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Energy Harvesting Throurgh Piezoelectric Materials

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Rajeev Kumar Energy Harvesting through Piezoelectric Material Computational Intelligence Applications to Renewable Energy-2012 Rajeev Kumar School of Engineering IIT Mandi 1
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Page 1: Energy Harvesting Throurgh Piezoelectric Materials

Rajeev Kumar

Energy Harvesting through Piezoelectric Material

Computational Intelligence Applications to Renewable Energy-2012

Rajeev KumarSchool of Engineering

IIT Mandi

1

Page 2: Energy Harvesting Throurgh Piezoelectric Materials

Outlines of the presentation

�Introduction

�Energy Scavenging through Vibration

�Thermodynamic of piezoelectric material

�Piezoelectric Frequency Response

�Piezoelectric Energy Harvester

Computational Intelligence Applications to Renewable Energy-2012 2

�Piezoelectric Energy Harvester

�Engineering Design Process

�Finite Element Analysis of Layered Piezoelectric Ma terial

�Optimization of Piezoelectric Energy Harvester by G enetic Algorithm

Page 3: Energy Harvesting Throurgh Piezoelectric Materials

Energy harvesting or the process of acquiring energy from thesurrounding environment has been a continuous humanendeavor throughout history.

Energy harvesting

Introduction

Computational Intelligence Applications to Renewable Energy-2012 3

Page 4: Energy Harvesting Throurgh Piezoelectric Materials

Need of Energy Harvesting

• Growing need for renewable sources of energy

• Proposes several potentially inexpensive and highly effective solutions

• Reduce dependency on battery power

Introduction (Cond..)

Computational Intelligence Applications to Renewable Energy-2012

• Reduce dependency on battery power

• Complexity of wiring

• Increased costs of wiring

• Reduced costs of embedded intelligence

• Increasing popularity of wireless networks

• Limitations of batteries

• Reduce environmental impact

Page 5: Energy Harvesting Throurgh Piezoelectric Materials

Available energy sources in the environment

Introduction (Cond..)

Computational Intelligence Applications to Renewable Energy-2012

Available energy sources

Page 6: Energy Harvesting Throurgh Piezoelectric Materials

Energy Scavenging through Vibration

Computational Intelligence Applications to Renewable Energy-2012 6

Page 7: Energy Harvesting Throurgh Piezoelectric Materials

Type Advantage Disadvantage

Piezoelectric 1. No separate voltage source2. Voltages of 2 to 10 Volts3. No mechanical stops4. Highest energy density

1. Micro fabrication processes are not compatible with standard processes and piezo thin films have poor coupling.

Electrostatic 1. Easier to integrate with 1. Separate voltage source

Energy Scavenging through Vibration (Cond..)

Computational Intelligence Applications to Renewable Energy-2012

From this comparison it is clear that the most desirable conversion method resultsthat piezoelectric one which presents the major number of advantages. So, it is forthese reasons that this is currently the best choice to realize the micro vibrationdriven generator for energy harvesting to power sensor nodes.

Electrostatic 1. Easier to integrate with electronics and microsystems

2. Voltages of 2 to 10 Volts

1. Separate voltage source required

2. Mechanical stops needed

Electromagnetic 1. No separate voltage source2. No mechanical stops

1. Max. voltage of 0.1 volt2. Difficult to integrate with

electronics and microsystems

Page 8: Energy Harvesting Throurgh Piezoelectric Materials

Examples of common vibration sources

Energy Scavenging through Vibration(Cond..)

Computational Intelligence Applications to Renewable Energy-2012 8

Page 9: Energy Harvesting Throurgh Piezoelectric Materials

Thermodynamic of piezoelectric material

Computational Intelligence Applications to Renewable Energy-2012 9

Page 10: Energy Harvesting Throurgh Piezoelectric Materials

Direct Piezo Effect:

The phenomenon of generation of a voltage under mechanical stress is referred to as the direct piezoelectric effect.

Thermodynamic of piezoelectric material ( Cond..)

Computational Intelligence Applications to Renewable Energy-2012 10

Page 11: Energy Harvesting Throurgh Piezoelectric Materials

Thermodynamic of piezoelectric material ( Cond..)

The mechanical strain produced in the crystal under electric voltage is referred as converse piezoelectric effect.

Converse Piezoelectric Effect

Computational Intelligence Applications to Renewable Energy-2012 11

Page 12: Energy Harvesting Throurgh Piezoelectric Materials

The phenomenon of generation of a electric field, when thetemperature of the crystal is raised or lowered is referred to as thePyroelectric effect.

Pyroelectric Effect

Thermodynamic of piezoelectric material ( Cond..)

Computational Intelligence Applications to Renewable Energy-2012 12

Page 13: Energy Harvesting Throurgh Piezoelectric Materials

Thermodynamic of piezoelectric material ( Cond..)

Piezoelectric Material (Material with Piezoproperties )

:

Naturally occurring crystals : Berlinite (AlPO4), Cane sugar, Quartz, Rochelle salt, Topaz,Tourmaline Group Minerals, and dry bone (apatite crystals)

Man-made ceramics :Barium titanate (BaTiO3), Lead titanate (PbTiO3), Lead zirconate

Computational Intelligence Applications to Renewable Energy-2012

Barium titanate (BaTiO3), Lead titanate (PbTiO3), Lead zirconatetitanate (Pb[ZrxTi1-x]O3 0<x<1) - More commonly known as PZT,Potassium niobate (KNbO3), Lithium niobate (LiNbO3), Lithiumtantalate (LiTaO3), Sodium tungstate (NaxWO3), Ba2NaNb5O5,Pb2KNb5O15

Polymer :Polyvinyledene fluoride (PVDF)

13

Page 14: Energy Harvesting Throurgh Piezoelectric Materials

Polarization of Piezoelectric Material

Thermodynamic of piezoelectric material (Cond..)

Computational Intelligence Applications to Renewable Energy-2012 14

Page 15: Energy Harvesting Throurgh Piezoelectric Materials

Applications of Energy Harvesting through Piezoelectric Material

• The best-known application is the electricCIGARETTE LIGHTER: pressing the buttoncauses a spring-loaded hammer to hit apiezoelectric crystal, producing a sufficientlyhigh voltage electric current that flows acrossa small spark-gap, thus heating and ignitingthe gas.

Computational Intelligence Applications to Renewable Energy-2012 15

the gas.

• Gas burners now have built-in piezo-basedignition systems .

• Battery-less wireless doorbell push button

• The armed forces toyed with the idea ofputting piezoelectric materials in soldiers bootsto power radios and other portable electronicgear

Page 16: Energy Harvesting Throurgh Piezoelectric Materials

• Several nightclubs,mostly in Europehave alreadybegun to powertheir strobes andstereos using theforce of hundreds

Applications of Energy Harvesting through Piezoelectric Material ( Cond..)

Computational Intelligence Applications to Renewable Energy-2012

force of hundredsof people poundingon piezoelectriclined dance floors

16

Page 17: Energy Harvesting Throurgh Piezoelectric Materials

Applications of Energy Harvesting through Piezoelectric Material ( Cond..)

• Several gyms, notable in Portland and a few other places arepowered by a combination of piezoelectric set ups and generatorsset up on stationary bikes.

Computational Intelligence Applications to Renewable Energy-2012 17

Page 18: Energy Harvesting Throurgh Piezoelectric Materials

• Laying piezoelectric crystalarrays underneath sidewalks,stairwells, and pretty much anyother high traffic area to powerstreet lights.

Applications of Energy Harvesting through Piezoelectric Material ( Cond..)

Computational Intelligence Applications to Renewable Energy-2012 18

Page 19: Energy Harvesting Throurgh Piezoelectric Materials

• Piezoelectric Powered MusicInstruments

Applications of Energy Harvesting through Piezoelectric Material ( Cond..)

Computational Intelligence Applications to Renewable Energy-2012 19

Page 20: Energy Harvesting Throurgh Piezoelectric Materials

Applications of Energy Harvesting through Piezoelectric Material (Cond..)

• Capitalizing on the friction and heat created bywalking, running and even just wearing jeans,engineers from Michigan TechnologicalUniversity, Arizona State University devised away to use this type of generated energy tocharge portable electronic devices, like iPodsandmobilephones.

Computational Intelligence Applications to Renewable Energy-2012

andmobilephones.

• Biomechanical Energy Harvester

• Energy harvesting byPiezoelectric windmills

20

Page 21: Energy Harvesting Throurgh Piezoelectric Materials

Identify the need or problem

Research the need or problem

Modify to improve the design if needed

Engineering Design Process

Computational Intelligence Applications to Renewable Energy-2012 21

Develop possible solutions

Select the best solutionsConstruct a

prototype

Test and evaluate the prototype

Detailed Design

Page 22: Energy Harvesting Throurgh Piezoelectric Materials

Analyze the problem

Detailed Design

Optimization of the problem

Detailed Drawing of the problem

Engineering Design Process (Contd..)

Computational Intelligence Applications to Renewable Energy-2012 22

the problem

Page 23: Energy Harvesting Throurgh Piezoelectric Materials

Piezoelectric Energy Harvester Model

The electromechanical model of this structure can be represented

Computational Intelligence Applications to Renewable Energy-2012

The electromechanical model of this structure can be represented by the following set of differential equations

[ ]{ } [ ]{ } [ ] [ ][ ] [ ]( ){ } { } [ ]{ }θθφφφφ umuuuuuuuu kFqkkkkqcqmsss 2

11 +=+++ −&&&

[ ][ ] [ ]{ } [ ]{ }auu asskkkk φθ φφφφ φθ

−− −1

2

1

{ } [ ] [ ]{ } [ ]{ }

+= − qkkk us sss φθφφφ θφ2

11

Harvested power

Page 24: Energy Harvesting Throurgh Piezoelectric Materials

Piezoelectric Frequency Response

Piezoelectric energy harvester is only effective under a narrowbandwidth of excitation frequency. If the excitation frequency shiftsfrom this band, the power density of the harvester will significantlydecrease

Computational Intelligence Applications to Renewable Energy-2012 24

Page 25: Energy Harvesting Throurgh Piezoelectric Materials

•If the excitation frequency shifts from this band, the power densityof the harvester will significantly decrease.

•Different strategies have been used to enhance the harvestingperformances when the vibration source has a larger frequencybandwidth.

Piezoelectric Energy Harvester Model ( Cond..)

Computational Intelligence Applications to Renewable Energy-2012

•One of them is to use an array of harvesters which consists ofmultiple harvesters having different resonance frequencies in orderto increase the harvested power on a wider frequency bandwidth.However, this leads to a harvester having a higher volume, whichdecreases its power density (mW.cm-3).

•Therefore Design optimization is required

Page 26: Energy Harvesting Throurgh Piezoelectric Materials

Piezoelectric Energy Harvester Model ( Cond..)

Computational Intelligence Applications to Renewable Energy-2012

Page 27: Energy Harvesting Throurgh Piezoelectric Materials

�The cost function of this first optimization problem is to maximizethe mean power density over a certain frequency bandwidth.�The power density is defined as the ratio of the harvested Powerand the harvester volume

Piezoelectric Energy Harvester Model ( Cond..)

Objective function

Computational Intelligence Applications to Renewable Energy-2012

Page 28: Energy Harvesting Throurgh Piezoelectric Materials

{ } [ ] { } [ ] { } { } Θ−−= kkkk EeQ λεσ

Direct Piezoelectric effect

Constitutive relation

Finite Element Modeling

Converse piezoelectric effect

Computational Intelligence Applications to Renewable Energy-2012 28

Direct Piezoelectric effect

{ } [ ] { } { } { } { } Θ++= kk

T

kk PEbeD ε

For a non piezoelectric layer

{ }oek

=

{ } { }0=kP { } { }oE k =

Page 29: Energy Harvesting Throurgh Piezoelectric Materials

Where [ ] [ ] [ ] [ ] [ ][ ]θε TTTeTe kokvkΤ=

[ ] [ ] [ ] [ ]vkvk TbTb Τ=

{ } [ ] { }kvk pTp Τ=

[ ] [ ] [ ] [ ] [ ] [ ] [ ][ ]θεεθ TTTQTTTQ ΤΤΤ=

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012

{ } [ ] [ ] [ ] { }kkok TTT λλ εθΤΤΤ=

[ ]εT

][ oT

[ ]θT

[ ]vT

[ ] [ ] [ ] [ ] [ ] [ ] [ ][ ]θεεθ TTTQTTTQ kokkok =

Strain transformation matrix

Ply orientation transformation matrix

Rotational transformational matrix

Vector transformation matrix

Page 30: Energy Harvesting Throurgh Piezoelectric Materials

[ ]

( ) ( )

( ) ( )

−−

−−

=

12

2112

2

2112

212

2112

212

2112

1

00000

000000

000011

000011

G

vv

E

vv

Evvv

Ev

vv

E

Q

{ }

=

012

3

2

1

λλλλ

λ

{ }

=

3

2

1

p

p

p

p

Elastic stiffness coefficients matrix Stress coefficients vector

Pyroelectric coefficients vector

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012

13

23

12

00000

00000

00000

SFG

SFG

G

0

0

{ }

=

363534333231

262524232221

161514131211

eeeeee

eeeeee

eeeeee

e

{ }

=

13

23

12

3

2

1

τττσσσ

σ [ ]

=

3

2

1

00

00

00

b

b

b

b

6

5SF = shear correction factor =

Piezoelectric coefficient matrix

Stress vector Dielectric constant matrix

Page 31: Energy Harvesting Throurgh Piezoelectric Materials

[ ] ( ) ( ) ( )( ) ( ) ( )( ) ( ) ( )

+++++++++

=

233223322332323232

122112211221212121

33333323

23

23

22222222

22

22

11111121

21

21

222

222

222

lnlnnmnmmlmlnnmmll

lnlnnmnmmlmlnnmmll

lnlnnmnmmlmlnnmmll

lnnmmlnml

lnnmmlnml

lnnmmlnml

[ ]

[ ][ ]

[ ][ ]

=

θ

θ

θ

θ

θ

t

t

t

t

T

000

000

000

000

Strain transformation matrixRotational transformation matrix

Transformation matrices

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012

( ) ( ) ( )

+++ 311331133113131313 222 lnlnnmnmmlmlnnmmll

[ ]

=

333

222

111

000

000

000

000100

000010

000001

nml

nml

nmltθ

[ ]

−−

=

cs

sc

sccscs

cscs

cssc

To

0000

0000

00022

000100

000

000

22

22

22

[ ]

=

333

222

111

nml

nml

nml

Tv

Ply orientation transformation matrix

θθ

sin

cos

==

s

c nml ,, Direction cosines between local and global axis

Vector transformation matrix

Page 32: Energy Harvesting Throurgh Piezoelectric Materials

zyx ′′′ ,, ζηξ ,,zyx ,,

9-node degenerate shell elementThree Co-ordinate system

Shape Function

( )( )11)1(4

1 −+++= iiiiiN ηηξξηηξξ

( )1

i=1,2,3,4

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012 32

( ) )1(12

1 2iiN ηηξ +−=

( )( )iiN ξξη +−= 112

1 2

( )22 1)1( ηξ −−=iN

i=5,7

i=6,8

i=9

Page 33: Energy Harvesting Throurgh Piezoelectric Materials

+

=

bottomi

i

i

topi

i

i

middlei

i

i

z

y

x

z

y

x

z

y

x

2

1

Nodal Coordinates

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012 33

bottomtopmiddle

=

=

bottomi

i

i

topi

i

i

ii

i

i

i

z

y

x

z

y

x

tn

m

l

V1

3

3

3

3

r

( ) ( ) ( )( ) 2/1222ibottomitopibottomitopibottomitopi zzyyxxt −+−+−=

9-node degenerate shell element

Page 34: Energy Harvesting Throurgh Piezoelectric Materials

iii

i

middlei

i

i

ii VtN

z

y

x

N

z

y

x

32

rζ∑∑ +

=

Relation between the Co-ordinate systems

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012 34

=

==

=9

13

3

9

1

3

3

3

3

iii

ii

i

VN

VN

n

m

l

Vr

r

r

3

3

1

1

1

1Vi

Vi

n

m

l

V r

rrr

×

×=

= 13

2

2

2

2 VV

n

m

l

Vrrr

×=

=

Page 35: Energy Harvesting Throurgh Piezoelectric Materials

Displacement Field

iii

i

npi

i

innel

ii VHN

z

y

x

N

z

y

x

3

r

∑∑ +

=

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012 35

ii kk

oki ttH2

ζ+=

( ) [ ]

−+

=

∑= i

iii

i

i

innel

ii VVH

w

v

u

N

w

v

u

βα

ζηξ 211

,rr

Page 36: Energy Harvesting Throurgh Piezoelectric Materials

{ } [ ] { }eeu

i

i

i

i

i

nnel

iiiiii

iiiii

iiiii

e qNw

v

u

HNnHNnN

HNmHNmN

HNlHNlN

w

v

u

u =

−−−

=

= ∑

αβ12

12

12

00

00

00

Displacement & Strain Field

ε

∂∂∂

x

u

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012 36

{ }

=

zx

yz

xy

z

y

x

εεεεεε

ε

∂∂+

∂∂

∂∂+

∂∂

∂∂+

∂∂

∂∂∂∂∂

z

u

x

wy

w

z

vx

v

y

uz

wy

vx

=

Page 37: Energy Harvesting Throurgh Piezoelectric Materials

oi

oi

nnel ziiziii

yiiyiii

xiixiii

v

u

gngnN

gmgmy

N

glglx

N

−∂

−∂

−∂

12

12

12

00

00

00

Finite Element Modeling (Cond..)

Strain Field

Computational Intelligence Applications to Renewable Energy-2012 37

{ }( ) ( )

( ) ( )

( ) ( )

[ ] { }ee

i

i

oi

oinnel

i

ziixiiziixiiii

yiiziiyiiziiii

xiiyiixiiyiiii

ziizii

qBw

v

glgnglgnx

N

z

N

gngmgngmy

N

z

N

gmglgmglx

N

y

N

gngnz =

++−

∂∂

∂∂

++−∂

∂∂

++−∂

∂∂

−∂=∑

αβ

ε

1122

1122

1122

12

0

0

0

00

Page 38: Energy Harvesting Throurgh Piezoelectric Materials

Thermal field

nnelnnel

HNN ϕ∑∑ +Θ=Θ

�Temperature distribution is taken linear within the element.

�Using the shape function the temperature of any point in the element can beuniquely given in terms of nodal temperature and gradient of the mid plane as

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012 38

ii

iii

i HNN ϕ∑∑ +Θ=Θ

[ ] [ ] [ ] { } [ ] { }eeeei

innel

ii

i

innel

ii NNHNN θθ

ϕϕ φ+=

Θ

+

Θ

=Θ Θ∑∑ 00

are the mid plane temperature and gradient respectively at node iiΘ iϕ

Page 39: Energy Harvesting Throurgh Piezoelectric Materials

{ } { }kpek BE φφ−=

Electric field in the kth piezoelectric layer within the element can be given as

l

Finite Element Modeling (Cond..) Electric Field

Computational Intelligence Applications to Renewable Energy-2012 39

Where

{ }

=

3

3

31

n

m

l

tB

kpeφ

Thickness of kth piezoelectric layer

Electric potential of kth piezoelectric layer

kpt

kpφ

Page 40: Energy Harvesting Throurgh Piezoelectric Materials

{ } { }kpek BE φφ−=

Electric field in the kth piezoelectric layer within the element can be given as

l

Finite Element Modeling (Cond..) Electric Field

Computational Intelligence Applications to Renewable Energy-2012 40

Where

{ }

=

3

3

31

n

m

l

tB

kpeφ

Thickness of kth piezoelectric layer

Electric potential of kth piezoelectric layer

kpt

kpφ

Page 41: Energy Harvesting Throurgh Piezoelectric Materials

{ } { } dAdzV k

A k

σεΤ

∫ ∫∑=2

1

Using the variational principle the potential energy is given as

Substituting various values

Finite Element Modeling (Cond..)

Strain energy

Computational Intelligence Applications to Renewable Energy-2012 41

{ } [ ] { } { } [ ] { } { } [ ] { }[ ]eeueeeueeeuue kqkqqkqV θφ θφΤΤΤ −+=

2

1

Substituting various values

where[ ] [ ] [ ] [ ]∑∫

=

Τ=nl

k V

ekeeuu dVBQBk1

Page 42: Energy Harvesting Throurgh Piezoelectric Materials

[ ] [ ] [ ] { } [ ] [ ] { } [ ] [ ] { }

= ∫∫∫

ΤΤΤΤΤΤ

Vepe

Vepe

Vepeeu dVBeBdVBeBdVBeBk

npl φφφφ ...21

[ ] [ ] { } [ ] [ ] { } [ ]( )dVNBHNBknl

k Vekeekeeu ∑∫

=

ΤΘ

Τ +=1

ϕθ λλ

Finite Element Modeling (Cond..) Strain energy

Computational Intelligence Applications to Renewable Energy-2012 42

{ }

=

nplp

p

p

e

φ

φφ

φ

.

.

.2

1

Page 43: Energy Harvesting Throurgh Piezoelectric Materials

{ } { }dzdADEWA k

t

t

ee

k

k

∫∑ ∫−

Τ=1

2

1

The element electrical energy can be given as

Finite Element Modeling (Cond..)

Electrical energy

Computational Intelligence Applications to Renewable Energy-2012 43

{ } [ ] { } { } [ ] { } { } [ ] { }eeeeeeeeuee kkqkW θφφφφ φθφφφ

ΤΤΤ −+−=2

1

2

1

2

1

Substituting various values

where

[ ] [ ]Τ=eueu kk φφ

Page 44: Energy Harvesting Throurgh Piezoelectric Materials

[ ]

{ } [ ] { }{ } [ ] { }

∫Τ

Τ

Vepe

Vepe

dVBbB

dVBbB

φφ

φφ

.

2

1

Finite Element Modeling (Cond..)

Electrical energy

Computational Intelligence Applications to Renewable Energy-2012 44

[ ]

{ } [ ] { }

=

∫Τ

Vepe

e

dVBbB

k

npl φφ

φφ

.

.

.

Page 45: Energy Harvesting Throurgh Piezoelectric Materials

[ ]

{ } { } [ ] { } { } [ ]( ){ } { } [ ] { } { } [ ]( )

+

+

∫Τ

ΘΤ

ΤΘ

Τ

Vepeepe

Vepeepe

dVNpBHNpB

dVNpBHNpB

φφφ

φφφ

22

11

Finite Element Modeling (Cond.)

Electrical energy

Computational Intelligence Applications to Renewable Energy-2012 45

[ ]

{ } { } [ ] { } { } [ ]( )

+

=

∫Τ

ΘΤ

Vepeepe

V

e

dVNpBHNpB

k

nplnpl φφφ

φθ

.

.

.

Page 46: Energy Harvesting Throurgh Piezoelectric Materials

( )( )∑∫=

++=nl

k V

k dVwvuT1

222

2

1&&&ρ

Substituting various values

The element kinetic energy can be given as

Finite Element Modeling (Cond..)

Kinetic energy

Computational Intelligence Applications to Renewable Energy-2012 46

{ } [ ] { }eeuue qmqT..

2

1 Τ

=

where

density of kth layer

[ ] [ ] [ ]∑∫=

Τ=nl

k Veueukeuu dvNNm

1

ρ

[ ] [ ] [ ]∑∫=

Τ=nl

k Veueukeuu dvNNm

1

ρ

Page 47: Energy Harvesting Throurgh Piezoelectric Materials

[ ] ∑

−−−

=nnel

iiiiii

iiiii

eu

HNnHNnN

HNmHNmN

HNlHNlN

N 12

12

00

00

00

Finite Element Modeling (Cond..)

Kinetic energy

Computational Intelligence Applications to Renewable Energy-2012 47

−iiiiii HNnHNnN 1200

Page 48: Energy Harvesting Throurgh Piezoelectric Materials

Where

Work done by the external force and electrical charge is given as

{ } { } { } { }eqeeme

s FFqW ΤΤ += φ

{ } [ ] { } [ ] { }epueseuem fNdsfNF ΤΤ += ∫ { } { } { } dsfBF

eqeeq ∫Τ= φ

Finite Element Modeling (Cond..) Work done

Computational Intelligence Applications to Renewable Energy-2012 48

{ } [ ] { } [ ] { }epues

seuem fNdsfNF += ∫

1

{ } { } { } dsfBFeq

seeq ∫=

2

φ

{ }esf

{ }epf

{ }eqf

Element surface force intensity vector

Element point load vector

Element surface electrical charge density vector

Page 49: Energy Harvesting Throurgh Piezoelectric Materials

∫ =+f

o

t

t

s dtWL 0)(δ ( )∫ =++−f

o

t

t

se dtWWVT 0δδδδ

Using Hamilton’s principle

Finite Element Modeling (Cond..)

Computational Intelligence Applications to Renewable Energy-2012 49

The governing equation for an element can be written as

[ ] { } [ ] { } [ ] { } [ ] { } { }emeeueeueeuueeuu Fkkqkqm =−++ θφ θφ 2

1&&

[ ] { } [ ] { } [ ] { } { }eqeeeeeeu Fkkqk =+− θφ φθφφφ 2

1

Page 50: Energy Harvesting Throurgh Piezoelectric Materials

[ ]{ } [ ]{ } [ ]{ } { } [ ]{ } [ ]{ }kkFkqkqm φθφ −+=++ 1..

• Sensors and actuators are present

• vector can be partitioned

• No charge accumulates on the sensor layer

Finite Element Modeling (Cond..)

Governing equations

Computational Intelligence Applications to Renewable Energy-2012 50

[ ]{ } [ ]{ } [ ]{ } { } [ ]{ } [ ]{ }auussuuuuu askkFkqkqm φθφ φθφ −+=++

2

1..

[ ]{ } [ ]{ } [ ]{ } { }021 =+− θφ θφφφφ sss

kkqk su

[ ]{ } [ ]{ } [ ]{ } { }aaaa qau Fkkqk =+− θφ θφφφφ 2

1

Page 51: Energy Harvesting Throurgh Piezoelectric Materials

Sensor equation

{ } [ ] [ ]{ } [ ]{ }

+= − qkkk us sss φθφφφ θφ2

11

Finite Element Modeling (Cond..)

Governing equations

Computational Intelligence Applications to Renewable Energy-2012 51

Actuator equation

[ ]{ } [ ]{ } [ ] [ ][ ] [ ]( ){ } { } [ ]{ }θθφφφφ umuuuuuuuu kFqkkkkqcqmsss 2

11 +=+++ −&&&

[ ][ ] [ ]{ } [ ]{ }auu asskkkk φθ φφφφ φθ

−− −1

2

1

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Flowchart of Genetic Algorithm

Computational Intelligence Applications to Renewable Energy-2012

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Computational Intelligence Applications to Renewable Energy-2012

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Computational Intelligence Applications to Renewable Energy-2012

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Computational Intelligence Applications to Renewable Energy-2012

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Computational Intelligence Applications to Renewable Energy-2012

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Optimization of Piezoelectric Energy Harvester

Computational Intelligence Applications to Renewable Energy-2012

Maximize

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Optimization of Piezoelectric Energy Harvester(Cond.. )

Material Properties

Computational Intelligence Applications to Renewable Energy-2012

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Simulation parameters for the genetic algorithm optimization

Optimization of Piezoelectric Energy Harvester(Cond.. )

Computational Intelligence Applications to Renewable Energy-2012

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Results of optimization problem

Computational Intelligence Applications to Renewable Energy-2012

Harvest a power 75% higher

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Conclusion

�Fromthe overview, it is quite clear that piezoelectric energyharvesting has great potential at micro level and some veryimportant part of applications are still in the research anddevelopment stage.

Computational Intelligence Applications to Renewable Energy-2012

�The ability of piezoelectric equipment to convert motionfrom human body into electrical power is remarkable.

�It is a great hope that energy harvesting will rule the nextdecade in the technical field

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1. M. Raju, “Energy Harvesting, ULP meets energy harvesting: A game-changing combination for design engineers,” Texas Instrument White Paper, Nov. 2008

2. R.J.M. Vullers, V. Leonov, T. Sterken, A. Schmitz, “Energy Scavengers For Wireless Intelligent Microsystems,” Special Report in Microsystems & Nanosystems, OnBoard Technology, June 2006

3. Imec, “Design for analog and RF technologies and systems,” www.imec.be

References

Computational Intelligence Applications to Renewable Energy-2012

63

3. Imec, “Design for analog and RF technologies and systems,” www.imec.be4. Imec, “Micropower generation and storage,” www.imec.be5. F. Whetten, “Energy Harvesting Sensor Systems – A Proposed Application

for 802.15.4f, ” DOC: IEEE802.15-09/0074-00-004f6. C. Cossio, “Harvest energy using a piezoelectric buzzer,” EDN, pg.94-96,

March 20, 2008

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Thank you

Computational Intelligence Applications to Renewable Energy-2012 64


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