging

(IC), Three Stage Charging (TSC) and Interrupted

Charge Control (ICC), among others.

This paper quantifies the effectiveness of these three

battery charging algorithms and evaluates their ability to

maintain the battery at a high state of charge. The

measurement setup is comprised of a solar simulator,

which replicates the output of a large 50W photovoltaic

panel using a low power cell. Repeatable load and solar

radiation profiles and temperature control are

implemented using LabView so that identical operating

conditions can be set up to compare the three battery

charging systems.

I. INTRODUCTION

A typical photovoltaic system consists of a solar panel,regulator, battery and load [1-3]. Of all thesecomponents, it had been shown that the battery mayaccount for up to 40% of the overall system cost overits lifetime [4]. Batteries in photovoltaic systems aresubject to performance losses that are caused bylimited availability of time and energy to recharge thebattery and inadequate battery maintenance. In mostapplications, batteries are undercharged [5-8].Extended periods of undercharging leads to sulphationand stratification, which reduces the effectiveness ofthe battery and shortens its lifetime. Overcharging thebattery causes gassing and grid corrosion, which alsoshorten the battery life. Three battery chargingalgorithms are investigated to determine theireffectiveness at maintaining the battery at a high stateof charge and increasing the life cycle of the battery.

Their performance is monitored using a solar simulatormanagement system with programmable solarradiation, temperature and load profile.

The paper is divided into the following sections; first,the solar simulator management system is introduced.The battery charging algorithms used for comparisonare then discussed and experimental results for the

battery charging profiles, temperature and state ofcharge are presented.

II. SOLARSIMULATORMANAGEMENT SYSTEM

Photovoltaic applications are subjected to randomlyfluctuating atmospheric conditions, making the testingof a battery management system in real weathersituations both costly and time consuming. The use ofa solar simulator system provides a low cost solution,generating reliable results, which can replicate actualweather conditions. These reproducible conditions

enable different battery charging algorithms to betested under the same operating conditions. The mainparameters in a photovoltaic system are solar radiation,temperature and load conditions. A solar simulatormanagement system has been developed to simulatethese variables.Several solar simulators have been previouslydescribed. In [9] predetermined solar parameters areloaded into a lookup table using EPROM and aregulator produces the required output voltage. Thesolar cells characteristic equations may beimplemented by a microcontroller under varyingoperating conditions [10-12]. Solar simulators havealso been based on the equivalent model using a

current source in parallel with a diode [13].

The proposed solar simulator is more accurate fortemperature and solar radiation control in a fullyautomated system controlled by LabView. The blockdiagram of the solar simulator management system isshown in Fig. 1. The main components are the solarsimulator and dc-dc converter. These are described inmore detail in the following section.

978-1-4244-1668-4/08/$25.00 2008 IEEE


2/7


3/7

1471

Fig. 3(a): Intermittent Charging

Fig. 3(b): Three Stage Charging

Fig. 3(c): Interrupted Charge Control

IV. EXPERIMENTAL SETUP

A. Battery Managment System

The experimental setup for the solar simulatormanagement system is shown in Fig. 4. The user maycontrol the choice of battery charging algorithm, solarradiation and load control for every hour of a particular

day. Accurate and automated control of the radiation,temperature levels and load profile allows testing over awide range of operating conditions and permitsrepeatable patterns of temperature and radiation toprovide a platform to compare the various chargingalgorithms. The solar simulator outputs and thebatterys voltage, current, temperature and state ofcharge are recorded by LabView to ascertain the abilityof the charging algorithms to maintain the battery at ahigher state of charge.

Fig 5: Example of the two pulse test for state of

charge detection

B. State of Charge Two Pulse Test

In order to properly compare the battery chargingalgorithms, identical starting conditions need to beestablished. The state of charge (SOC) of the batteriesare determined by a two pulse method described in[18]. The SOC of a battery is defined as the ratio of thedischargeable Ampere-hour to the current Ampere-hour capacity of the battery. The starting SOC of eachbattery was set as 65%. A screenshot of the two pulsetechnique is shown in Fig. 5. The battery is first left inopen circuit for a minute so that it may recover fromany recent load activity. Two current pulses of 12.5A

are supplied to the battery with a duty cycle of 33%.The first pulse is responsible for stabilising the batteryrelative to its previous history or activity. The secondpulse establishes the parameters necessary to determinethe state of charge. The voltage of the battery at thebeginning and end of the second pulse, Vmax and Vminrespectively are used to determine the state of chargeof the battery.

Fig. 4: Experimental Setup


4/7

1472

V. EXPERIMENTAL RESULTS

A. Charging Profiles

The charging profiles of the Intermittent Charging,Three Stage Charging method and Interrupted ChargeControl method are shown in Fig. 6, 7 and Fig. 8respectively. These are the resultant charging profilesfrom the solar radiation pattern shown in Fig. 9. Theoperating parameters for each charging algorithm areshown in Table 1.

TABLE I.

In Intermittent Charging (IC) in Fig. 6, the lower andupper voltage thresholds are recommended by [19]. Atthis threshold, the battery is charged to 95% state ofcharge. The choice of the upper and lower thresholdlimits need to be chosen carefully because if theparameters are too close together then the charging willbegin too early and it will operate close to floatcharging. If the parameters are chosen too far apartthen the battery will discharge to an unacceptable lowstate of charge. Under a simulated solar day, the lowervoltage threshold is not reached as the VRLA batterytakes longer than twenty hours to drop to thisthreshold. The rate of the voltage drop depends on theage of the battery; an older battery with a lower state ofhealth will have a higher rate of self-discharge.

Fig. 7 shows the charging profiles of the Three Stagecharging method. This charging method brought thebattery to 100% state of charge at the end of the day.

Fig. 6: Intermittent Charging profiles

Fig. 7: Three stage charging profiles

Fig. 8 shows the charging profile of the ICC method.The battery charging current is maintained at 0.1C(1.6A for a 16Ah battery) until the upper voltagethreshold is met (Mode I). At this stage, the battery isleft in open circuit and the battery is at 98% SOC(Mode II). Under the simulated solar day, the batteryvoltage does not fall to 13.0V; therefore the batteryremains in Mode II.

Fig. 8: ICC charging profile

Charging Algorithm Important Parameters

Intermittent Charging Upper Voltage Threshold: 14.2VLower Voltage Threshold: 12.84V

Three Stage Charging Upper Voltage Threshold: 14.2VLower Voltage Threshold: 13.2V

Interrupted Charge Control Upper Voltage Threshold: 14.7VLower Voltage Threshold: 13.0V


5/7

1473

Fig. 9: Solar simulator measurements over the course of a

simulated day

B. Variable Load Profiles

The performance of the battery under a varying loadprofile was also investigated. The load profile is shownin Fig. 10, featuring high load demand in the morningand afternoon. The effect of the varying load profile onthe batterys SOC will be demonstrated in Section V.

Fig. 10: Variable load profile

V. COMPARISON OF THE BATTERY CHARGING REGIMES

In this section, the three battery charging algorithmsare compared under different criteria; the ability tomaintain the battery at a high state of charge, chargingefficiency and effect of charging algorithm on thebatterys temperature.

A. State of Charge

The performance of the three battery chargingalgorithms was first investigated under no load

conditions. Figure 11 shows the state of charge of thebattery under each battery charging algorithm.

Evidently, the Three Stage Charging algorithm restoresthe battery to 100% SOC in the quickest time, althoughsome overcharge is apparent. Intermittent Chargingrestores the battery to 95% SOC but the batteryremains in this state as the battery will take over

twenty four hours to drop to the lower voltagethreshold under no load conditions. The ICC chargesthe battery to 98%. Similarly, the battery remains atthis point as the battery is also left in open circuit. Thischarging algorithm takes longer to reach the same levelof SOC because the battery charging current is limitedto 1.6A (0.1C) whereas the other two algorithms usethe maximum solar panel current available byincorporating maximum power point tracking.

Fig. 11: SOC of the charging algorithms over the day

(under no load conditions)

Fig. 12 shows the state of charge of the battery underthe varying load profile shown in Fig. 10. Once more,the Three Stage Charging method returns the battery tofull state of charge in the quickest time possible. TheIntermittent Charging algorithm also fully rechargesthe battery, although some overcharging occurs. This isbecause the choice of the voltage threshold does notcorrespond to the 100% state of charge condition. Thevoltage at which overcharging begins is dependent onthe charge rate which varies due to atmosphericconditions [19]. When the battery voltage drops below

the lower voltage threshold, maximum power pointtracking is implemented. This delivers all the availablecurrent to the battery that is not required by the load,causing overcharging. The jagged edge in theIntermittent Charging SOC curve is caused by thefrequent transition between Mode 2 and Mode 3,where the battery is constantly connected anddisconnected. The overcharging caused by the ThreeStage Charging method is less significant because thecharging current has been decreased in Mode 2 and 3.The SOC is lowest for the Interrupted Charge Controlmethod. Mode 1 limits the battery charging current to1.6A therefore the charging algorithms is unable torestore the charge to the battery that has been drawn by

the load and always remains in Mode I.


6/7

1474

Fig. 12: SOC under varying load profile

B. Charging Efficiencies

Fig. 13 shows the charging efficiencies of the threedifferent charging algorithms under no load. Thecharging efficiency is determined by the fraction of solarenergy available that is supplied to the battery. Energy issupplied to the battery at a rate which is determined bythe batterys state of charge. The starting state of chargeof the battery is identical for each charging algorithm andeach one is subjected to the same operating conditions sothat charging efficiencies can be compared. Theexperimental results show that the Three Stage Chargingmethod has the highest efficiency over the course of theday. The Intermittent Charging method shows high

efficiency as it recharges the battery to 95% SOC butonce this condition has been reached, the battery isdisconnected and the solar energy available is not fullyutilized. Similarly, the efficiency of the InterruptedCharge Control Method is decreased when 98% SOC hasbeen reached. The overall efficiency of this algorithm isthe lowest as the full solar panel current is not utilized.

Fig. 13: Comparison of the charging efficiencies

(under no load conditions)

C. Temperature effects

The battery temperature was also monitored toinvestigate the effect of the different charging algorithms.

The operating temperature has a profound effect on thebattery. It affects the chemical reactions of the batterywhich in turn determines the amount of useable chargeavailable. For every 10 degree rise in temperature, thechemical reaction rates double, leading to possiblethermal runaway and more rapid self discharge [17].Temperature compensation may be implemented tocorrect the voltage thresholds and charge rates of thecharging regimes in order to avoid overcharging orundercharging the battery [20].

The charge rate of the battery charging algorithmsdetermines the temperature response of the battery. Thetemperature response of the battery under the three

different charging algorithms is shown in Fig. 14.Evidently, the Three Stage Charging method results inthe highest temperature rise. The float voltage is presentacross the battery terminals even after it is fully charged.A high percentage of the float current goes intoovercharge which can cause grid corrosion and potentialgassing. The Intermittent Charging algorithm shows arise in battery temperature until the upper voltagethreshold is met. At this point the battery charger isdisconnected; therefore the temperature begins to drop.The ICC charging algorithm results in the lowest averagebattery temperature due to the fact that the majority ofthe charge is returned to the battery at a charge rate of

0.1C in Mode 1, whereas the other two algorithms usemaximum power point tracking. The peak temperatureoccurs when the SOC is equal to 98% and subsequentlybegins to drop. This lower operating temperature isbeneficial in prolonging the lifespan of the battery.

Fig. 14: Battery temperature during the different

charging algorithms


7/7

1475

The experimental data shown has demonstrated thatthe Three Stage Charging method is the most suitablebattery charging algorithm for a photovoltaic system.This method returns the most charge to the battery inthe quickest time without causing a significant rise in

the battery temperature. The peak temperature of theThree Stage Charging method is only one degreehigher than the Intermittent Charging peaktemperature. The Interrupted Charge Control method isless effective at returning the battery to a full state ofcharge than the other two algorithms. However, theoperating temperature of the battery is lower whichaids in prolonging the life of the battery. Underregularly cycled conditions, ICC method does notreach Mode III, therefore the full benefits of thisregime are not exploited. Mode III reduces the degreeof overcharging experienced by the other chargingregimes and provides equalization of the cell voltagesin a string of batteries. The results suggest that this

charge regime would be better utilised in a standbyapplication.

VIII. CONCLUSIONS

The Three Stage Charging method has beenestablished at the most suitable battery chargingalgorithm for a regularly cycled battery in aphotovoltaic system. A fully automated solar simulatormanagement system was used to accurately comparethe performance of battery charging algorithms duringvarying atmospheric and load conditions.

REFERENCES

[1] IEEE recommended practice for testing the performance ofstand-alone photovoltaic systems. IEEE Standard 1526, 2003,

pp.11-18, 2004.[2] IEEE guide for selection, charging, test, and evaluation of leadacid batteries used in stand-alone photovoltaic (pv) systems IEEEStandard 1361, 2003.[3] Stand alone photovoltaic system design principles. ER120,Renewable Energy, February 21, 2002.[4] S. Duryea, S. Islam, W. Lawrance, A battery managementsystem for stand-alone photovoltaic energy systems, IEEE IndustryApplications Magazine, Vol. 7, Issue: 3, pp. 67-72, 1999.[5] E. Lorenzo, L. Narvarte , K. Preiser, R. Zilles, A fieldexperience with automotive batteries in shss., Proceeding of the

2nd World Conference and Exhibition on Photovoltaic Solar EnergyConversion, Vienna, pp. 3266-3268, 1998.[6] P. Diaz, M.A. Egido, Experimental analysis of battery chargeregulation in photovoltaic systems. Progress in Photovoltaics:Research and Applications, Vol. 11, No. 7, pp. 481-493, November2003.[7] H. Yang, H. Wang, G. Chen, G. Wu, Influence of the chargeregulator strategy on state of charge and lifetime of VRLA battery inhousehold photovoltaic systems, Solar Energy, Vol. 80, pp. 281-287, March 2006.[8] J. Garche, A. Jossen, H. Doring, The influence of differentoperating conditions, especially over-discharge, on the lifetime and

performance of lead acid batteries for photovoltaic systems. Journalof Power Sources, Vol. 67, pp. 201-212, August 1997.[9] S.H. Lloyd, D.G. Infield, "Design and construction of a modularelectronic photovoltaic simulator", Power Electronics and VariableSpeed Drives Conference Proceedings, No. 475, pp: 120-123, 2000.[10] K. Kachtsevanos, G. Vachtsevanos, "A hybrid photovoltaicsimulator for utility interactive studies", IEEE Transactions onEnergy Conversion Vol. 2, pp: 227-231, 1987.[11] J. Yoo. J. Gho, G. Choe, Analysis and control of pwmconverter with v-i output characteristics of solar cell, IEEEInternational Symposium on Industrial Electronics (ISIE)Proceedings, Vol. 2, pp: 1049-1054, 2001.[12] K. Khouzam, K. Hoffman, Real-time simulation of

photovoltaic modules, Solar Energy, Vol. 56, No. 6, p. 521-526,1996.[13] Appelbaum, J. Starting and steady-state characteristics of DCmotors powered by solar cell generators. IEEE Transactionson Energy Conversion, EC-1, pp: 17-23, 1986.[14] F. Huang, D. Tien, J. Or, A microcontroller based automaticsun tracker combined with a new solar energy conversion unit,Proceedings of the International Conference on Power ElectronicsDrives and Energy Systems for Industrial Growth, Vol. 1, pp: 488 492, 1998.[15] P. Waltari, T. PSuntio, A. Tenno, R. Tenno, The effects ofintermittent charging on VRLA battery life expectancy in telecomapplications, 24th Annual International TelecommunicationsEnergy Conference, pp: 121-127, 2002.[16] www.xantrex.com

[17] M. Bhatt, W.G. Hurley, W.H. Wolfe, A new approach tointermittent charging of valve regulated lead acid batteries in standbyapplications, IEEE Trans. on Indust. Electronics, Vol. 52, 5, 2005.[18] M. Coleman, C.K. Lee, W.G. Hurley, State of healthdetermination: two pulse load test for a VRLA battery, 37 th IEEEPower Electronics Specialists Conference, 2006, pp: 1 6.[19] E. Koutroulis, K. Kalaitzakis, Novel battery chargingregulation system for photovoltaic applications, IEEE Proc. PowerAppl, Vol . 151, No. 2, pp: 191-197, 2004.[20] Y.S. Wong, W.G. Hurley, W.H. Wlfle, Temperaturecompensation algorithm for interrupted charge control regimefor a VRLA battery in standby applications, 23 rd IEEE AppliedPower Electronic Conference, 2008.

Comparison of Charging Algorithms for Stand Alone Photovolatian Systems

Documents

Transcript of Comparison of Charging Algorithms for Stand Alone Photovolatian Systems