Transcript
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Energy Harvesting Devices
Davi de Br unel l i
davide.brunelli@unitn.it
Universi t y of Trent o
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The nightmare of pervasive embedded computing:
Power avalaibility
Ubiquitous computing’s dream of pervasive sensors and electronics
everywhere is accompanied by the nightmare of battery replacement
and disposal.
No Moore’s Law in batteries:
2-3%/year growth
Battery Technology is Stuck!
ES lifetime dependson battery life!!
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Limits to Battery Energy Density
Energy Density by Mass (MJ/kg)
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
TNT
2012 - Nanowire-based
lithium ion battery
2004 - Lithium ion at its
current max
1991 - Lithium Ion battery
released
1899 - NiCd battery created
• Processing power doubles every 2 years, but…
• Battery capacity doubles every 10 years
• We need a more efficient way to enable longer life
Research in progress
[TI09]
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Available Energy is All Around
Light
Motion and
vibration
EM waves
Heat
Technology trend:
Design systems that harvest limited energy from ambient (heat, light, radio,
or vibrations…) or scavenge power from human activity
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• Energy Harvesting shrinks or replaces batteries or
extents recharge periods
• 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
Energy Harvesting Basics
Ener gy har vest i ng is the process by which
energy is captured and stored
This term often refers to small
autonomous devices – micro energy
harvesting
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The Good News
WSN
Mobile terminals Batteries
Today’s
Scavengers
• The gap between scavengers energy and requirements of digital
systems is shrinking [Paradiso05]
• Exploit energy management strategies and improvements in scavenger
technology
– Overcome traditional energy management strategies (battery-driven)
• An new unified design methodology is required
– Smart adaptation
– Design for unreliability – Exploit unpredictable power sources
scavenger evolution
scavenger-aware
design
WSN evolution
Today’s WSNs
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<10kb/s
1%
DSP&storage
Security
MAC
RFNon-EWorld
Sensor CE-ADC Processor PicoRadio
20 W 20 W 40 W 20 W Avg.Power80 Mops 2nJ/b
Energy Harvester
Objective: 100 µW Avg Energy neutrality becomes “ easy”
Power Mgr
Ambient energy
Sensor Node Evolution
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Where we are now
1W
100 mW
10 mW
1 mW
100 µW
10 µW
1 µW
Harvesters Consumers
Average Power
Small piezo beam vibe harvesters
Large inductive vibe harvesters
1 in2
TEG on crease beam
TEG stringer clip
1 cm2 a-Si PV in cabin lighting
1 cm2 a-Si PV in blue sky
1 cm2 a-Si PVin sun lit airplane pax window
Wireless dimming window
Push button transmitter
Sensor @ 2.8 hrs interval
AAA LED flash light
Cell phone
Wireless sensor @ 1 HzPush button harvester
GSE monitoring sensor(log data every 10sec, Tx 2X per day)
Zigbee mesh network node(w/ Rx from wireless sensor)
TI MSP430 microprocessor (awake)
TI MSP430 microprocessor (asleep)Chipcon CC2500 radio (asleep)
Chipcon CC2500 radio (Tx mode)
6 mm2 TEG on hydraulic line
TEG=thermoelectric generator
Energy Harvesting Power Generation & Utilization
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Energy Harvesting: new design methodology
Hardware Design• Conversion efficiency
• Impedance Matching
• Maximum power transferred
• …
Software Design• Scheduling algorithm
• Adaptive duty cycle
• Energy prediction algorithm• …
Low Power Design
Power Aware Design
Battery Aware Design
Energy Harvesting Aware Design
Natural progression of Energy
Optimization Techniques
Why is it different?
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1uW 10uW 100uW 1mW 10mW
Seiko watch
~5uW
2 channel EEG
~1mW
100mW 1W+
Energy Source Characteristics Efficiency Harvested Power
Light OutdoorIndoor
10~24% 100 mW/cm2
100 µW/cm2
ThermalHuman
Industrial
~0.1%
~3%
60 µW/cm2
~1-10 mW/cm2
Vibration
~Hz–human
~kHz–machines
25~50%
~4* µW/cm3
~800 µW/cm3
RFGSM 900 MHz
WiFi~50%
0.1 µW/cm2
0.001 µW/cm2
Adapt ivEnergy
~10mW
~30mm
Holst Center
~40uW
Energy Harvesting Sources
BigBelly
~40W
Elastometer
~800mW
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Energy from Human daily activity
Thad Starner, Human-Powered Wearable Computing, IBM Systems
Journal 35, pp. 618-629 (1996).
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AirflowGoal
Investigate energy harvesting andmanagement technologies that can
support the operation of a smartsensor node indefinitely
Effective, long term, power supplies are limited and/or expensive
Example: At an average powerconsumption of 100 mW, you need morethan 1 m3 of lithium battery volume for 1year of operation.
PV
Kinetic
Inductive
RF
Contacts: Telecom Italia, STM
Environmentally powered wireless sensors
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Mixed Architecture
Energy Harveter and
Smart Power Unit
EH powered nodesphilosophy
Sensors
Input
protection
ADCCPUWireless
EH-management
Switch
Supercapacitor Battery
Switch
Independent
Load
Ref2Ref1
Supercapacitor Battery
• General purpose
Optimized 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
Interface
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Design Methodology
Gener i c App r oach
• Dedicated blocks, depending on energy source, ambient
conditions and application
• Not all are required in any application and with any source
• 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
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Ambient Energy
Non-monotone, Unpredictable
• Ex: solar power (PV-cells)
• Ex: power waveform from
human walk (piezo-scavengers)
18[Paradiso05]
Too much
Too little
Aperiodic
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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
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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
prevent degrading efficiency from the
forward voltage drop
• Typical values:• Start-up 150mV,
• drop 40 mV, later on 5 mV
• Diodes are only active during start-up
where still no supply voltage for the
comparator
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Maximum Power Point Tracking
• Maximum power from source to load when internal resistances arematched
• Input resistance of a DC-DC converter is influenced with its duty
cycle
Ideal si tuation:• 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-
January 25, 2007 Ing. Davide Brunelli 23
0
20
40
60
80
100
120
140
0 0,5 1 1,5 2 2,5 3 3,5 4 4,5
V[Volt]
I [ m A
I-V chart
I [ u A ]
V [Volt]
0
50
100
150
200
250
300
0 0,5 1 1,5 2 2,5 3 3,5 4 4,5
V[Volt]
P [ u W
P [ m W ]
V [Volt ]
P-V chart
0
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0 150 300 450 600 750 900
T (s)
V c ( m V )
10.9J 15.7J
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P
Vtransducer
VsolarVctrl
Vsolar
Controlled variable
Vlow, Vhigh duty cycle
Vlow crossing switch off
Vhigh crossing switch on
Vlow
Vhigh
Online control for tracking Transducer curve variations
MPP Regulator
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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
suited for Energy Harvesting
• 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
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MPPT Techniques
• Energy storage required
• Intelligent, adaptive power management ensures maximum power
output
• Switching frequency is fixed and depends
on circuit parameters and components.
• Maximum Power Point Tracker duty cycle
is controlled and output power
measured
• Increasing output power: duty cycle is
changed further in the same direction and
vice versa
Climb the Hill !!
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Buck: Vo <= Vi
Boost: Vo >= Vi
Buck-boost: Vo <=> -Vi
DC/DC-Typical Power Converter Topologies-
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Steady State Transfer Function - Buck
Continuous mode
Discontinuous mode
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Steady State Transfer Function - Boost
Continuous mode
Discontinuous mode
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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 toinitialise at 0.9V, system locked in perpetual loop.
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 tostart) until supply is above 2V.
controller
MOSFET
XC61CSupercap
With Cold Start circuit
Without Cold Start circuit
Example from Perpetuum Inc. (VIBES project)
Start-up problems
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Startup-example-
Charging curve Efficiency
Vctrl=0V
Energy Harvesting Storage Required
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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 doesn’t exist
h l
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Energy Storage Technologies
• Tradeoffs
– 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)
• Configuration Tiered Capacitor+Battery.
Battery-only, Capacitor-only
• Options
Secondary Batteries Capacitors
Supercapacitor
THF Batteries
Fuel cell…
Energy Reservoirs will still play an important role
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Charge Termination Methods
Lead Acid Nicad NiMH Li-IonSlow Charge Trickle OK Tolerates Trickle Timer Voltage Limit
Fast Charge 1 Imin NDV dT/dt Imin at Voltage Limit
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
Recharging Issues
• No general purpose method
E.g. Lithium batteries have:
• wide voltage operating range
• thick range to determine the end-of-charge
and undercharge
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Mature Energy Storage Options on the market
Li-IonThin Film
RechargeableSuper Cap
Li-Ion
CapacitorRecharge Cycles 100s 5k-10k Millions Millions
Self Discharge Moderate Negligible High Moderate
Charge Time Hours MinutesSec-Minutes Minutes
SMT & Reflow Poor-None Good Poor Poor
Physical Size Large Small Medium Large
Capacity0.3-2500mAHr 12-700uAHr
10-100uAHr 10-1600mAHr
Environmental
Impact High Minimal Minimal Minimal
Micro-power storage
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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
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Anode Cathode
• Fuel Gas Temperature…….25(°C); Air Breath ing;
• 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)
PCB Mini Fuel Cell
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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
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Harvesting-Aware Policies
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Learn Local Energy
Characteristics
Predict Future
EnergyOpportunity
Learn
Consumption
Statistics
Distributed
Decision
forScheduling
Topology
Control
Routing
Clustering
• Tasking aware of battery status & harvesting opportunities
– Richer nodes take more load – Looking at the battery status is not enough
• Learn the energy environment
Harvesting-aware Management
Energy harvesting Electronic System Design
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Energy harvesting Electronic System Design
What is di f f er ent in Sof t war e and Fir mwar e development ?
Conventional energy management: How do we save energy ?
Energy harvesting management: When do we use energy ?
[Sunergy: June 2007]
Determine an optimal on-line scheduling of activities:
If the set of activities is schedulable, it determines a feasible schedule.
Determine decisions on the application level thatoptimize the long term system behavior
System Reconfiguration
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System Reconfiguration
• Environmental energy is variable (solar power, vibrational
microgenerators, thermal scavengers)
• Types of reconfigurations
– 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
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energy source
energy storage
computing
device
tasks
PS(t)
PD(t)
a1, e1, d1 a2, e2, d2
D
J1 J2
S
EC(t) ≤ C
Lazy Scheduling: Model
Task Ji– can be preempted
– arrives at time ai
– has deadline di
– needs total energy eito complete
– can consume power
– therefore, needs time
h d ?
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21
Greedy
scheduling is not
suited.
When do we use energy ?
1 2
ALAP does not
work either.
And what happens if the energy storage is full?
Wh d ?
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21
Greedy
scheduling is not
suited.
When do we use energy ?
1 2
ALAP does not
work either.
And what happens if the energy storage is full?
Lazy Scheduling Algorithm
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Lazy Scheduling Algorithm
ai d i
ei
t s i
d
iC ii
p
t d t E C d s
))()(,min( −+−=
ε
Rule 1:
L S h d li Al ith
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Lazy Scheduling Algorithm
ai di
ei
t s i
Rule 2:
LSA l
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LSA example
a1 d1 s1 s2a2 d2 t
Features
• Start time Si can be computed once when the task is scheduled
• Energy is not wasted on task that can’t be finished
Ad itt T t
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Admittance Test
Pmax
The proof uses
concepts of network
calculus
and real-time calculus.
P f
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Performance
X axis = max CapacityY axis = time of the first overflows
* EDF
• LSA
Capacity savings of ~40% measured forrandom task sets for LSA with ε l( Δ)
compared to EDF
E h ti S ft D i
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• Conventional energy management: How do we safe energy ?• Energy harvesting: When do we use energy ?
If sensor node is not OS equipped:
Energy harvesting Software Design
What is different?
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
data transmission
receive messages
forward messages
E h ti S ft D i
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Determine decisions on the application level
that optimize the system behavior
Determine decisions on the application level
that optimize the long term system behavior
minimal sensing ratereactivity
freshness of dataaverage throughput
sensing rate
data transmission
receive messages
forward messages
• Conventional energy management: How do we safe energy ?• Energy harvesting: When do we use energy ?
If sensor node is not OS equipped:
Energy harvesting Software Design
What is different?
Principles: Model predictive control
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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 by repeatedly
solving on line an optimal control problem.
MPC is based on iterative, finite horizon optimization of the system
under control Receding Horizon Control
MPC :
Model A model of the process (system) under control is required.
Predictive Optimization is based on the predicted evolution of the model
Control It is usually adopted for complex systems
(Multi-Input Multi-Output)
P i i l R di
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Principles: Receding
Horizon ControlTwo Steps
• At time k , solve an open loop optimal control problem over a predefined horizon and applyonly the first input (i.e. control law for timek+1)
• At time k+1 repeat the same procedure. (Theprevious optimal solution is discarded!)
MPC is like playing chess!!
• 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!
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Principles
current time t current state (memory, battery, …)
current environment (input power)
• Optimization problem: finite horizon control
t
System Model
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System Model
environment
run-time
platform
Models for application, quality/util ity, system behavior ?
Optimization problem ?
Efficient run-time implementation ?
P i i l
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Principles• Optimization problem: finite horizon
control– Example: Linear program for
sensing/transmitting optimization
Rate of acquisition
Memory usage
Stored energy
Used memory
Final stored energy
Principles
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p
• Efficient run-time implementation – 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 affine“controller selection” rule• Desgin issue: limiting the number of different controllers• Preliminary results on highly constrained CPU are promising
Different control laws in dif ferent regions of the state space!
Solving a linear program in a resource-
constraint sensor node at each time step ?
Simulation and Experiments
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Simulation and Experiments
Example 1
sensing rate control
minimize interval
between samples
Example 2
rate control with
memory buffer
•minimize intervalbetween samples
•minimize amount
of stored dataGain:
56,8 %
Distributed energy management
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Distributed energy management
• 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
strategy
• Routing paths can change depending on energyavailability
– However, how to distribute this information?
– Distributed algorithms with low messaging overhead arerequired
How can a distributed system manage the harvested
energy to maximize performance of system as awhole?
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EH aware routing
• EH routing must be able to exploit nodes with
high energy intake and take into account distance
between nodes
[Lattanzi06]
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Case Studies
Case Study
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Electrostatic Electromagnetic Piezoelectric
•More easilyimplemented instandard micro-
machining processes
•Requires a separatevoltage source (suchas a battery) to beginthe conversion cycle.
•Typically output ACvoltages is below 1 voltin magnitude
•Not easy to implementwith MEMS technologies
•The output voltage isirregular and dependson the constructions
•An overvoltageprotection circuits isrequired
Vibrations
Case Study
Case Study
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modulated by
polarity
detection
Case Study
-Electromagnetic transducer-
• Boost Topology for step-up
• In-phase sinusoidal current from
a sinusoidal source
• Two converter
to eliminate theneed for rectifier
• Impedance matching by altering
duty cycle
• Not overlapping control signals
Unique control
signal
Source: S. Roundy
Case Study
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Case Study-El ect r omagnet i c t r ansducer -2-
Seiko Kinet i c
Oscillating Weight
Magnetic Rotor
Induced Current
Harvested Energy
Supercapacitor
Boosting Circuit
Final Capacitor
Case Study
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Case Study
-seiko kinetic- Boosting Circuit
By means of two flying capacitor s and
charge is transferred into the final capacitor
where the voltage level rises faster.
Charge is initially stored into a supercapacitor
used as charge tank.1
C
2C
3C
4C
x3
Case Study
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Case Study
-seiko kinetic- Boosting Circuit
In few seconds, the final capacitor reaches 3x the SuperCap voltage.
Case Study
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Case Study-Electromagnetic transducer-3-
-EnOcean-
• Pushing a button causes a
complete inversion of a permanent
Magnetic filed
• Voltage and current generate byLents law is enough to transmit
16bits
• New generation of devices with
self-powered sensors and
bidirectional wirelesscommunication
Case Study
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Case Study
-ThermoElectric Generator- TEG
Seiko Thermic wristwatch, convert heat from the wrist (body
heat) into electricity.
Thermoelectric conversion
Carnot efficiency : ( TH - TL) / TH = ∆ T / TH.
Seebeck-Peltier effect
Case Study
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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
depends on temperature and aging
Case Study
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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.)
Case Study
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y
-Power Delivery to Bio-Implantable wireless circuits-
Size: 1 cm2
Output voltage to
the implanted
device 2,2V
(Dondi et al.)
Case Study
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• Powering sensor nodes with unregulated and variable voltage supply from
the solar cell adaptive Act ive-Recovery DC
− Minimize the energy used for DC/DC or linear regulation
− 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)
• WSN HW support a wide voltage
supply range (usually between 1Vand 4V )
Tmote Sky 2,1 – 3,6 V
TI Node 1,8 – 3,6V
TinyNode 584 2,4 – 3,6 V[µsolar scavenger 10mm2
PV surface: Brunelli, Benini]
Indoor PV powering is feasible!
Case Study
-Sub-mW PV cells-
Approach
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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 thesenor node to operate in this range of values.
Sub-mW PV cells
k
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-How it works-
Vth2
Vth1
Inductor-less solar harvester
Design of the energy storage and
conversion circuitry together with
the target platform
Energy available for the whole Activity time
C, Vth1, Vth2 are evaluated to guarantee
the complete execution of the worst-case task or activity
Adaptive duty cycling
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• Activity time grows with energy intake
PV energy harvesting is usable indoor
Implementation
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example
Cmin is evaluated by characterizing the most power-consuming operations,
in order to guarantee the completion of the worst-case task
[µsolar cell for ZigBee Sensor node]
30 packets each cycle
Cmin = 0.1F
EM harvesting – Easy
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Energy harvesting exploiting the EM
field from AC electric current during
idle (no measurement) times
• Inductively powerered WSN Node
+ +
Research supported by a grant of Telecom Italia
Fully energy-neutral solution
EM Harvesting - Hard
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• Energy harvested from RF waves,
generated by a transmitter
(wireless power transmission)
• Store the energy with
supercapacitor like energy buffer
RFID
transmitter868 MHz
Rectenna
Power Transfer Efficiency
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y
3,9
4
4,1
4,2
4,3
4,4
4,5
4,6
0 2 4 6 8 10 12
M a x i m u
m V
o l t a g e [ V ]
Distance [m]
WISP - 2007
WISP - 2009
Power Cast -
2009
Lessons Learned:
Power levels are low (tens of µW)
Advanced RF & Antenna design is needed
Wireless Power over Distance
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Video
Electrostatic Conversion
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U
Q
C =
Energy filled by internal
source
Net converted energy
• 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
field breakdown
Piezoelectric Effect
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Some materials present relations
between deformation and electric
field
Unactivated
1
23
T
L
W
+
_
+
_
Activated
V
+
_
L+∆L
W+∆W
T-∆T
+ _ + _
S sT dE = +
Simple model equation:
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 μF
Mean power (benchmark 2 Hz) 18 μW
Energy (1 min.) 1,1 mJ
Piezoelectric Prototype
Kinetic Harvester with micro-motors
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12,4 mJEnergy per minute
206 μW Average power (2 Hz)
4700 μF Storage Capacitance
~80 gWeight6,5 x 2,5 x 2,5 cmSize
Aluminum chassis mounted on a customized orthopedic knee brace(1.6Kg)
Kinetron (NL)
(10x more than piezo!)
Donelan et. Al, Science 2008: 5W from leg movement with no extra effort!
BUT
Micromotors
Energy from wrist movement
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Proof mass oscillation directly cranks generator rotor– Little intervening mechanics
– Charge accumulated on capacitorPower Output:– 5 µW average when the watch is worn– 1 mW or more when the watch is forcibly shaken
Charge control
circuit
Drive circuitGear train
Rotor
Stator
Coil
Oscillating weight
Secondarypower supply
Seiko AGS System
Energy Harvester Output Power
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• Transducer mechanisms include electrostatic MEMS,
piezoelectric, and electromagnetic
• Output power between 10 µW and 1 mW for typical
vibration scenarios
Commercial products
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p
• more than 20 mW in the presence of a
significant vibration
•very weak vibration (e.g. microwave oven
0.24 g's, 120 Hz) it is able to harvest 43µW.
Volture
www.perpetuum.co.uk
The Sustainable DanceFloor
www.enviu.org
Commercial harvesters
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• PV: quite mature, with many products– Flexible PV materials are interesting e.g.
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
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96
Conversion Electronics
Take raw electrical signal from
transducer and convert it to a usable
DC voltage
Energy
Transducers 2
Kinetic Energy,Photovoltaic …
Energy Storage and Delivery
Receives energy from conversion
electronics and stores it (SuperCap,
batteries, etc. ) Regulates the output
voltage and current.
Wearable sensing and elaboration platform
Fuel Cell
Power
Supply
Power Unit
Monitor
Measures
energy and
battery charging
status,
elaborates
energypredictions and
provides
information to the
powered system
Energy Delivery
Energy
Transducers 1
Kinetic Energy,Photovoltaic …
Power Supply
r esearch br anches f or next 10 years
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y
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
Batteries Energy Scavenging Fuel Cells Etc.
MotionSolar RF
Matching
circuit
Power
electronics
Design
optimization
Mechanical
fabrication
. . .
. . .
HW/SW
co-design
Summary
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• Energy harvesting systems are promising for many
autonomous and distributed applications
• Energy Harvesting and permanent power storage
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
algorithms plays a fundamental role.
• Distributed energy awareness is the frontier
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Thank You.
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