EES12 - Energy Harvesting

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    Energy Harvesting Devices

    Davi de Brunelli

    [email protected]

    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.