This document discusses energy harvesting for powering sensor networks as part of the Internet of Things. It motivates the need for energy harvesting due to the limited battery life of sensors. It then outlines Los Alamos National Laboratory's work developing multi-source energy harvesting systems to power structural health monitoring devices for wind turbines. Finally, it discusses challenges that need to be addressed for successful large-scale deployment of energy harvesting-enabled IoT systems.
The Spansion Energy Harvesting family includes the MB39C811, an ultra-low-power buck PMIC with dual input that enables efficient harvesting from both solar and vibration energy; and the MB39C831, an ultra-low-voltage boost PMIC for solar or thermal. The Spansion Energy Harvesting family of devices works seamlessly with Spansion FM0+ microcontrollers (MCUs), ultra-low-power microcontrollers (based on the ARM Cortex-M0+ core) for industrial and cost-sensitive applications with low-power requirements.
Learn more: http://www.spansion.com/Products/Analog/Energy-Harvesting-PMICs/Pages/pmic-eh.aspx
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how energy harvesters are becoming more economically feasible for the Internet of Things (IoT). Small amounts of energy can be harvested from vibrations, temperature differences, and radio frequencies using various types of electronic devices such as piezoelectric, MEMS, thermo-electric power generators, and other devices. As improvements in them occur and as the energy requirements of accelerometers, pressure sensors, gas detectors, bio-sensors, and readout circuits fall from microwatts to hundreds of nano-watts, energy harvesters become cheaper and better than are batteries. Improvements in energy harvesting are occurring in the form of higher power per area or higher power per temperature difference and improvements of about five times are expected to occur in the next 5 to 10 years. The market for energy harvesters is expected to reach $2.5 Billion by 2024. In addition to their impact on buildings and the other usual applications for IoT, they will also impact on agriculture, aircraft, and medical implants.
Linda Drabik - Energy harvesting for IoTWithTheBest
As sensors and actuators are deployed in increasing numbers across greater distances, autonomous devices will become more ubiquitous. For systems that require longer life than a primary battery can deliver, Energy Harvesting offers a promising solution.
Energy Harvesting (EH) is the process by which ambient energy is captured from one or more energy sources and stored for later use. It enables autonomous sensors or switches to perpetually run with little to no maintenance, eliminating the need for connection to an electric grid and overcoming limitations of a battery-only power source with limited energy storage.
While the cost of buying and disposing batteries is a significant consideration, it’s the operational drain of maintenance that makes Energy Harvesting a particularly attractive solution for IoT.
In this presentation:
- Energy Harvesting solutions, including those that convert sources such as light, vibration, and heat into electricity (solar cells, piezoelectric devices, and thermoelectric generators).
- Key considerations for an Energy Harvesting terminal, including optimal capacitor size.
Linda Brabik, Founder/Organizer, IoT NY Meetup
Theoretical and Experimental Investigations of a Non-linear Single Degree of ...Rathish Chandra Gatti,Ph.D
There is an increasing need for sensors to be selfpowered
and hence autonomous in order to operate in remote and
inaccessible locations for long periods of time. Amongst the
various ambient sources of energy, mechanical vibration is a viable
wasted source of energy and can be found in rotating equipment
including generators, motors and compressors as well as
structures including bridges. The current research deals with
developing a novel non-linear single degree of freedom
electromagnetic vibration energy harvester using spatial variation
of the magnetic field.
Initially, approximate linear methods using Laplace transforms
and the linear state space methods were considered, where the
magnetic field and hence the coupling coefficient were considered
as constants. The linear methods were used to derive the frequency
response behavior of the system and also its eigenvalues to
determine the approximate resonant frequency range. This was
followed by more accurate non-linear single degree of freedom
electromagnetic energy harvester model simulation considering
the spatial variation of the magnetic field and hence a spatially
varying coupling coefficient. An experiment of the single degreeof-
freedom one-direction electromagnetic vibration energy
harvester (SDOF1D EMVEH) prototype was conducted for a
range of frequencies to obtain the time domain data to validate
against the theoretical data obtained from theoretical time domain
simulation.
The Spansion Energy Harvesting family includes the MB39C811, an ultra-low-power buck PMIC with dual input that enables efficient harvesting from both solar and vibration energy; and the MB39C831, an ultra-low-voltage boost PMIC for solar or thermal. The Spansion Energy Harvesting family of devices works seamlessly with Spansion FM0+ microcontrollers (MCUs), ultra-low-power microcontrollers (based on the ARM Cortex-M0+ core) for industrial and cost-sensitive applications with low-power requirements.
Learn more: http://www.spansion.com/Products/Analog/Energy-Harvesting-PMICs/Pages/pmic-eh.aspx
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how energy harvesters are becoming more economically feasible for the Internet of Things (IoT). Small amounts of energy can be harvested from vibrations, temperature differences, and radio frequencies using various types of electronic devices such as piezoelectric, MEMS, thermo-electric power generators, and other devices. As improvements in them occur and as the energy requirements of accelerometers, pressure sensors, gas detectors, bio-sensors, and readout circuits fall from microwatts to hundreds of nano-watts, energy harvesters become cheaper and better than are batteries. Improvements in energy harvesting are occurring in the form of higher power per area or higher power per temperature difference and improvements of about five times are expected to occur in the next 5 to 10 years. The market for energy harvesters is expected to reach $2.5 Billion by 2024. In addition to their impact on buildings and the other usual applications for IoT, they will also impact on agriculture, aircraft, and medical implants.
Linda Drabik - Energy harvesting for IoTWithTheBest
As sensors and actuators are deployed in increasing numbers across greater distances, autonomous devices will become more ubiquitous. For systems that require longer life than a primary battery can deliver, Energy Harvesting offers a promising solution.
Energy Harvesting (EH) is the process by which ambient energy is captured from one or more energy sources and stored for later use. It enables autonomous sensors or switches to perpetually run with little to no maintenance, eliminating the need for connection to an electric grid and overcoming limitations of a battery-only power source with limited energy storage.
While the cost of buying and disposing batteries is a significant consideration, it’s the operational drain of maintenance that makes Energy Harvesting a particularly attractive solution for IoT.
In this presentation:
- Energy Harvesting solutions, including those that convert sources such as light, vibration, and heat into electricity (solar cells, piezoelectric devices, and thermoelectric generators).
- Key considerations for an Energy Harvesting terminal, including optimal capacitor size.
Linda Brabik, Founder/Organizer, IoT NY Meetup
Theoretical and Experimental Investigations of a Non-linear Single Degree of ...Rathish Chandra Gatti,Ph.D
There is an increasing need for sensors to be selfpowered
and hence autonomous in order to operate in remote and
inaccessible locations for long periods of time. Amongst the
various ambient sources of energy, mechanical vibration is a viable
wasted source of energy and can be found in rotating equipment
including generators, motors and compressors as well as
structures including bridges. The current research deals with
developing a novel non-linear single degree of freedom
electromagnetic vibration energy harvester using spatial variation
of the magnetic field.
Initially, approximate linear methods using Laplace transforms
and the linear state space methods were considered, where the
magnetic field and hence the coupling coefficient were considered
as constants. The linear methods were used to derive the frequency
response behavior of the system and also its eigenvalues to
determine the approximate resonant frequency range. This was
followed by more accurate non-linear single degree of freedom
electromagnetic energy harvester model simulation considering
the spatial variation of the magnetic field and hence a spatially
varying coupling coefficient. An experiment of the single degreeof-
freedom one-direction electromagnetic vibration energy
harvester (SDOF1D EMVEH) prototype was conducted for a
range of frequencies to obtain the time domain data to validate
against the theoretical data obtained from theoretical time domain
simulation.
Designing Energy Harvesting Solar Powered SensorsDan Wright, MBA
Presented 19 Nov 2014 at Energy Harvesting and Storage USA in Santa Clara California
http://www.idtechex.com/events/presentations/designing-energy-harvesting-solar-powered-sensors-005321.asp
The slides for a presentation on Energy harvesting and the state off the art designs currently taking advantage of the energy around us.
Energy harvesting (also known as power harvesting or energy scavenging) is the process by which energy is derived from external sources (e.g.solar power, thermal energy, wind energy, salinity gradients, and kinetic energy), captured, and stored for small, wireless autonomous devices, like those used in wearable electronics and wireless sensor networks.
Credits: A thanks go out to Johan Pedersen for introducing me to the subject a great workshop and use of some of his slides.
Harvesting Energy for the Internet of ThingsAmala Putrevu
Harvesting energy for the Internet of Things is the primary challenge that engineers of today face. Through this presentation we bring to you two models of sensors that use piezoelectric energy harvesting to generate the required power.
Self-generating devices can truly make the Internet of Things a reality.
MicroGrid and Energy Storage System COMPLETE DETAILS NEW PPT Abin Baby
A microgrid is a localized grouping of electricity generation, energy storage, and loads that normally operates connected to a traditional centralized grid (macrogrid). This single point of common coupling with the macrogrid can be disconnected. The microgrid can then function autonomously. Generation and loads in a microgrid are usually interconnected at low voltage. From the point of view of the grid operator, a connected microgrid can be controlled as if it were one entity.
Microgrid generation resources can include fuel cells, wind, solar, or other energy sources. The multiple dispersed generation sources and ability to isolate the microgrid from a larger network would provide highly reliable electric power. Produced heat from generation sources such as micro turbines could be used for local process heating or space heating, allowing flexible trade off between the needs for heat and electric power.
a survey of energy harvesting sources for io t deviceIJAEMSJORNAL
Environmental Energy is an alternative energy for wireless devices. A Survey of Energy Harvesting Sources for IoT Device is proposed. This paper identifies the sources of energy harvesting, methods and power density of each technique. Many reassert have carried to extract energy from environment. The IoT and M2M are connected through internet or local area network and these devices come with batteries. The maintenance and charging of batteries becomes tedious due to thousands of device are connected. The concept of Energy harvesting gives the solution for powering IoT, M2M, Wireless nodes etc. The process of extracting energy from the surrounding environment is termed as energy harvesting and derived from windmill and water wheel, thermal, mechanical, solar.
Designing Energy Harvesting Solar Powered SensorsDan Wright, MBA
Presented 19 Nov 2014 at Energy Harvesting and Storage USA in Santa Clara California
http://www.idtechex.com/events/presentations/designing-energy-harvesting-solar-powered-sensors-005321.asp
The slides for a presentation on Energy harvesting and the state off the art designs currently taking advantage of the energy around us.
Energy harvesting (also known as power harvesting or energy scavenging) is the process by which energy is derived from external sources (e.g.solar power, thermal energy, wind energy, salinity gradients, and kinetic energy), captured, and stored for small, wireless autonomous devices, like those used in wearable electronics and wireless sensor networks.
Credits: A thanks go out to Johan Pedersen for introducing me to the subject a great workshop and use of some of his slides.
Harvesting Energy for the Internet of ThingsAmala Putrevu
Harvesting energy for the Internet of Things is the primary challenge that engineers of today face. Through this presentation we bring to you two models of sensors that use piezoelectric energy harvesting to generate the required power.
Self-generating devices can truly make the Internet of Things a reality.
MicroGrid and Energy Storage System COMPLETE DETAILS NEW PPT Abin Baby
A microgrid is a localized grouping of electricity generation, energy storage, and loads that normally operates connected to a traditional centralized grid (macrogrid). This single point of common coupling with the macrogrid can be disconnected. The microgrid can then function autonomously. Generation and loads in a microgrid are usually interconnected at low voltage. From the point of view of the grid operator, a connected microgrid can be controlled as if it were one entity.
Microgrid generation resources can include fuel cells, wind, solar, or other energy sources. The multiple dispersed generation sources and ability to isolate the microgrid from a larger network would provide highly reliable electric power. Produced heat from generation sources such as micro turbines could be used for local process heating or space heating, allowing flexible trade off between the needs for heat and electric power.
a survey of energy harvesting sources for io t deviceIJAEMSJORNAL
Environmental Energy is an alternative energy for wireless devices. A Survey of Energy Harvesting Sources for IoT Device is proposed. This paper identifies the sources of energy harvesting, methods and power density of each technique. Many reassert have carried to extract energy from environment. The IoT and M2M are connected through internet or local area network and these devices come with batteries. The maintenance and charging of batteries becomes tedious due to thousands of device are connected. The concept of Energy harvesting gives the solution for powering IoT, M2M, Wireless nodes etc. The process of extracting energy from the surrounding environment is termed as energy harvesting and derived from windmill and water wheel, thermal, mechanical, solar.
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Energy Harvesting for Autonomously-Powered Sensor Networks
1. Los Alamos National Laboratory
Energy Harvesting for Autonomously-
Powered Sensor Networks
Scott Ouellette, Ph.D.
R&D Engineer
Advanced Engineering Analysis Group
Los Alamos National Laboratory
Los Alamos, New Mexico
A systems-level paradigm for energy harvesting to
power the connected world
LA-UR-16-28210
Operated by Los Alamos National Security, LLC for the U.S. Department of Energy's NNSA
2. Los Alamos National Laboratory
Motivation – Internet of Things (IoT) for the Connected
World
• Many definitions for IoT depending on perspective: applications,
technological, benefits, etc.
• In general, the Internet of Things is the process by which environmental or
operational data is networked and processed to become actionable
information.
• Examples of IoT are:
• Sensors for microbial awareness in cities
• Connected automobiles / autonomous driving
• Smart buildings: adaptive lighting and air conditioning
• Structural Health Monitoring (SHM)
Ambient Energy System Data Information
Energy Harvesting
IoT
3. Los Alamos National Laboratory
Uses of IoT in Business/Industry
Information and Analysis Automation and Control
1. Tracking Behavior
• Inventory and supply-chain
management
2. Enhanced Situational Awareness
• Damage detection in composite
structures using Acoustic
Wavenumber Spectroscopy
3. Sensor-Driven Decision Analytics
• Condition-based aircraft
maintenance vs. time-based
maintenance
1. Process Optimization
• Continuous, precise adjustments
in manufacturing processes
2. Optimized Resource Consumption
• Intelligent energy grid to match
consumption demand / prevent
power black-outs
3. Complex Autonomous Systems
• Adaptive automobile cruise
control and collision avoidance
systems
M. Chui, M. Löffler, and R. Roberts, “The Internet of Things | McKinsey & Company.”
[Online]. Available: http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-
of-things#0. [Accessed: 21-Oct-2016].
4. Los Alamos National Laboratory
Example of IoT Project Dataflow
“IoT streaming analytics, data production and workflow services added to Azure,” The Fire
Hose, 29-Oct-2014.
Event Producers Collection Ingestor Transformation
Long-term
Storage
Presentation
and Action
5. Los Alamos National Laboratory
Projected Growth of Deployed Sensors
Cerasis_IT, “The IOT Supply Chain Benefits Coming Clearer,” Transportation Management
Company | Cerasis, 14-Jul-2015. [Online]. Available: http://cerasis.com/2015/07/14/iot-
supply-chain/. [Accessed: 24-Oct-2016].
50 billion
6. Los Alamos National Laboratory
Motivation for Energy Harvesting Approach
• Advances in semiconductor manufacturing technology have drastically outpaced
battery storage capacity
• Power consumption of CMOS integrated circuits are also continuing to decrease
• As such, energy harvesting as a means of powering microprocessors continues to
become more viable
G. Park, T. Rosing, M. Todd, C. Farrar, and W. Hodgkiss, “Energy Harvesting for Structural
Health Monitoring Sensor Networks,” J. Infrastruct. Syst., vol. 14, no. 1, pp. 64–79, 2008.
7. Los Alamos National Laboratory
Purpose of Energy Harvesting Paradigm
• Desire to reduce / eliminate costs associated with conventional battery replacement
and chemical waste
• Enabling technology for IoT and SHM sensor networks
• Ultimate goal is to provide autonomous power to sensor network for time scales on
the order of the lifetime of the host structure
H. Boukabache, C. Escriba, and J.-Y. Fourniols, “Toward Smart Aerospace Structures:
Design of a Piezoelectric Sensor and Its Analog Interface for Flaw Detection,” Sensors, vol.
14, no. 11, pp. 20543–20561, Oct. 2014.
8. Los Alamos National Laboratory
An Analogy of the Energy Harvesting Approach
• The human body is a mixed, wired and wireless, network of sensor performing
continuous measurements (sensing) which are transmitted to the brain
(communication) and converted to diagnostic information (local computing)
• The body is nourished with food, which is then converted (transduced) to metabolic
energy
• Digestive process (conditioning) requires a small amount of energy, but is overall
highly efficient
• Excess energy is converted to fat (storage) which could be used when access to
nourishment becomes sparse (management)
9. Los Alamos National Laboratory
Internet of Things
Conventional Powering Approach for IoT Networks
Power Source
Battery or Mains
Power
Central
Computing Server
and Storage
Database
Sensor Node
Sensor Node Sensor Node
Sensor Node
Battery or Mains
Power
Battery or Mains
Power
End Users
10. Los Alamos National Laboratory
Systematic Energy Harvesting Paradigm for
Autonomously-Powered Sensor Networks
Solar
Vibration
Electrochemical
Thermal
Radio Frequency
AC
DC
EnergyDensity
AC-DC
Converter
Sufficient
Power?
No
Yes
Energy
Buffer
DC-DC
Converter
Voltage
Regulator
Power
Management
Super Capacitor
OR
Rechargeable
Battery
Energy Source Power Conditioning & Management
S. A. Ouellette, “Energy Harvesting Paradigms for Autonomously-Powered Sensor
Networks,” UNIVERSITY OF CALIFORNIA, SAN DIEGO, 2015.
11. Los Alamos National Laboratory
Energy Harvesting at LANL
• Development of a multi-source
energy harvesting system for
structural health monitoring of wind
turbine blades
• Transduction schemes studied:
• Solar / Photovoltaic
• Vibration
• Thermal-Electric Generation
• A multi-source energy combination
circuit was prototyped
C. P. Carlson, A. D. Schlichting, S. Ouellette, K. Farinholt, and G. Park, “Energy Harvesting
to Power Sensing Hardware Onboard Wind Turbine Blade,” in Structural Dynamics and
Renewable Energy, Volume 1, T. Proulx, Ed. Springer New York, 2011, pp. 291–304.
12. Los Alamos National Laboratory
Energy Harvesting at LANL
S. G. Taylor et al., “A mobile-agent-based wireless sensing network for structural monitoring
applications,” Meas. Sci. Technol., vol. 20, no. 4, p. 045201, 2009.
• Multi-source energy combination circuit was tested as a power supply on
prototype mobile wireless interrogation device (WID 2.0)
• Custom electronic devices have been developed (WID 3.0 / WiDAQ) for
application-specific health monitoring of wind turbine blades
14. Los Alamos National Laboratory
Problems that need solutions for successful
deployment of IoT systems
• Network security and data privacy
• Computers are bad at keeping
secrets
• Interoperability of hardware /
devices
• Too many communication
protocols / standards, no
unification
• Complexity of hardware and
networking
• Energy / Powering devices
• Need replacement for batteries
15. Los Alamos National Laboratory
Problems that need solutions for successful
deployment of IoT systems
• Network security and data privacy
• Computers are bad at keeping
secrets
• Interoperability of hardware /
devices
• Too many communication
protocols / standards, no
unification
• Complexity of hardware and
networking
• Energy / Powering devices
• Need replacement for batteries
16. Los Alamos National Laboratory
Problems that need solutions for successful
deployment of EH-enabled IoT systems
• Energy storage
• Improvements to rechargeable battery energy density
• Improvements to number of super-capacitor recharge cycles and thermal
resilience
• Reduce Transmission Power Consumption
• New protocols for low-power data transmission
• Improvements to network design protocols
• Power Management Circuit Design and Efficiency
• Reduce consumption overhead of circuitry used for combining and
managing power storage and usage within sensor nodes
17. Los Alamos National Laboratory
LANL collaborators on Energy Harvesting
Technologies
• Prof. Gyuhae Park – Chonnam National University
• Dr. Kevin Farinholt – Luna Innovations Incorporated
• Prof. Steve Anton – Tennessee Technological University
• Dr. Scott Ouellette – Los Alamos National Laboratory
Kevin FarinholtSteve Anton Gyuhae ParkScott Ouellette
18. Los Alamos National Laboratory
Acknowledgements
• National Science Foundation
• Korea Global Research and Development Centers (GRDC)
• Los Alamos National Laboratory Engineering Institute
• University of California, San Diego
• Professor Gyuhae Park, Professor Reon Kang, Professor Jung-Ryul
Lee