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8/12/2019 EES12 - Energy Harvesting
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1
Energy Harvesting Devices
Davi de Brunelli
Depart ment of Informat ion Engineering and Comput er Science
DISI Univer sit y of Trent o
The nightmare of pervasive embedded computing:
Power avalaibility
Ubiquitous computings dream of pervasive sensors and electronics
ever where is accom anied b the ni htmare of batter re lacementand disposal.
No Moores Law in batteries:
2-3%/year growth
Battery Technology is Stuck!
ES lifetime depends
on battery life!!
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Limits to Battery Energy Density
Processing power doubles every 2 years, but
Battery capacity doubles every 10 years
We need a more efficient wa to enable lon er life
Energy Density by Mass (MJ/kg)
2004 - Lithium ion at its
current max
1991 - Lithium Ion battery
released
1899 - NiCd battery created
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
TNT
2012 - Nanowire-based
lithium ion batteryResearch in progress
[TI09]
Available Energy is All Around
Technology trend:
Design systems that harvest limited energy from ambient (heat, light, radio,
or vibrations) or scavenge power from human activity
waves
o on an
vibrationea
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Energy Harvesting Basics
Energy harvest i ngis the process by which
energy is capturedand stored
Energy Harvesting shrinks or replaces batteries or
extents recharge periods
This term often refers to small
autonomous devices micro energy
harvesting
Power output of Energy Harvesting transducers islinked to their size (area, volume) and thus to their
price
Power addresses matching of loads and of
transducers and aim at the maximum energy output
Setting expectations
[Van Hoof HOLST10]
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The Good News
WSN
Mobile terminalsBatteries
Todays
scavenger-aware
design
The gap between scavengers energy and requirements of digital
systems is shrinking [Paradiso05]
scavenger evolutionWSN evolution
Todays W SNs
Exploit energy management strategies and improvements in scavengertechnology
Overcome traditional energy management strategies (battery-driven)
An new unified design methodology is required
Smart adaptation
Design for unreliability
Exploit unpredictable power sources
20W 20W 40W 20W Avg.Power
80 Mops 2nJ/b
Sensor Node Evolution
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Where we are now
1WHarvesters ConsumersAverage Power
Cell phone
Zi bee mesh network node
Energy Harvesting Power Generation & Utilization
100 mW
10 mW
1 mWLarge inductive vibe harvesters
1 in2 TEG on crease beam
TEG stringer clip
1 cm2 a-Si PVin sun lit airplane pax window
Wireless dimming window
AAA LED flash light
Wireless sensor @ 1 HzPush button harvester
(w/ Rx from wireless sensor)
TI MSP430 microprocessor (awake)
Chipcon CC2500 radio (Tx mode)
6 mm2 TEG on hydraulic line
100 W
10 W
1 W
Small piezo beam vibe harvesters
1 cm2 a-Si PV in cabin lightingcm a- n ue s y Push button transmitter
Sensor @ 2.8 hrs interval
GSE monitoring sensor(log data every 10sec, Tx 2X per day)
TI MSP430 microprocessor (asleep)Chipcon CC2500 radio (asleep)
TEG=thermoelectric generator
Energy Harvesting: new design methodology
Hardware Design Conversion efficiency
Natural progression of Energy
Optimization Techniques
mpe ance a c n
Maximum power transferred
Software Design Scheduling algorithm
Low Power Design
Power Aware Design
Battery Aware Design
Energy prediction algorithm
Energy Harvesting
Aware Design
Why is it different?
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Energy Source Characteristics Efficiency Harvested Power
Light Outdoor
Indoor 10~24%
100 mW/cm2
100 W/cm2
~ 2
Energy Harvesting Sources
ThermalIndustrial
.
~3%
~1-10 mW/cm2
Vibration
~Hzhuman
~kHzmachines
25~50%
~4*W/cm3
~800 W/cm3
RF GSM 900 MHz
WiFi ~50%
0.1 W/cm2
0.001 W/cm2
1uW 10uW 100uW 1mW 10mW
Seiko watch
~5uW
2 channel EEG
~1mW
100mW 1W+
AdaptivEnergy
~10mW
~30mm
Holst Center
~40uW
BigBelly
~40W
Elastometer
~800mW
Energy from Human daily activity
Thad Starner, Human-Powered Wearable Computing, IBM Systems
Journal 35, pp. 618-629 (1996).
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Effective, long term, power supplies are limited and/or expensive
Example: At an average powerconsumption of 100 mW, you need morethan 1 cm3 of lithium battery volume for 1ear of o eration.
Environmentally powered wireless sensors
AirflowGoal
Investigate energy harvesting andmanagement technologies that can
support the operation of a smartsensor node indefinitely
Inductive
KineticRF
Contacts: Telecom Italia, STM
EH powered nodesphilosophy
Input
ADCCPU
Wireless
EH-management
Independent
Load
Interface
Energy Harveter and
Sensors
pro ec on
Switch
Supercapacitor Battery
Switch
Ref2Ref1
Supercapacitor Battery
xe rc ec uremar ower n
General purposeOptimized from Ambient Source and storage,
but not for a specific application
Plug-&-play
Analog or with Digital Interface for external
power management (standardization?)
Usually more efficient
Tailored on a specific application
HW /SW dependent
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Design Methodology
Gener ic Appr oach Dedicated blocks, depending on energy source, ambient
conditions and application
Rectifier, DC-DC converter and MPPT are the most challenging
and require a very accurate design process
Charger/limiter/protection consumes additional power and are
often to some extent redundant.
AmbientEnergy
Energy
Trans-ducer
RectifierMPPTDC/DC
Charger/Protection
Storage
DC/DC Load
Ambient EnergyNon-monotone, Unpredictable
Ex: solar power (PV-cells)
Ex: power waveform from
human walk (piezo-scavengers)Too much
Too little
A eriodic
18[Paradiso05]
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Challenges for Harvested power
management
Changing polarity input
Low input voltage (e.g some mVs)
AC input with variable frequencies
Several AC inputs
Sources with variable resistance (depending on
temperature and aging) High dynamic range of input voltage
Rectifier
Energy is usually available with dual polarity voltage
Design choices:
Simple Diode Bridge (Vdrop ~1,2V)
Active mosfet Bridge (Vdrop ~0,4V)
(needs Input Polarity Detector)
Dual circuit topology
(No Vdrop, at the cost of size and complexity)
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Rectifier
Active Mosfet Bridge
Diodes can be short-circuited by switches to
revent de radin efficienc from the
forward voltage drop
Typical values:
Start-up 150mV,
drop 40 mV, later on 5 mV
Diodes are only active during start-up
w ere s no supp y vo age or ecomparator
Maximum Power Point Tracking
Maximum power from source to load when internal resistances are
matched
Input resistance of a DC-DC converter is influenced with its duty
cycle
Ideal situation: Load RL and Internal resistance
Ri are naturally matched
Vsupply in the correct range
Typical situation: DC/DC with MPPT to match Rl and Ri
and /or to adjust Vsupply
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Maximum Power Point Tracking
- an example-
120
140
I-V chart
0
20
40
60
80
100
0 0,5 1 1,5 2 2,5 3 3,5 4 4,5
V[Volt]
I[mA
I[uA]
V [Volt]300
P-V chart1500
2000
January 25, 2007 Ing. Davide Brunelli 230
50
100
150
200
250
0 0,5 1 1,5 2 2,5 3 3,5 4 4,5
V[Volt]
P[u
P[mW]
V [Volt]
0
500
1000
0 150 300 450 600 750 900
T (s)
Vc
(m
V)
10.9J 15.7J
VsolarVctrl
Vsolar
Vlow crossing switch off
Vhigh crossing switch on
MPP Regulator
P
Vtransducer
Controlled variableVlow, Vhigh duty cycle
Vlow
Vhigh
Online control for tracking Transducer curve variations
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MPPT Techniques
MPPT Techniques depend mainly on the transducer and the ambient
energy
Most common techniques of MPPT employ DSPs or microcontrollers, not
Simpler solutions employing only analog circuits sometime have smaller
performanceFor e photovoltaic cells with Fractional Open Circuit Voltage: Photovoltaic panels
output voltage that allow to drain the maximum amount of power correspond atabout the 70 % of the open circuit voltage.
KOFCV = VOC/Vmpp ~ 0,71-0,75
MPPT Techniques
Energy storage required
Intelligent, adaptive power management ensures maximum power
Switching frequency is fixed and depends
on circuit parameters and components.
Maximum Power Point Tracker duty cycle
Climb the Hill !!
is controlled and output power
measured
Increasing output power: duty cycle is
changed further in the same direction andvice versa
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Buck: Vo = Vi
Buck-boost: Vo -Vi
Steady State Transfer Function - Buck
Continuous mode
Discontinuous mode
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Steady State Transfer Function - Boost
Continuous mode
Discontinuous mode
Steady State Transfer Function Buck-Boost
Continuous mode
Discontinuous mode
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Microsystem would not operate when charged from zero voltage.
Microprocessor drew significant amount of power when attempting to
initialise at 0.9V, system locked in perpetual loop.
Start-up problems
Possible Solutions:
To guarantee a charging path even if storage device is depleted.
Voltage level detectors which do not allow the microsystem to boot (or to
start) until supply is above 2V.
With Cold Start circuit
MOSFET
XC61CSupercap
Without Cold Start circuit
Example from Perpetuum Inc. (VIBES project)
Startup-example-
Vctrl=0V
Charging curve Efficiency
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Energy Harvesting Storage Required
Scavenged energy is not constant
Power not available on-demand
High peak power not available
An ideal energy storage device:
Infinite shelf life
Negligible leakage
Unlimited capacity
Negligible volume
No need for energy conversion
Efficient energy acceptance and delivery
Ideal battery doesnt exist
Energy Storage Technologies
Options Secondary Batteries
Capacitors
Supercapacitor
Tradeoffs
Configuration Tiered Capacitor+Battery.
Battery-only, Capacitor-only
THF Batteries
Fuel cell
Batteries
Mature technology, high energy density, less efficient, limited to fewhundred full recharging cycles (significantly more shallow cycles)
Ultracapacitors (up to hundreds of Farads)
Virtually infinite recharge cycles, higher leakage current (goes up with size)
Energy Reservoirs will still play an important role
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Charge Termination Methods
Lead Acid Nicad NiMH Li-Ion
Slow Charge Trickle OK Tolerates Trickle Timer Voltage Limit
Fast Charge 1 Imin NDV dT/dt Imin at Voltage Limit
Recharging Issues
Fast Charge 2 Delta TCO dT/dt dV/dt=0
Back up Termination 1 Timer TCO TCO TCO
Back up Termination 2 DeltaTCO Timer Timer Timer
No general purpose method
E.g. Lithium batteries have:
wide voltage operating range
c range o e erm ne e en -o -c argeand undercharge
Mature Energy Storage Options on the market
Micro-power storage
Li-Ion Thin Film
Rechargeable
Super Cap Li-Ion
CapacitorRecharge Cycles 100s 5k-10k Millions Millions
Self Discharge Moderate Negligible High Moderate
Sec-
SMT & Reflow Poor-None Good Poor Poor
Physical Size Large Small Medium Large
Capacity0.3-2500mAHr 12-700uAHr
10-100uAHr 10-1600mAHr
EnvironmentalImpact High Minimal Minimal Minimal
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Looking forward: Fuel Cell
Membrane splits electrons off hydrogen
Electrons recombine with proton on other side incatalyzed reaction w. oxygen to form water
Photo showing conceptual Motorola/LANL fuel-cell-phone
Fuel in electricity, and exhaust out
Anode Cathode
Fuel Gas Temperature.25(C); Air Breathing;
PCB Mini Fuel Cell
Fuel Gas Pressure.Ambient;
H2 Flow Rate....0.030(slpm);
Relative Humidity.................100 %;
Max Power Density:
282 mW/cm2
Power : 1 W (0,52 V @ 1,94 A)
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Managing harvested energy
It is different from battery energy
Supply varies with time Need to adapt performance
Supply varies in space Different nodes get different energy: need load sharing
Supply is repetitive (does not die out) Opportunity to last forever
Efficiency concerns Match load to maximize transfer
Supply direct when possible, instead of through battery
Harvesting-Aware Policies
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Tasking aware of battery status & harvesting opportunities
Richer nodes take more load
Looking at the battery status is not enough
Harvesting-aware Management
Learn Local Energy
Characteristics Distributed
Topology
Control
Learn the energy environment
re c u ureEnergy
OpportunityLearn
Consumption
Statistics
for
Scheduling
Routing
Clustering
Energy harvesting Electronic System DesignWhat is dif f erent in Soft war e and Fir mwar e development ?
[Sunergy: June 2007]
Conventional energy management: How do we save energy ?Energy harvesting management: When do we use energy ?
Determine an optimal on-line schedulingof activities:
If the set of activities is schedulable, it determines a feasible schedule.
Determine decisions on the application levelthatoptimize the long term system behavior
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System Reconfiguration
Environmental energy is variable (solar power, vibrational
microgenerators, thermal scavengers)
T es of reconfi urations
SW: Lazy scheduling [Brunelli06], adaptive power management [Kansal06,Moser07],
Game theoretic approach to determine sleep/wake-up schedules [Nihato07]
HW: Reconfiguration through FPGA [Nahapetian07,Susu07]
Concept
Exploit period of light to reconfigure system to execute nexttasks with less power
Statistical energy availability estimation to decide about
reconfiguration
maximize the work done adapting to the available energy profile
energy source S
Lazy Scheduling: Model
TaskJi
can be reem ted
energy storage
computing
device
PS(t)
PD(t)
D
EC(t) C
arrives at time ai
has deadline di
needs total energy eito complete
can consume power
tasks
a1, e1, d1 a2, e2, d2
J1 J2 therefore, needs time
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When do we use energy ?
21
scheduling is not
suited.
1 2
ALAP does not
work either.
And what happens if the energy storage is full?
When do we use energy ?
21
scheduling is not
suited.
1 2
ALAP does not
work either.
And what happens if the energy storage is full?
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Lazy Scheduling Algorithm
Rule 1:
ai di
ei
tsi
d
iCii
p
tdtECds
))()(,min(
Lazy Scheduling Algorithm
Rule 2:
ai di
ei
tsi
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LSA example
a1 d1s1s2a2 d2t
Features Start time Sican be computed once when the task is scheduled
Energy is not wasted on task that cant be finished
Admittance Test
The proof uses
Pmaxconcepts of network
calculus
and real-time calculus.
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Performance
* EDF
LSA
X axis = max Capacity
Y axis = time of the first overflows
Capacity savings of ~40% measured for
random task sets for LSA with l()
compared to EDF
Conventional energy management: How do we safe energy ?
Energy harvesting: When do we use energy ?
Energy harvesting Software Design
What is different?
If sensor node is not OS equipped:
Determine decisions on the application level
that optimize the long term system behavior
Determine decisions on the application level
that optimize the long term system behavior
sensing rate receive messages
data transmission forward messages
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Conventional energy management: How do we safe energy ?
Energy harvesting: When do we use energy ?
Energy harvesting Software Design
What is different?
Determine decisions on the application level
that optimize the system behavior
Determine decisions on the application level
that optimize the long term system behavior
sensing rate receive messages
If sensor node is not OS equipped:
minimal sensing ratereactivity
freshness of dataaverage throughput
data transmission forward messages
Principles: Model predictive control
Model predictive control is the class of advanced control techniques
most widely applied in the process industries.
The main idea of MPC is to choose the control action b re eatedl
solving on line an optimal control problem.
MPC is based on iterative, finite horizon optimization of the system
under control Receding Horizon Control
MPC :
o e mo e o t e process system un er contro s requ re .
Predictive Optimization is based on the predicted evolution of the model
Control It is usually adopted for complex systems
(Multi-Input Multi-Output)
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Principles: Receding
Horizon ControlTwo Steps
At time k, solve an open loop optimal control
only thefirst input (i.e. control law for timek+1)
At time k+1 repeat the same procedure. (Theprevious optimal solution is discarded!)
Prediction of opponents moves
Optimization of outcome a few moves ahead
An unexpected move from the opponent =
change of strategy!
Good players thinks several moves ahead =
long prediction horizon!
Principles
Optimization problem: finite horizon control
t
current time t
current state (memory, battery, )
current environment (input power)
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System Model
run-time
platform
Models for application, quality/utility, system behavior ?
Optimization problem ?
Efficient run-time implementation ?
Principles Optimization problem: finite horizon
control
sensing/transmitting optimization
Rate of acquisition
Memory usage
Stored energy
Used memory
Final stored energy
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Principles
Solving a linear program in a resource-
constraint sensor node at each time step ?
- Approach Solve the LP as a parameterized LP and
implement the explicit solution [Morari, Bemporad et al.]:
The optimal controller is a set of controllers with an affinecontroller selection rule Desgin issue: limiting the number of different controllers Preliminary results on highly constrained CPU are promising
Different control laws in different regions of the state space!
Simulation and Experiments
Example 1
sensing rate control
minimize interval
between samples
Example 2
rate control with
memory buffer
minimize interval
between samples
minimize amount
of stored dataGain:
56,8 %
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Distributed energy management
How can a distributed system manage the harvestedenergy to maximize performance of system as awhole?
Energy resources vary across nodes,
Task-load differs at different nodes, some workload is share-able while some is not
Consider one energy intensive task: routing data Determine environmental energy aware communication
Routing paths can change depending on energyavailability
However, how to distribute this information?
Distributed algorithms with low messaging overhead arerequired
EH aware routing
EH routing must be able to exploit nodes with
between nodes
[Lattanzi06]
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Case Studies
Electrostatic Electromagnetic Piezoelectric
VibrationsCase Study
More easilyimplemented in
Typically output ACvoltages is below 1 volt
The output voltage isirregular and depends
s an ar m cro-machining processes
Requires a separatevoltage source (suchas a battery) to beginthe conversion cycle.
n magn u e
Not easy to implementwith MEMS technologies
on e cons ruc ons
An overvoltageprotection circuits isrequired
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Case Study
-Electromagnetic transducer-
Unique control
modulated by
polaritydetection Boost Topology for step-up
In-phase sinusoidal current from
a sinusoidal source
Source: S. Roundy
to eliminate theneed for rectifier
Impedance matching by alteringduty cycle
Not overlapping control signals
Case Study-Electr omagnet ic t r ansducer-2-
Seiko Kinet ic
Oscillating Weight
Magnetic Rotor
Induced Current
Harvested Energy
upercapac or
Boosting Circuit
na apac or
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Case Study
-seiko kinetic- Boosting Circuit
x3
By means of two flying capacitors and
charge is transferred into the final capacitor
where the voltage level rises faster.
used as charge tank.1C
2C 3C
4C
Case Study
-seiko kinetic- Boosting Circuit
In few seconds, the final capacitorreaches 3x the SuperCap voltage.
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Case Study
-Electromagnetic transducer-3--EnOcean-
complete inversion of a permanentMagnetic filed
Voltage and current generate by
Lents law is enough to transmit
16bits
New generation of devices with
self-powered sensors andbidirectional wireless
communication
Case Study-ThermoElectric Generator- TEG
Seiko Thermic wristwatch, convert heat from the wrist (body
heat) into electricity.
Thermoelectric conversionCarnot efficiency : ( TH - TL) / TH = T / TH.
Seebeck-Peltier effect
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Case Study
-TEG-
TEGs output voltage is very low
TEGs have a maximum power point(MPP) which change with T
MPP is usually the half of the open
circuit voltage (Vteg-oc)
Problem: Internal resistance of TEG
epen s on empera ure an agng
Case Study-TEG-
Essentially a boost converter with auto-generation of the control signal(regulation loop)
The circuit starts to work with 20 mV due to JFET and L2 (
(Spies et al.)
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Case Study
-Power Delivery to Bio-Implantable wireless circuits-
Output voltage to
(Dondi et al.)
device 2,2V
Size: 1 cm2
WSN HW support a wide voltagesupply range (usually between 1V
and 4V )
Case Study
-Sub-mW PV cells-
Powering sensor nodes with unregulated and variable voltage supply fromthe solar cell adaptive Active-Recovery DC
Minimize the energy used for DC/DC or linear regulation
Tmote Sky 2,1 3,6 V
TI Node 1,8 3,6V
TinyNode 584 2,4 3,6 V[solar scavenger 10mm2
PV surface: Brunelli, Benini]
Automatically adapt duty-cycle with analog thresholds (comparators)
on voltage supply
Optimize thresholds for MPP in low-lighting condition (no tracking at
high lighting as energy is over-abundant)
Indoor PV powering is feasible!
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Approach
Select the desired light intensity and find the solar cell MPP A window (Vth1 , Vth2 ) is defined around the MPP forcing the
senor node to operate in this range of values.
Sub-mW PV cells
-How it works- Inductor-less solar harvesterDesign of the energy storage and
conversion circuitry togetherwith
the target platform
Vth2
Vth1
Energy available for the whole Activity time
C, Vth1, Vth2 are evaluated to guarantee
the complete execution of the worst-case task or activity
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Adaptive duty cycling
Activity time grows with energy intake
PV energy harvesting is usable indoor
Implementation
example
[solar cell for ZigBee Sensor node]
Cmin is evaluated by characterizing the most power-consuming operations,
in order to guarantee the completion of the worst-case task
30 packets each cycle
Cmin = 0.1F
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EM harvesting Easy
Inductively powerered WSN Node
Energy harvesting exploiting the EM
+ +
idle (no measurement) times
Research supported by a grant of Telecom Italia
Fully energy-neutral solution
EM Harvesting - Hard
Energy harvested from RF waves,
generated by a transmitter
(wireless power transmission)
ore e energy w
supercapacitor like energy buffer
Rectenna
RFID
transmitter
868 MHz
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Power Transfer Efficiency
WISP - 2009
WISP - 2007
Power Cast -
2009
Lessons Learned:
Power levels are low (tens of W)
Advanced RF & Antenna design is needed
Video
Wireless Power over Distance
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Electrostatic Conversion
Use compression/tension between parallel plates
Use ambient or intentional vibration to cause motion between
plates
Electrostatics tractable only if very small air gaps (microns) due to
Energy filled by internal
source
Net converted energy
field breakdown
U
QC
Piezoelectric Effect
Some materials present relations
between deformation and electric
field 1
23
T
+
_
+
_
Unactivated
L
Activated
V
+
_
L+L
W+W
T-T
+_ +_
S sT dE
mp e mo e equa on:
T Stress S Strain
s Compliance E Electric Field Strength
d Piezoelectric Coefficient
Size 9,8 x 5,7 x 3 cm
Weight ~120 g
Energy buffer 4,7 F
Mean power (benchmark 2 Hz) 18 W
Energy (1 min.) 1,1 mJ
ezoe ec r c ro o ype
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Aluminum chassis mounted on a customized orthopedic knee brace
(1.6Kg)
Donelan et. Al, Science 2008: 5W from leg movement with no extra effort!BUT
Kinetic Harvester with micro-motors
Kinetron (NL)
cromo ors
12,4 mJEnergy per minute
206WAverage power (2 Hz)
4700FStorage Capacitance
~80 gWeight
6,5 x 2,5 x 2,5 cmSize
(10x more than piezo!)
Energy from wrist movement
Charge controlcircuit
Oscillating weight
Proof mass oscillation directly cranks generator rotor
Drive circuitGear train
Rotor
Stator
Coil
power supply
Seiko AGS System
Charge accumulated on capacitor
Power Output: 5 W average when the watch is worn 1 mW or more when the watch is forcibly shaken
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Energy Harvester Output Power
Transducer mechanisms include electrostatic MEMS,
piezoelectric, and electromagnetic
Output power between 10 W and 1 mW for typical
vibration scenarios
Commercial products
more than 20 mW in the presence of a
si nificant vibration
Volturewww.perpetuum.co.uk
very weak vibration (e.g. microwave oven
0.24 g's, 120 Hz) it is able to harvest 43W.
The Sustainable Dance
Floor
www.enviu.org
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Thermoelectric conversion
Thermoelectric conversion
Carnot efficiency : ( TH - TL) / TH = T / TH.
Thermo pile (thermolife)Seebeck-Peltier effect
Applied Digital Solutions Thermo Life (10 A at 3 Vwith only 5 degrees Celsius of temperaturedifference ).
Store extra energy produced during periods ofhigher T so they can continue to run duringwarmer, less efficient ambient temperatures.
TEGs Have Variable Polarity
Output polarity of TEG is dependenton the direction of the temperaturegradient
Capability to work with positive andnegative input voltages
Rectifier structure: Polarity switch controlled by a
comparator
- -where still no supply voltage for
the comparator is present
prototype: Start-up 150mV, drop 40 mV,later on5 mV
[Fraunhofer]
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Other issues with TEGs
Vout < 1V when T is low Boost DC/DC
R with T MPTT is needed
Solutions similar to PV (indoor) harvester
Thermoelectric example
Seiko Thermic wristwatch, convert heat from the wrist
(body heat) into electricity.
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Commercial harvesters
PV: quite mature, with many products Flexible PV materials are interestin e. .
www.powerfilmsolar.com
Solution provides www.enocean.com (Piezo, kinetic, solar)
www.kinetron.com (EM kinetic)
www.micropelt.com (thermal)
www.powercast.com (RF transmission)
www.microstrain.com (Piezo) and many others EH forum
www.energyharvesting.net
Enhanced Power Unit Architecture
Energy
Transducers 2Kinetic Energy,
Photovoltaic
Power Unit
Monitor
Energy
Transducers 1Kinetic Energy,
Photovoltaic
Conversion Electronics
Take raw electrical signal from
transducer and convert it to a usable
DC voltage
Energy Storage and Delivery
Receives energy from conversion
electronics and stores it (SuperCap,
batteries, etc. ) Regulates the output
voltage and current.
Power
Supply
energy and
battery charging
status,
elaborates
energypredictions and
provides
information to the
powered system
Energy Delivery
96
Wearable sensing and elaboration platform
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Power Supplyresearch branches f or next 10 year s
Matching
circuit
Power
electronics
Design
optimization
Mechanical
fabrication
HW/SW
co-design
Batteries Energy Scavenging Fuel Cells Etc.
MotionSolar RF
. . .
. . .
Application areas over next 10 years:smart homes, fatigue monitoring, ubiquitous data access for people, building env. control,
emergency response in commercial buildings, manufacturing monitoring and control, inventory tracking, etc.
Power Supplies
Summary
Energy harvesting systems are promising for manyautonomous and distributed applications
ner y arves n an permanen power s ora e
devices are self-power enablers
All system components need to be Energy Aware
Excellent HW design is the a key factor
but also developing effective power management
a gor ms p ays a un amen a ro e.
Distributed energy awareness is the frontier
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an ou.