EES13 - Energy Harvesting.wsn

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Energy Harvesting Devices

Davi de Br unel l i

[email protected]

Universi t y of Trent o



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!!



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]



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



• 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



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



<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



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



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?



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



Energy from Human daily activity

Thad Starner, Human-Powered Wearable Computing, IBM Systems

Journal 35, pp. 618-629 (1996).



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



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



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



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



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)



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



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



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

500

1000

1500

2000

0 150 300 450 600 750 900

T (s)

V c ( m V )

10.9J 15.7J



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



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



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 !!



Buck: Vo <= Vi

Boost: Vo >= Vi

Buck-boost: Vo <=> -Vi

DC/DC-Typical Power Converter Topologies-



Steady State Transfer Function - Buck

Continuous mode

Discontinuous mode



Steady State Transfer Function - Boost

Continuous mode

Discontinuous mode



Steady State Transfer Function – Buck-Boost

Continuous mode

Discontinuous mode



• 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



Startup-example-

Charging curve Efficiency

Vctrl=0V

Energy Harvesting Storage Required



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



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



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



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



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 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



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



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



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



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



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 ?



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 ?



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




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




ai di

ei

t s i

Rule 2:

LSA l



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



Admittance Test

Pmax

The proof uses

concepts of network

calculus

and real-time calculus.

P f



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



• 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



sensing rate

data transmission

receive messages

forward messages

E h ti S ft D i




that optimize the 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



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



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!



Principles

current time t current state (memory, battery, …)

current environment (input power)

• Optimization problem: finite horizon control

t

System Model



System Model

environment

run-time

platform

Models for application, quality/util ity, system behavior ?

Optimization problem ?

Efficient run-time implementation ?

P i i l



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



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



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



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?



EH aware routing

• EH routing must be able to exploit nodes with

high energy intake and take into account distance

between nodes

[Lattanzi06]



Case Studies

Case Study



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



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



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



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



Case Study

-seiko kinetic- Boosting Circuit

In few seconds, the final capacitor reaches 3x the SuperCap voltage.

Case Study



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



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



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



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



y

-Power Delivery to Bio-Implantable wireless circuits-

Size: 1 cm2

Output voltage to

the implanted

device 2,2V

(Dondi et al.)

Case Study



• 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



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



-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



• Activity time grows with energy intake

PV energy harvesting is usable indoor

Implementation



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



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



• 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



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



Video

Electrostatic Conversion



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



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



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



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



• Transducer mechanisms include electrostatic MEMS,

piezoelectric, and electromagnetic

• Output power between 10 µW and 1 mW for typical

vibration scenarios

Commercial products



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



• 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

http://www.powerfilmsolar.com/

http://www.enocean.com/

http://www.kinetron.com/

http://www.micropelt.com/

http://www.powercast.com/

http://www.microstrain.com/

http://www.microstrain.com/

http://www.powercast.com/

http://www.micropelt.com/

http://www.kinetron.com/

http://www.enocean.com/

http://www.powerfilmsolar.com/



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



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



• 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



Thank You.

EES13 - Energy Harvesting.wsn

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