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8/6/2019 0 - Imperial College Primer
http://slidepdf.com/reader/full/0-imperial-college-primer 1/22
Energy Harvesting Technologiesfor Wireless Sensors
Andrew S Holmes
p ca an em con uc or ev ces roup
Department of Electrical and Electronic Engineering
Imperial College London
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Wireless Sensor Applications
Wireless sensors very well established in certain market sectors e.g. domestic
fuel monitoring
Huge opportunity for expansion in other areas such as:
• Machine/process monitoring
• Remote monitoring
- inaccessible/hostile environments
• Intelligent buildings
- , ,
• Medical telemetry
- continuous, unobtrusive monitoring
• Ubiquitous computing
- ad hoc sensor networks
•
- ‘smart dust’ concept 1 cc wireless sensor node [IMEC]
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Power Sources for Wireless Sensors
Short term solutions inevitably based on chemical batteries
• High energy density (~2000 J/cm3 or ~500 mA.hr/cm3 at 1V)
• Limited life before recharging or replacement
• Disposal/recycling problematic
Fuel-burning power sources
• Very high energy density
• Technolo ies still some wa from maturit
• Limited life before refuelling, as for batteriesMeOH fuel cell
[Fraunhofer Inst.]MEMS gas turbine stage
[MIT]
nergy arves ng
• Long term storage capacity no longer an issue
• Low power density in most casese.g. cm or so ar ce n o ce env ronmen
• Intermittent supply in many cases so likely to
be used with battery/capacitor back-upPico Radio solar cell
Vibration-driven generator
1 mW @ 0.25g rms
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Energy Harvesting Technologies
Energy Source Conversion Mechanism
Electromagnetic radiation
Ambient light Photovoltaic cell
Heat
Temperature gradients Thermoelectric device or
ea eng ne
Kinetic energy
Movement and vibration Electrostatic
Volume flow (of liquids or gases) Magnetic (induction)
Piezoelectric
Technology of choice will depend strongly on application environment,
average power and duty cycle requirements
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Motion-driven Microgenerators
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Inertial Energy Harvesters
• Single point of attachment to moving “host” e.g. machine, person…
• Peak inertial force on proof mass: F = ma = m2Ywhere a is the peak acceleration applied by the host
• Damper force < F or no internal movement ax mum wor per rans : zo = m ozo
Maximum harvested power: P = 2W/T m3Yozo/
zo
m o
damper implements energy conversion
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How Much Power is Available?
Plot assumes:
• cubic device with mass occupying half of
1000
10000
,
• const. source acceleration amplitude (2Y0)
of 10 m/s2 (equiv to Y0
= 25 cm at 1 Hz)
• roof mass with densit 20 /cc
10
100
o w e r ( m W )
f = 1 Hz
f = 10 Hz sensor node *
0.01
0.1 watchcellphone
0.001
0.01 0.1 1 10 100 1000
volume (cc)
* For the sensor node, we are assuming a simple physical sensor (e.g. temp, pressure or motion)
with short-range (e.g. within room) wireless link and low data-rate
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Comparison of Architectures
= excitation frequency
Normalised axes:
resonan requency
(resonant devices)
Z l /Y 0 = mass travel range
excitation amplitude
Power = P (Watts)
m 3 Y 0 2
c
Z l /Y 0
• Resonant devices better for large generators / small displacements, operated
near resonance• Non-resonant good for large displacements, wide input frequency ranges
Mitcheson P.D., Green T.C., Yeatman E.M., Holmes A.S., “Architectures for vibration-driven
micropower generators”, IEEE/ASME J. Microelectromechanical Systems 13(3), (2004), 429-440.
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Machine Powered Applications
• Resonant vibration-driven generators aimed at machine/process monitoring
are the most highly developed
• Synchronous electrical machines have predictable vibration frequency,
making them ideal for resonant energy harvesters
• , . .
PMG17PMG17 from Perpetuum Ltd
Resonant generator tuned to 2nd harmonic of mains frequency – 100 or 120 Hz
4.5 mW output power (rectified DC) at 0.1g
acceleration
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Human Powered Applications
Excitations are slow, large in amplitude and
irregular compared to those generally
• Non-resonant device can win at small generator sizes• Data obtained in collaboration with ETH Zurich (T. von Buren)
von Büren T., Mitcheson P.D., Green T.C., Yeatman E.M., Holmes A.S., Tröster G., “Optimization of
inertial micropower generators for human walking motion”, IEEE Sensors Journal, 6(1), (2006), 28-38.
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Non-resonant Device developed at Imperial
Discharge contact
Model: MEMS parallel plate capacitor implementation:
on op p a e
Moving capacitor plate / mass
plate on baseplate
Pre-chargingcontact
Generation cycle:
• Capacitor is pre-charged when mass is at bottom (max capacitance)
• Under sufficiently large (downward) frame acceleration, capacitor plates separate
at constant charge , and work is done against electrostatic force stored
electrostatic energy and plate voltage increase
• Charge is transferred (at higher voltage) to external circuit when moving plate
reaches position of max displacement
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Energy Yield per Cycle
Separation
Input phase Output phase
input input V C Q output output
V C Q
input
input
ouput V C
C V
Generated ener :
222 1
)(
111
output output input output output output input input ouput output V C V V V C V C V C E
input output V V
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Measured Performance
shaker generator
• o age pro e as npu mpe ance
>1012 and dynamically measures
voltage on capacitor
voltage probe
• Net power in this experiment: 2.2 μW
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Motion-driven Harvesters – are they any good?
1.6%
1.8%
EM
ES
Volume Figure of Merit defined
as:
1.2%
1.4%PZ
FoM V = Au Vol 4/3 Y 0 3 1
16
0.6%
0.8%
1%
F o M V
power to that of idealised
generators on slide 7
0.2%
0.4%
achieved only about 2%
Better devices have emerged
2000 2002 2004 2006 20080
Publication Year
,
to go...
Main issues are: 1 dam in /transduction – need to im lement stron er dam ers 2 ower
Mitcheson P.D., Yeatman E.M., Kondala Rao G., Holmes A.S., Green T.C., “Energy harvesting from human
and machine motion for wireless electronic devices”, Proc. IEEE 96 9 , 2008 , 1457-1486.
conversion electronics – difficult to make efficient; (3) adaptive operation
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Flow-driven Microgenerators
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Energy Scavenging from Air Flow
Basic concept:
wind turbines on a
smaller scale
(cm-scale or smaller)
x rac ne c energy
from air flow
K.E. per unit vol in flow = ½V2
K.E. per sec crossing swept area is:
= 2 = 3 100
1000
10000100000
( m W )
CP = 0.1
Betz limit (CP = 0.59)
For 1 cm-dia disc:
ava
Actual output power is:
P = ½AV3CP 0.01
0.1
1
10
O
u t p u t p o w e r
Land vehicle
Flight vehicle
where CP = power coefficient 0.0001
0.001
0.1 1 10 100 1000
Flow speed (m/sec)
HVAC duct
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2-cm dia. Device developed at Imperial
• Ducted turbine with integrated axial-flux
permanent magnet generator
• mW output power levels
• Starts at low flow speeds (~3 m/s)
• Applications in HVAC duct sensing and
gas pipeline monitoring
5
6
7
e r ( m W )
3
4
r o
u t p u t p o w
speed
10.0 m/s
0
1
0 1000 2000 3000 4000 5000 6000
G e n e r a t
7.0 m/s
8.0 m/s
9.0 m/s
6.0 m/s
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Rotation spe ed (RPM)
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Comparison with other Flow-driven Harvesters
• Small flow-driven devices are expected to perform relatively poorly because
of high viscous losses
• Small turbines also suffer from relatively large clearances and bearing
losses
10000
100
1000
/ c m ^ 2 )
Betz lim itCp = 0.1Federspiel (2003), A = 81 sq.cmRancourt (2007), A = 13.9 sq.cmMyers (2007), A ~ 317 sq.cmHolmes (2009), A = 3.14 sq.cm
m-sca e pro o ype ev ces o
date have struggled to reach
Cp ~ 0.1
0.1
1
d e n s i t y
( mever e ess, use u m
power levels can begenerated because available
ower in flow is si nificant
0.0001
0.001
0.01
P o w e
even at modest flow speeds
Duct sensing applications
look uite viable even with0.1 1 10 100
Flow speed [m/sec]current devices
Bansal A., Howey D.A., Holmes A.S., “Cm-scale air turbine and generator for energy scavenging from
”
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HVAC Duct Sensor Concept
“Spider” mounted
ns e uc
Sensor arrayDistributed network of wireless sensors
with peer-to-peer communication to relay
Generator /
Transceiver
Monitoring of:
• Air flow and tem for HVAC control
• Air-quality e.g. RH; CO2, Ammonia, VOCs
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ummary
o on- r ven energy arves ers are s per orm ng a a eve some
way below what is theoretically achievable
Current performance is adequate for some important applications such
as machine monitoring, and commercial solutions are available
Improvements in performance will be required before harvesting power
Flow-driven devices at cm-scale also have relatively low conversion
efficiencies, but the available power in the flow is such that duct sensing
applications appear viable
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Acknowledgements
Motion-driven Generators:
r c ea man
Paul Mitcheson
Tim GreenPeng Miao (now with Oxford Instruments)
Bernard Stark (now with University of Bristol)
Flow-driven Generators:Keith Pullen (now with City University, London)
Anshu Bansal
David Howey
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Contact
Andrew S Holmes
Professor of Micro Electro Mechanical Systems
Optical and Semiconductor Devices GroupDepartment of Electrical and Electronic Engineering
Imperial College London
Exhibition Road London SW7 2BT UK
Tel: +44 (0)20 7594 6239
Email : [email protected]
Web: http://www3.imperial.ac.uk/opticalandsemidev
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