Upload
others
View
0
Download
0
Embed Size (px)
Citation preview
Pavia, 5Pavia, 5th th February 2013February 2013
MagnetoMagneto--mechanical Energy Harvester mechanical Energy Harvester for wireless sensors for wireless sensors
in Tyre, Shoes and other components in Tyre, Shoes and other components
ElvioElvio BonisoliBonisoli
DIGEPDIGEPDepartment of ManagementDepartment of Managementand Production Engineeringand Production Engineering
Pavia, 5Pavia, 5thth February 2013February 2013 22 / 37/ 37
Aim: supply energy to remote wirelesssensors inside tyre, shoes or other items
Presentation of the Cyber Tyre project Requirements, source characteristics
and boundary constraints Description of the harvesting device dynamics Optimization aspects Performance index and comparison Other applications: shoes and mouse devices Conclusions
OutlinesOutlines
Pavia, 5Pavia, 5thth February 2013February 2013 33 / 37/ 37
Cyber™Tyre
Cyber™Tyre
Cyber™Tyre
Processing UnitReceiver
Receiver User Applications
Control System
Cyber™Tyre
Cyber™Tyre
Cyber™Tyre
Processing Unit Receiver
Receiver
Vehicle Dynamics
Tyre like a sensor !
Pirelli Pirelli TyreTyre ProjectProject
Pavia, 5Pavia, 5thth February 2013February 2013 44 / 37/ 37
Cyber™Tyre
Cyber™Tyre
Cyber™Tyre
Processing UnitReceiver
Receiver User Applications
Control System
Cyber™Tyre
Cyber™Tyre
Cyber™Tyre
Processing Unit Receiver
Receiver
Vehicle Dynamics
Tyre like a sensor !
Supply energy to remote wirelesssensors inside tyre
Why ? No time‐life constraintBatteries are not enough …“Green” meaning !
How ? Use tyre kinematicCharacteristics ? Small and powerful
Reliable and robust !Efficient and adaptive
Pirelli Pirelli TyreTyre ProjectProject
Pavia, 5Pavia, 5thth February 2013February 2013 55 / 37/ 37
Cyber™Tyre
Cyber™Tyre
Cyber™Tyre
Processing UnitReceiver
Receiver User Applications
Control System
Cyber™Tyre
Cyber™Tyre
Cyber™Tyre
Processing Unit Receiver
Receiver
Vehicle Dynamics
Tyre like a sensor !
Sensors inside tyreWhy ? Pressure and Temperature monitoring
Forces, wearing measurementsGrip and aquaplaning estimations
How ? High technologyIntegration on vehicle dynamics
Pirelli Pirelli TyreTyre ProjectProject
Pavia, 5Pavia, 5thth February 2013February 2013 66 / 37/ 37
University
Industrial partners
Car maker
Cyber™Tyre
Cyber™Tyre
Cyber™Tyre
Processing UnitReceiver
Receiver User Applications
Control System
Cyber™Tyre
Cyber™Tyre
Cyber™Tyre
Processing Unit Receiver
Receiver
Vehicle Dynamics
Pirelli Pirelli TyreTyre ProjectProject
Pavia, 5Pavia, 5thth February 2013February 2013 77 / 37/ 37
Cyber Tyre: Temperature, pressure and
grip evaluation on each tyre Integrated vehicle dynamic
control (ABS, ESC, …)
Inside each node Sensors (temperature, pressure, 3‐axial accelerometer) Electronic circuits UWB Antenna + PAN on vehicle Energy Harvester (mechanical‐electro‐magnetic) Harvester Interface (active control)
Pirelli Pirelli TyreTyre ProjectProject
Pavia, 5Pavia, 5thth February 2013February 2013 88 / 37/ 37
Very long historical connection to the water wheel, windmills and waste heat
Ironically, before batteries (Volta, 1799) and the dynamo (Faraday, 1831), energy scavenging or energy harvesting was the only way to get any useful power !
Today, great interest in the community for powering ubiquitously deployed sensor networks and mobile electronics
All depends on the application (source characteristics, power density requirements, …) – No one-size-fits-all !
Energy Harvester historyEnergy Harvester history
Pavia, 5Pavia, 5thth February 2013February 2013 99 / 37/ 37
Mean power and power density, obtained through scavengers, are usually small (µW, mW, W)
Source of power, excitation characteristics and harvesting technologies
Energy scavenging principles:• Radio waves• Vibrations• Electromagnetic• Electrostatics• Piezoelectric• Thermoelectric• Photovoltaic• Biological
ElectromagneticElectromagneticVibrationsVibrationsElectrostaticsElectrostaticsThermoelectricThermoelectric
PhotovoltaicPhotovoltaicPiezoelectricPiezoelectric
Examples ???Examples ???
Energy Harvester typologyEnergy Harvester typology
Pavia, 5Pavia, 5thth February 2013February 2013 1010 / 37/ 37
ShakeShake‐‐drivendrivenflashlightflashlight
Mechanical watchesMechanical watches
Piezoelectric pavementPiezoelectric pavement
ElectrostaticElectrostaticgeneratorgeneratorin shoesin shoes
Solar panelsSolar panels
PZT generators in shoesPZT generators in shoes
Some examples Some examples ……
Pavia, 5Pavia, 5thth February 2013February 2013 1111 / 37/ 37
depend heavily on the ambient excitation and harvesting technologies
Power densityPower density
Pavia, 5Pavia, 5thth February 2013February 2013 1212 / 37/ 37
Characteristics and operational requirements: Small dimensions: about 10x10x10 mm High power density Wake‐up velocity 20‐30 km/h Velocity range up to 300 km/h Voltage > 1.3 V Lowest mean power 2.5 mW
Nonlinear electro‐magnetic device
(resonant and adaptive)
Pirelli Pirelli TyreTyre HarvesterHarvester
Pavia, 5Pavia, 5thth February 2013February 2013 1313 / 37/ 37
Characteristics and operational requirements: Small dimensions: about 10x10x10 mm High power density Wake‐up velocity 20‐30 km/h Velocity range up to 300 km/h Voltage > 1.3 V Lowest mean power 2.5 mW
Nonlinear electro‐magnetic device
(resonant and adaptive)
Conicalrubberwedge
Peekcube
EnergyScavenger
Pirelli Pirelli TyreTyre HarvesterHarvester
Pavia, 5Pavia, 5thth February 2013February 2013 1414 / 37/ 37
How it works: A floating magnet slides
into the guide Around the guide are
wound two coils Preload obtained through
magnets Use of rare‐earth permanent
magnets (high power density) Electric‐magnetic coupling
with imposed dynamic
Pirelli Pirelli TyreTyre HarvesterHarvester
Pavia, 5Pavia, 5thth February 2013February 2013 1515 / 37/ 37
Time [s]
Z-RADIAL
Source characteristics: Variations in the radial acceleration impose dynamic
to a floating permanent magnet One‐wheel‐turn gives one pulse of energy
Radial acceleration [m
/s2 ]
Pirelli Pirelli TyreTyre HarvesterHarvester
Pavia, 5Pavia, 5thth February 2013February 2013 1616 / 37/ 37
0 50 100 150 200 250 300 350-6
-4
-2
0
2
4
6
8
10
12
14
Vel [km/h]
Energy Scavenger Simulator
Voltage min [V]Voltage max [V]Voltage rms [mV]Power [mW]
Friction [N]Velox z [m/s]
Time [s]Accel x_input [m/s²]Accel y_input [m/s²]Accel z_input [m/s²]Disp z_shaker [mm]Velox z_shaker [m/s]Accel z_shaker [m/s²]
Disp x [mm]Disp y [mm]Disp z [mm]Velox z [m/s]Accel z [m/s²] Force K [N]
Force C [N]
Force K [N]Force C [N]Current [A]Voltage [V]Voltage Core1 [V]Voltage Core2 [V]Power av [mW]
Magnet_3dof
Input
0
0
0.08359
0.0114
-0.0029958
-1.9771972
0
0
2.442971
-0.039
-0.051
-0.173
1.038
0.011
0.00151
38.931
41.411
833.131
0
0
0
-0.019
1.038
CoilMagnet
Bumpstop
Box
Rif. Inerziale
Shaker
GeometricalGeometricalparametersparameters
0.08 0.09 0.1 0.11 0.12 0.13 0.14-6
-5
-4
-3
-2
-1
0
1
2
3
4
Time [s]
Vol
tage
[V]
Test Rubber 1Test Rubber 2Test No RubberES simulator
EstimatedEstimatedperformanceperformance
ComparisonComparison
FEMFEM
CADCAD
SIMSIM
PrototypePrototypeand testsand tests
Simulated performanceSimulated performance
Pavia, 5Pavia, 5thth February 2013February 2013 1717 / 37/ 37
Friction [N]Velox z [m/s]
Time [s]Accel x_input [m/s²]Accel y_input [m/s²]Accel z_input [m/s²]Disp z_shaker [mm]Velox z_shaker [m/s]Accel z_shaker [m/s²]
Disp x [mm]Disp y [mm]Disp z [mm]Velox z [m/s]Accel z [m/s²] Force K [N]
Force C [N]
Force K [N]Force C [N]Current [A]Voltage [V]Voltage Core1 [V]Voltage Core2 [V]Power av [mW]
Magnet_3dof
Input
0
0
0.08359
0.0114
-0.0029958
-1.9771972
0
0
2.442971
-0.039
-0.051
-0.173
1.038
0.011
0.00151
38.931
41.411
833.131
0
0
0
-0.019
1.038
CoilMagnet
Bumpstop
Box
Rif. Inerziale
Shaker
Floatingmagnet
Iron-coilcoupling
Elasticand/or
dissipativeterms
Tyrekinematic
State
Forces
Simulated performanceSimulated performance
Pavia, 5Pavia, 5thth February 2013February 2013 1818 / 37/ 37
CxFxm CyFym
emairbumpfricCz FFFFFzm
Dynamic equilibrium of the floating magnet:
xy
z
xgmF Cx
~ygmF C
y~zgmF C
z~
from Pirelli exp.
0if
if00if
limlim
limlim
limlim
bump
bumpbump
Fzzzczzk
zzzFzzzczzkF
limlimlimlim
limlim
if0,ifsign
yyyxxxyyxxzzfF fric
Rubbery bumpers
Adhesion and friction
Modelling aspectsModelling aspects
Pavia, 5Pavia, 5thth February 2013February 2013 1919 / 37/ 37
xy
z Pneumatic effects
airvis
airp
air FFF
12
2
4ppdF air
p
inpr
inpvisair
vis zzc
hdzzF
inpairairairp
airairairp
zzF
zzkF
Modelling aspectsModelling aspects
Pavia, 5Pavia, 5thth February 2013February 2013 2020 / 37/ 37
xcFxm ,
ycFym ,
bumpfricemzc FFFFzm ,
Dynamic equilibrium of the floating magnet:
xy
z
Electro-magnetic coupling from FEM
cemkemem FFF ,, zFF kemkem ,,
idzdN
zi
dtdN
zieF cem
,
ediC
iRRdtdiL
t
ll 0
1
Modelling aspectsModelling aspects
Pavia, 5Pavia, 5thth February 2013February 2013 2121 / 37/ 37
xcFxm ,
ycFym ,
bumpfricemzc FFFFzm ,
Dynamic equilibrium of the floating magnet:
xy
z
Electro-magnetic coupling from FEM
cemkemem FFF ,, zFF kemkem ,,
idzdN
zi
dtdN
zieF cem
,
ediC
iRRdtdiL
t
ll 0
1 -3 -2 -1 0 1 2 30
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Displacement [mm]
Forc
e [N
]
v = 7.4 % , m = 7.2 %
v = 7.4 % , m = 3.2 %
v = 5.3 % , m = 14.1 %
v = 5.3 % , m = 6.3 %
Modelling aspectsModelling aspects
Pavia, 5Pavia, 5thth February 2013February 2013 2222 / 37/ 37
xcFxm ,
ycFym ,
bumpfricemzc FFFFzm ,
Dynamic equilibrium of the floating magnet:
xy
z
Electro-magnetic coupling from FEM
cemkemem FFF ,, zFF kemkem ,,
idzdN
zi
dtdN
zieF cem
,
ediC
iRRdtdiL
t
ll 0
1 -3 -2 -1 0 1 2 30
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Displacement [mm]
Forc
e [N
]
v = 7.4 % , m = 7.2 %
v = 7.4 % , m = 3.2 %
v = 5.3 % , m = 14.1 %
v = 5.3 % , m = 6.3 %
-3 -2 -1 0 1 2 30
50
100
150
200
250
Displacement [mm]
Freq
uenc
y [H
z]
v = 7.4 % , m = 7.2 %
v = 7.4 % , m = 3.2 %
v = 5.3 % , m = 14.1 %
v = 5.3 % , m = 6.3 %
Modelling aspectsModelling aspects
Pavia, 5Pavia, 5thth February 2013February 2013 2323 / 37/ 37
xy
z
30 km/h30 km/h60 km/h60 km/h
100 km/h100 km/h
Comparison of dynamicsComparison of dynamics
Pavia, 5Pavia, 5thth February 2013February 2013 2424 / 37/ 37
Velocity of 20 km/h not working … Magneto‐elastic preload confines magnet displacement
to no dynamic Electric‐magnetic coupling is close to be null
0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1-4
-2
0
2
4
Dis
p z-
z inpu
t [mm
]
Energy Scavenger Simulator
0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1-0.4
-0.2
0
0.2
0.4
Vel
zd-
zdin
put [m
/s]
Time [s]0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1
-20
0
20
40
60
80
100
120
140
160
180
200
Acc
el z
ddin
put [m
/s2 ]
Time [s]
Energy Scavenger Simulator
InputShaker
Simulated performanceSimulated performance
Pavia, 5Pavia, 5thth February 2013February 2013 2525 / 37/ 37
0.9 0.92 0.94 0.96 0.98 1 1.02 1.04 1.06 1.08 1.1-100
0
100
200
300
400
500
600
700
800
900
Acc
el z
ddin
put [m
/s2 ]
Time [s]
Energy Scavenger Simulator
InputShaker
Velocity of 40 km/h full operative condition Wide amplitude resonant motion Electric‐magnetic coupling is equivalent to a viscous
damping effect
0.9 0.92 0.94 0.96 0.98 1 1.02 1.04 1.06 1.08 1.1-4
-2
0
2
4
Dis
p z-
z inpu
t [mm
]
Energy Scavenger Simulator
0.9 0.92 0.94 0.96 0.98 1 1.02 1.04 1.06 1.08 1.1-1.5
-1
-0.5
0
0.5
1
Vel
zd-
zdin
put [m
/s]
Time [s]
Simulated performanceSimulated performance
Pavia, 5Pavia, 5thth February 2013February 2013 2626 / 37/ 37
Velocity of 80 km/h full operative condition Magnet presents an impulsive dynamic Electric‐magnetic coupling maintains a good power flow At higher speed the power reduces because the moving mass
does not have the time to reach the upper bumper
0.55 0.56 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 0.65-500
0
500
1000
1500
2000
2500
3000
3500
Acc
el z
ddin
put [m
/s2 ]
Time [s]
Energy Scavenger Simulator
InputShaker
0.55 0.56 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 0.65-4
-2
0
2
4
Dis
p z-
z inpu
t [mm
]
Energy Scavenger Simulator
0.55 0.56 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 0.65-6
-4
-2
0
2
Vel
zd-
zdin
put [m
/s]
Time [s]
Simulated performanceSimulated performance
Pavia, 5Pavia, 5thth February 2013February 2013 2727 / 37/ 37
Device Device behaviourbehaviour
Pavia, 5Pavia, 5thth February 2013February 2013 2828 / 37/ 37
PrototypesPrototypes
Pavia, 5Pavia, 5thth February 2013February 2013 2929 / 37/ 37
PrototypesPrototypes
Pavia, 5Pavia, 5thth February 2013February 2013 3030 / 37/ 37
PrototypesPrototypes
Pavia, 5Pavia, 5thth February 2013February 2013 3131 / 37/ 37
0.15 0.16 0.17 0.18 0.19 0.2-1.5
-1
-0.5
0
0.5
1
1.5
2
Time [s]
Vol
tage
[V]
ExperimentalSimulated
Test on shaker:• Resonance detection and nonlinearity weight• Tests with sinusoidal and road profiles
• Test with adapted load Rl = 2 x 210 :
• road profile accelerationswithout radialcomponent 60 km/h
Experimental comparisonExperimental comparison
Pavia, 5Pavia, 5thth February 2013February 2013 3232 / 37/ 37
0.15 0.16 0.17 0.18 0.19 0.2-1.5
-1
-0.5
0
0.5
1
1.5
2
Time [s]
Vol
tage
[V]
ExperimentalSimulated
0 0.05 0.1 0.15 0.2 0.25 0.3 0.350
0.1
0.2
0.3
0.4
0.5
Time [s]
Pow
er [m
W]
ExperimentalSimulated
Test on shaker:• Resonance detection and nonlinearity weight• Tests with sinusoidal and road profiles
• Test with adapted load Rl = 2 x 210 :
• road profile accelerationswithout radialcomponent 60 km/h
Experimental comparisonExperimental comparison
Pavia, 5Pavia, 5thth February 2013February 2013 3333 / 37/ 37
mm75.00 Y mm00.10 Y
Experimental comparisonExperimental comparison
Pavia, 5Pavia, 5thth February 2013February 2013 3434 / 37/ 37
mm75.00 Y mm00.10 Y
mm15.10 Y mm25.10 Y
Experimental comparisonExperimental comparison
Pavia, 5Pavia, 5thth February 2013February 2013 3535 / 37/ 37
mm75.00 Y mm00.10 Y
mm15.10 Y mm25.10 Y
mm35.10 Y mm50.10 Y
Experimental comparisonExperimental comparison
Pavia, 5Pavia, 5thth February 2013February 2013 3636 / 37/ 37
Goals: Verifying that the topology is optimal Improving the performance at low speed Optimization of components “weights”
Device optimizationDevice optimization
Pavia, 5Pavia, 5thth February 2013February 2013 3737 / 37/ 37
Multiple coils configurations Two (or more) spacers
between coils One or more floating
magnets
Flux
linkage [W
b]Displacement [mm]
Coils optimizationCoils optimization
Pavia, 5Pavia, 5thth February 2013February 2013 3838 / 37/ 37
Full parametric model Volume constraints Gradient or genetic
algorithms
0 20 40 60 80 100 120 140 1600
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Uni
tary
erro
r [-]
Iteration
Mean po
wer [m
W]
Global optimizationGlobal optimization
Pavia, 5Pavia, 5thth February 2013February 2013 3939 / 37/ 37
Maximum available energy and power:
The system is more effective at low vehicle speed
samLE rext 2max
2
3
max 2rvsmsamfP r
Device efficiencyDevice efficiency
Pavia, 5Pavia, 5thth February 2013February 2013 4040 / 37/ 37
Work in progress Work in progress ……
Pavia, 5Pavia, 5thth February 2013February 2013 4141 / 37/ 37
Work in progress Work in progress ……
Pavia, 5Pavia, 5thth February 2013February 2013 4242 / 37/ 37
Friction [N]Velox z [m/s]
Time [s]Accel x_input [m/s²]Accel y_input [m/s²]Accel z_input [m/s²]Disp z_shaker [mm]Velox z_shaker [m/s]Accel z_shaker [m/s²]
Disp x [mm]Disp y [mm]Disp z [mm]Velox z [m/s]Accel z [m/s²] Force K [N]
Force C [N]
Force K [N]Force C [N]Current [A]Voltage [V]Voltage Core1 [V]Voltage Core2 [V]Power av [mW]
Magnet_3dof
Input
0
0
0.08359
0.0114
-0.0029958
-1.9771972
0
0
2.442971
-0.039
-0.051
-0.173
1.038
0.011
0.00151
38.931
41.411
833.131
0
0
0
-0.019
1.038
CoilMagnet
Bumpstop
Box
Rif. Inerziale
Shaker
Floatingmagnet
Iron-coilcoupling
Elasticand/or
dissipativeterms
Tyrekinematic
State
Forces
Simulated performanceSimulated performance
Pavia, 5Pavia, 5thth February 2013February 2013 4343 / 37/ 37
Time histories and frequency contents
From From tyretyre to to …… shoesshoes
0 10 20 30 40 50 60 70-250
-200
-150
-100
-50
0
50
100
150
200
250
Time [s]
Acc
eler
atio
n [m
/s2 ]
xddyddzdd
Pavia, 5Pavia, 5thth February 2013February 2013 4444 / 37/ 37
Optimization with conical springs
From From tyretyre to to …… shoesshoes
Pavia, 5Pavia, 5thth February 2013February 2013 4545 / 37/ 37
Prototypes
From From tyretyre to to …… shoesshoes
Pavia, 5Pavia, 5thth February 2013February 2013 4646 / 37/ 37
ResumeResume
Energy Harvester for power supplier of wireless sensors
Power density and suitability of sources Pirelli Cyber Tyre project Magneto-mechanical Energy Harvester Nonlinear dynamics, optimization and results From tyre to shoes and other device applications
Acknowledgements / People involved
Prof. Stefano TORNINCASA Ing. Elvio BONISOLI ([email protected]) Ing. Marco BRINO ([email protected]) Ing. Francesco DI MONACO Ing. Gabriele MARCUCCIO