Supercapacitor-based Hybrid Storage Systems for Energy Harvesting in Wireless Sensor Networks

S. Saggini*, F. Ongaro*, C. Galperti**, P. Mattavelli***

* DIEGM, University of Udine, Udine, Italy. E-mail: stefano.saggini@uniud.it, fabio.ongaro@uniud.it ** DEI, Politecnico of Milano, Milano, Italy. E-mail: galperti@elet.polimi.it

*** DTG– University of Padova, Vicenza, Italy. E-mail: paolo.mattavelli@unipd.it

Abstract – This paper investigates a power management architecture based on a hybrid accumulator system that utilizes the supercapacitor cell as storage element for energy harvesting applications. The supercapacitor guarantees a longer lifetime in terms of charge cycles, it presents itself as a “green” technology compared to batteries and it has a wide range of operating temperature. The drawbacks of this type of solution are the low energy density and the leakage current that reduce the performance for very low power applications. In this paper a photovoltaic scavenger based on the supercapacitors is investigated and it can work only with supercapacitors, or together with the lithium battery cell in order to obtain a good compromise in terms of energy density and lifetime. A dedicated power management strategy is also proposed. Experimental results with a 5W photovoltaic energy source are reported.

Keywords – DC-DC converters, power management, wireless

sensor network, super-capacitors, battery charger

I. INTRODUCTION

The pervasive computing and network of wireless sensors are applications based on energy-autonomous system [1-4]. For example, a network of wireless sensors consists of a large number of micro-sensors distributed in an area of interest. Each node monitors the local environment and it shares this information with the other neighboring nodes by using a wireless link. The nodes should not require any maintenance and thus, they have to be energetically autonomous without the need of batteries replacement. In many application scenarios the targeted node lifetime ranges typically between 2 to 5 years and the need of energy harvesting is a primary issue in order to grant effectiveness of the wide-spread diffusion of this technology.

In principle, all energy sources should be exploited to extract the available energy; among the others, the solar one [1-5] is generally the most effective in outdoor applications. Solar cells exhibit a strong non-linear electrical characteristic and the extraction of energy is even more difficult in non-stationary environments. Variable operating conditions can be associated with weather changes (e.g., cloudy and non optimally radiating solar power environments) and aging effects or efficiency degradation in the solar panel (e.g., dust in the cell surface). Moreover, the energy transfer mechanism is strongly influenced by the illumination conditions, such as

the incidence angle of the sunlight, which varies along the day especially if the sensor node is in a mobile system.

Most of the solar energy harvesting solutions for wireless sensor nodes present a simple on/off-threshold charge mechanism relying on a diode connecting the cell with the rechargeable battery [6]. Unfortunately, a diode-based solution is extremely low cost, but the working point of the cell is set by the battery voltage and it cannot be adjusted to maximize the energy transfer in changing environment. This problem is addressed by substituting the diode-based solution with a Maximum Power Point Tracker (MPPT) system. This approach requires the development of adaptive systems to transfer the energy generated by the solar cell into storage elements, such as batteries or supercapacitors, while maintaining the working point of the PV cell around the optimal one.

In general terms, traditional MPPT circuits differentiate themselves in the design of the power converting electronics and/or in the control strategy. In most applications [6-9], the charging of high-density batteries in presence of fluctuating power sources remains an open issue. For example, Fig.1 reports the power obtainable by a photovoltaic panel, having 2W as the nominal power, in a mobile system constituted by a buoy on the sea. As can be observed, even if the weather condition is good, the irradiation is in the tropical condition and the temperature is maintained stable by the water, the available power has a high fluctuation due to the wave motion that changes the instantaneous orientation. This situation is usually not compatible with the charging of high density accumulators, like the lithium cell, because a precise charging profile, in terms of charging current and final voltage, must be implemented in order to prolong the lifetime [10] and the conditions reported in Fig. 1 determine a large variation of the charge and discharge cycles.

The solution proposed in this paper utilizes an hybrid accumulator architecture that combines the advantages of the supercapacitors in terms of the charge speed and instantaneous output power and the lithium cells for the stored available energy. In this architecture, the instantaneous power demand is supplied by the supercapacitor if the power generated by the converter is less than the power required for the load and the battery charge. If the technologies of the supercapacitors improve their performance in terms of leakage current and energy density, the structure can be simplified and only supercapacitors can be utilized.

978-1-4244-4783-1/10/$25.00 ©2010 IEEE 2281

II. PROPOSED POWER MANAGEMENT ARCHITECTURE

The proposed power management architecture is reported in Fig. 2, where three dc-dc converters are connected in parallel to the dc power bus. The first converter, denoted “dc-dc PV converter” interfaces the PV panel to the dc power bus, the second, denoted “dc-dc battery converter” connects the dc bus to the battery and the third, denoted “dc-dc supercap converter” connects the dc bus to the supercapacitor. The internal power bus is the main power supply of the electronic system utilized by sensor node. The power range of the proposed photovoltaic scavenger is in the range of 5W. The average power obtained by this source during a day is about 150mW, that is sufficient for our application where the average load power is 100mW. Moreover, the energy stored in the battery is designed in order to supply the load for the whole day.

The architecture reported in Fig. 2 is aimed to the maximization of the power conversion efficiency from the PV source to the load. In fact, when the power source is available, the energy flows directly from the source to the load through

the dc-dc PV converter. Moreover, with the power architecture of Fig. 2, it is possible to parallel different power sources. In fact, other energy sources can be connected by a converter to the same bus without changing the system architecture or the power management control. Thus, by including a hybrid accumulator system, the battery can be protected from overcharging and discharging stresses in order to prolong its lifetime. In fact, the extra power required by the load or generated by the fluctuating source is smoothed by the supercapacitors elements. In our case of study the dc bus is at 3.3V (VBusDC), the solar panel has a voltage of about 6V (VPV), the maximum voltage utilizable by the supercapacitor is of 2.5V and the lithium battery has a nominal voltage of 3.7V (VBattery).

The dc-dc PV converter is based on the Sepic topology as reported in Fig. 3. This solution guarantees small input current ripple and it is compatible with the voltage of the photovoltaic cell because, in CCM operation, the conversion ratio can be stepped down to the bus voltage:

D

DV

V

PV

BusDC−

=1

(1)

where D is the converter duty-cycle. In the implementation, a coupled inductor was used, thus reducing the magnetic element to one, as shown in Fig. 3. The input voltage of the converter is controlled by a feedback loop and the reference is determined either by the source MPPT or by the control on the supercapacitor voltage, depending on the state of the power management algorithm.

0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

H

Pow

er

Time (h)

Powe

r (W

)

Fig. 1 – Power obtainable by a photovoltaic cell mounted on a buoy on the sea

Fig. 2 – Power conversion system of the scavenger with super-capacitors

Fig. 4 – Bidirectional Buck converter connected to the super-capacitor

Fig. 3 – Coupled inductor Sepic converter utilized for Photovoltaic conversion with input voltage controller

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The dc-dc supercap converter is a bidirectional converter based on the synchronous buck topology, as reported in Fig.4; it operates in step-down mode when the power flows from the dc bus to the supercapacitor, and in step-up mode when the power flows in the opposite direction. The converter controls the dc bus voltage and it behaves as a sink or a source depending on the instantaneous power conditions. By utilizing the dc-dc converter, the supercapacitors are connected in parallel without requiring an overvoltage protection system on each element that is needed in the serial connection [11].

If the consumption of the wireless node is high during the period of time without irradiation, a lithium battery is used decreasing the size of the energy storage unit. During the charge of the battery the system works as in the previous description, and the supercapacitor system allows a stable charge process. When irradiation is not present and the supercapacitor system is almost discharged, the operation of the battery converter must be inverted by the power manager. In this case the energy required for the super-capacitor is the energy utilized to compensate the input energy variations with respect to the load requirement and the battery charge profile.

The dc-dc battery converter is a bidirectional Buck-Boost converter, as shown in Fig. 5, to charge and to utilize the battery at the same time depending on the working conditions. This choice is dictated by the required voltage level of the battery with respect to the dc power bus; in fact, the lithium battery has a nominal voltage of 3.7V and the dc bus voltage is 3.3V. This converter operates like a Buck-Boost by turning on the switch M2 and M3 during the “on phase”, and by turning on the switch M1 and M4 during the “off phase”. In CCM operation the conversion ratio is:

D

DVV

Battery

BusDC−

=1

(2)

This type of converter has a current controlled loop and the power management algorithm decides the current reference value, which is either positive or negative depending on the working conditions of the battery (charge or discharge).

III. POWER MANAGEMENT ALGORITHM

The proposed power management algorithm for the complete hybrid storage system is based on different control states defined by the charge state of the supercapacitor. The supercapacitor energy is used to switch from a state to another one because it inherently monitors the power balance of the conversion system. In Fig. 6 the state machine of the proposed power management is reported.

The first state is the state of “Turn-off”: from this state, the system wakes up only if some sources can deliver the energy required to start up the control electronic and, consequently, the system can proceed to the soft-start state. During this state the energy comes from the source and it is directed to the supercapacitor and to the controllers in order to maintain the

bus voltage soft-start. In this state, the control strategies are based on the MPPT of the sources and on the dc bus voltage regulation. The state machine switches from the soft-start state to battery charge state, when the dc bus voltage has reached its nominal values and the energy of the supercapacitor exceeds the reference energy ErefBC.

In these conditions, the dc bus voltage is controlled by voltage loop using the dc-dc supercap converter and the voltage, thus the energy, in the capacitor is regulated at ErefBC by changing the charging current of the battery if it is less than the maximum input current of the charge profile of the battery. When the battery charging current reaches the maximum value, the supercapacitor voltage becomes the degree of freedom during this state. When the supercapacitor voltage increases, the algorithm moves towards the Over-Power state. In this case, the source MPPT is switched to a control loop on the supercapacitor voltage that regulates the maximum energy to ErefOP, thus reducing the input power. Instead, when the input power reduces and it is less than the load power, the supercapacitor voltage decreases and the system moves to the Battery Help state. In this state, the control maintains the voltage stable for the dc bus, the MPTT for the source and it uses the battery as a source in order to maintain the voltage of the supercapacitor at ErefBH by modulating the battery current level. At the same time, the maximum current output level of the battery is limited by the control in order to prolong the battery lifetime. Moreover, in order to avoid extra discharge of the battery, the output current limit falls to zero if the voltage of the battery is lower than a threshold value.

Finally, if the input power is lower than the output power required by the load and the battery is discharged, the voltage across the supercapacitor decreases until it reaches the threshold voltage associated with the energy Eth1 and the system goes to the Turn-off state. Figure 6 summarizes the operation of the state-machine.

Fig. 5 – Bidirectional Buck-Boost converter connected to the Battery

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The value of the supercapacitor is based on the study of the instantaneous power variation of the source or the load in two main states of the algorithm: battery help and battery charge. If the system is correctly designed these two states switch during the day and the night.

By neglecting the converter losses, the instantaneous power on the supercapacitor in the battery charge state can be expressed as:

)()()()( arg tptptptp eChLoadSourceSC −−= (3)

and in the battery help state

)()()()( tptptptp LoadBatterySourceSC −+= (4)

If there is a load power increase or an input power reduction during the battery charge state, the battery charging current is reduced in order to regulate the voltage across the supercapacitor and under the worst case, it is reduced to zero. Under these conditions, (3) becomes:

)()()( tptptp LoadSourceSC −= (5)

In the battery help state, when the battery generates the maximum power available for a given current limit Imax, the power can be expressed as follow:

)()()( max tpVItptp LoadbatterySourceSC −+= (6)

The following analysis is based on the hypothesis that the instantaneous power generated or accumulated on the super-capacitor can be considered an uncorrelated random variable for event with a distance of Tsample. This interval depends on the fluctuation of the load and source power. Based on a statistical approach, the power generated or accumulated by the supercapacitor is described by a probability density function fPsc that is different depending on the state of the power manager (Battery Help or Battery Charge). Using fPsc , it is possible to express the probability of extracting power from the supercapacitor at a given time t as:

∫∞−

=<0

)()0)(( dppftpPSCpSC (7)

Let’s denote pSCgen(t) the power generated by super-capacitor:

)()( tptp SCSCgen −= . (8)

Thus, the probability that pSCgen(t) is greater than power P at a given time t is

)0)((

)(

)0)(|)((<

=<>∫∞−

tpP

dppf

tpPtpPSC

P

p

SCSCgen

SC

(9)

The extracted energy ESCgen is defined from the extracted power pSCgen:

∑=

+=N

nsamplesampleSCgenSCgen TTntpE

1)( (10)

where N is the number of subsequent events where pSC(t)<0. From (10), the probability density function of ESCgen is:

))0)((1()0)((

)()0)((

)0)(|(

11 <−

<

∑<

=

=<>

∑ ∫∞

= ∞− = tpPtpP

dppftpP

tpEEP

SCSC

n

TE

pn

SC

SCSCgen

sample

n

kSC

(11)

where ∑=

n

kSCp

f

1

is the probability density function of the sum

of pSC(t) when pSC(t)<0 . Then, the probability density function of the extracted

energy ESCgen is derived differentiating (11):

[ ]∑∞

=

−

∑<<−=

=1

1 )()0)(()0)((1)(

1n samplep

nSCSC T

eftpPtpPef n

kSC

Esc

(12)

For example, let’s consider the case where the input power is constant ( WtpSource 5.2)( = ), the system is working in battery charge mode and, when the load is active, the power load )(tpLoad is a random variable with an uniform distribution from 0 to 4 W with a sample time of Tsample=10 s. Using (12), the probability density function fEsc is reported in Fig. 7: it can be noted that in 90% of the cases, the energy supplied by the supercapacitor is less than 25J. Thus, in order to avoid to use the battery during the day, i.e. to avoid the battery help state, we impose:

JEE 25Th2RefBC>− (13)

Assuming ErefBC=2V and ETh2=1V, the supercapacitor value C should be greater than 16.67 F.

Fig. 6 – Block diagram of the state machine for the power management

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Fig. 7 – a) Density probability function of the energy required by the super-capacitor during the battery charge mode b) Cumulative distribution function of the energy required by the super-capacitor during the battery charge mode

Fig. 8 – a) Simulation of the power load requirement during the battery charge mode b) Super-capacitor voltage in the same condition with the two voltage levels referred to Battery charge mode (2V) and Battery help mode

(1V)

The same reasoning should be applied during the night

taking into account that the battery is able to supply a maximum current Imax=600mA. In these conditions, the input source is zero and the maximum power supplied by the battery is 2.2W. Moreover, when the load is active, the power load )(tpLoad is assumed to be a random variable with an uniform distribution from 0 to 2.5 W with a sample time of Tsample=10 s. Using (11), we found that in 90% of the cases the energy supplied by the supercapacitor is less than 3J. Thus, in order to avoid the turn-off state, we impose:

JEE 3Th1RefBH>− (14)

Assuming ErefBH=1V and ETh1=0.2V, the supercapacitor value C should be greater than 6.25 F.

IV. SIMULATION RESULTS

The converter connected to the input power is designed on the maximum input power coming from the source, which is 5W, the nominal power of the photovoltaic panel. The supercapacitor converter is designed to work in a range of power that depends on the maximum instantaneous power required by the load or generated by source. In our application the power fluctuation of the load and the source is approximately the same (5W). The converter operates in bidirectional mode with a minimum capacitor voltage of 200mV. In order to select the appropriate values of inductance and frequency of the sepic and supercapacitor converters, Figures 9a and 9b are used.

b)

P2

SC Converter

a)

P1

PV Converter

(H)

(H)

(Hz)

(Hz)

Fig. 9 – a) Efficiency of the Sepic converter as a function of inductance and frequency for the nominal voltage and current and P1 the value of the utilized parameters (L=22μH, Fsw=250kHz) b) The same for the bidirectional Buck converter and P2 the value of the utilized parameters (L=0.5μH, Fsw=1MHz)

Using MATLAB scripts, one for each converter, the losses

for every operating conditions are calculated: the inputs are the inductance, the frequency and the input current, while fixed parameters are the input and the output voltages. The program calculates the converter operating conditions (continuous or discontinuous conduction mode); detailed mosfets conduction and switching, DCR, dead times, ground resistance (valued in 50mΩ) and drivers losses are evaluated. As the conclusion of this calculation, the maximum value of the converter efficiency within the input power range is obtained and reported in Figs. 9a and 9/b; Figure 9a is used as the design criteria for the selection of L and fsw for the sepic converter and point P1 of Fig. 9a has been chosen (L=22 μH, Fsw=250 kHz); for the super-capacitor converter the inductor is chosen to optimize the efficiency, but the frequency of operation is chosen higher in order to reduce the ripple of the DC Bus voltage. Thus point P2 of Fig. 9b has been chosen (L=0.5μH, Fsw=1 MHz).

0 10 20 30 40 50 60 70 80 90 1000

1

2

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8x 10

-4

0 10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

)(efEsc

e e

)(eFEsc

90%

[J] [J]

a) b)

200 300 400 500 600 700 800 900 10000

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1

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pLoad(t) pSource(t)

Vc(t)[V]

[sec][sec]

[W]

a) b)

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The control system of both converters is based on the stabilization of the input voltage; in the Sepic case, the regulation is realized with a Proportional-Integral (PI) controller, which allows a dominant pole compensation, being the bandwidth requirement not very restrictive; the bidirectional Buck regulator instead, is a Proportional-Integral-Derivative (PID), which allows a faster stabilization of the input voltage level.

V. EXPERIMENTAL RESULTS

The power management system was experimentally verified on a prototype built using discrete Component Off The Shelf (COTS) reported in Fig. 10. The power manager is implemented by a microcontroller of the Microchip family (PIC18LF2620). The controller utilized for the sepic converter is a commercial IC (TPS43000). The converter works at 250kHz and it utilizes a coupled inductor of L=22μH with a coupling factor of 0.95 (Coilcraft MSD1260 series). The supercapacitor converter is a Buck converter that utilizes a 500nH inductor (Coilcraft SER2000 series) and it works at 1MHz, while the controller IC is ISL8118. In Fig. 11 the main converter waveforms are reported: channel 1 (CH1) is the gate of the low side transistor of the input converter, CH2 the signal of the bus DC voltage, CH3 the current to the supercapacitor and CH4 the gate of the high side transistor of the supercap converter. The efficiency of each stage has been measured: Fig. 12 reports the efficiency of the input stage with a fixed and

controlled input voltage. Figure 13a shows the efficiency of the supercapacitor converter in Buck operating mode, varying the input power at different supercapacitor voltages; Figure 13b shows the efficiency in Boost mode, where the power is extracted from the supercapacitor; the results reported in

0

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30

40

50

60

70

80

90

100

0 1 2 3 4 5

Eff

icen

cy[%

]

Pin [W]

Vmpp=6V

Fig. 12 – Efficiency of the Sepic input power stage, with a controlled input

voltage of 6V

0

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30

40

50

60

70

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0 1 2 3 4 5

Eff

icen

cy[%

]

Pout [W]

Vcap=2.5VVcap=1.5VVcap=1V

0

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0 1 2 3 4 5

Eff

icen

cy[%

]

Pin [W]

Vcap=2.5VVcap=1.5VVcap=1V

a)

b) Fig. 13 – a) Efficiency of the supercap power stage a) in Buck mode (sinking mode), b) in Boost mode (sourcing mode), over input power

Power Manager

PV ConverterSuper CAP Converter

Connection to optional Battery Converter

Fig. 10 - Bidirectional Buck-Boost and Sepic converter connected

Fig. 11 – CH1: gate of the low side transistor of the input converter, CH2: signal of the bus DC voltage, CH3: sinking current to the supercapacitor,

CH4: gate of the high side transistor of super cap converter.

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Figs 12-13 are in agreement with the estimated efficiency reported in Fig. 9. As a verification of the transient behaviour, the source is turned off and Fig. 14 reports the main waveforms of the proposed system without batteries, with a 50F supercapacitor as primary energy storage and with the load power constant and equal to 3W; in channel 1 (CH1) the input voltage of the sepic stage is reported, during its normal MPPT operation, in CH2 the dc bus voltage, in CH3 the he supercapacitor current and in CH4 its voltage. As can be seen, when the input voltage decreases and no energy flows in the dc bus, the supercap converter provides the holding up og the dc voltage with the supercapacitor energy.

Input Source off

Fig. 14 – Transient response of the circuit when no energy flows from the

input source in the dc bus. CH1: input voltage, CH2: dc bus, CH3 super cap current, CH4 super cap voltage.

VI. CONCLUSIONS

In this paper a power management system based on hybrid storage architecture composed by supercapacitors and lithium-ion batteries is proposed. This architecture, combined with the power management algorithm, ensures a controlled battery charge and the maximum power point tracking from the energy sources even in presence of large fluctuations of the sources and of the load, thus prolonging the battery lifetime. Moreover, the experimental results show high efficiency in the proposed conversion systems.

VII. REFERENCE

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[3] J.AParadiso, T.Staner,”Energy scavenging for mobile and wireless electronics”, IEEE Pervasive Computing Vol 4, January-March 2005, pp. 18-27.

[4] S.Roundy, E,S Lesland, J.Baker, E.Carleton, E.Reilly, E.Lai, B.Otis, J.MRabaey, P.KWright, V.SunDararajan,”Improving power output for vibration based energy scavengers”, IEEE Pervasive computing vol 4. January-March 2005, pp. 28-36.

[5] C.B.Williams, R.B. Yates, “ Analysis Of a Micro-electric generator for Microsystems” in the 8th international conference on the solid state sensor and actuators, vol.1 25-29 June 1995 pp.369-372.

[6] V. Raghunathan, A. Kasal, J. Hsu, J. Friedman, M. Srivastava, “Design Consideration for Solar Energy Harvesting Wireless Embedded System”, IEEE Internetional Conference on Information Processing in sensor networks (IPSN)2005, 15 April 2005 pp. 457-452.

[7] N. Lujara, J.D van Wyk, P.N. Materu, “Power electronics loss models of DC-DC converters in photovoltaic applications” in IEEE Internetional Symposium and on industrial Electronics, 1998. Volume 1, 7-10 July 1998, pp.35-39.

[8] M. Veerachary, T. SenJyu, K. Uezato, “Neural-Network-Based Maximum Power Point Traking of a Coupled-Inductor Interleaved-Boost Converter Supplied PV System Using Fuzzy Controller”, IEEE Transaction on Industrila Electronics Vol.50, No.4, August 2003, pp. 749-757.

[9] J.H.R Enslin, D.BSnyman, “Combinated Low Cost , High Efficient Inverte, Peak Power Tracker and Regulator for PV Applications”, IEEE Transaction on Power Electronics Vol.6 No.I January 1991, pp. 73-82.

[10] I. Kim; P.S. Ji, U.D. Han, C. Lhee, H.G. Kim,”State estimator design for solar battery charger”, IEEE International Conference on Industrial Technology, 2009. ICIT 2009, 10-13 Feb. 2009.

[11] C. Alippi, C. Galperti, “Energy sotrage mechanisms in low power embedded systems: Twin batteries and supercapacitors”, VITAE international conference on Wireless Comunication, Vehicular Tecnology, Information Theory and Aereospace & Electronic Systems Technology, 17-20 May 2009, pp 31-35.

[12] S. Saggini, P. Mattavelli, “Power management in multi-source multi-load energy harvesting systems”, European Conference on Power Electronics and Applications, EPE 2009, 8-10 Sept. 2009, pp 1–10.

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