The document discusses a methodical design process for vibration energy harvesting systems used at Fraunhofer LBF. The process begins with estimating the available vibration energy and its spectral characteristics. Then, the energy harvesting system and its key components are designed based on the available vibration energy, electrical energy demand, and mechanical loads. Finally, operational and durability tests are conducted in the laboratory. The design process aims to iteratively validate the system performance through testing at various stages from concept to deployment.

1. © Fraunhofer
Dirk Mayer, Thilo Bein
12: METHODICAL DESIGN OF VIBRATION
ENERGY HARVESTING SYSTEMS

2. © Fraunhofer
Methodical design of vibration energy harvesting
systems
The design of practical vibration energy harvesting systems is not always straight forward. However, following a
methodical design process can ensure a final system design that is up to the task. This lecture will discuss a
methodical design process for vibration energy harvesting systems used at the Fraunhofer LBF. First the available
vibration energy and its spectral characteristics are estimated; then the energy harvesting system and its key
components (energy-harvester, -storage and -management) are designed taking into account the vibration energy
available, the electrical energy demand of the task and mechanical and/or other loads. Finally operational and
durability tests are conducted in the laboratory.

3. © Fraunhofer
METHODICAL DESIGN OF VIBRATION
ENERGY HARVESTING SYSTEMS
 Introduction
 Industrial Internet of Things
 Self powered sensor applications
 Design challenges for self powered systems and vibration energy harvesting
 Development methods
 Waterfall model
 Agile development / iterative and incremental development
 An framework for the methodic development of self powered systems
 Proof of feasibility
 System simulation
 Hardware-in-the-Loop testing
 Field testing

4. © Fraunhofer
Miniaturization as an Enabler for the Internet of Things
 Small, low-power systems
 MEMS Sensors
 Low cost solutions
 Wireless communication
 Gather high amount of data
from integrated sensors
 Extraction of information by data
fusion
 Optimization of processes by
control and monitoring networks

5. © Fraunhofer
Daniel Wellers: Is this the future of the Internet of Things?

6. © Fraunhofer
Scenarios for Potential Added Value
Application Scenario:
Operation of commercial vehicles under
varying conditions
IOT Application:
Sensor integration in structural parts
 Acquisition of loads in real operation
 Analysis of damages and fatigue
Benefits
 Optimization of maintenance schedules
 Optimization of designs
 Optimization of operation

7. © Fraunhofer
Requirements for Industrial IoT Applications
Application Requirements
 Harsh environmental conditions
 High availability and reliabiltiy
 High lifetime of industrial equipment
 High amount of sensor data

8. © Fraunhofer
Applications for self powered sensors
https://pixabay.com/de/users/skeeze-272447/
 Large structures – wireless sensing saves efforts for wiring
 Moving parts and mobile systems – wires not possible or no energy
supply present
 Long term operation – no maintenance (battery change) required

9. © Fraunhofer
Commercial Vibration Energy Harvesting System
(Example)
Can all vibrations be converted into electricity?
Yes, and no. Theoretically, all vibrations can be converted into
electricity. However, there are certain types of vibrations the are
preferred when the intent is to power a sensor or monitoring system.
They have the following characteristics:
• A steady vibration (i.e. not random shocks)
• A dominating frequency
(http://revibeenergy.com/vibrationenergyharvesting/)

10. © Fraunhofer
Experiences from real world applications
Machinery
 Compressor working in steady state
 Constant frequency and amplitude of vibrations
 Resonant energy harvester can be tuned to the dominant frequency
 Scavenged energy can be predicted, if the operation schedule of the compressor is
known

11. © Fraunhofer
Experiences from real world applications
Bridges
 “First, the excitation provided by traffic is
nonstationary and will be subject to
substantial transients”
 “Second, the amplitude of vibration varies at
different locations of the bridge and depends
on type of abutment, proximity to supports,
modal number of a specific frequency, and
other factors.“
Sazonov, E., Haodong Li, D. Curry, und P. Pillay. „Self-Powered Sensors for
Monitoring of Highway Bridges“. Sensors Journal, IEEE 9, Nr. 11
(November 2009): 1422–29. https://doi.org/10.1109/JSEN.2009.2019333.

12. © Fraunhofer
Experiences from real world applications
Vibrations of railway freightcars
0 50 100 150 200 250 300
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
BZ: gerade, 90km/h
Frequenz [Hz]
Amplitude[m/s²]
unbeladen
beladen
 Unsteady operation
 Vibrations influenced by track quality
 Structural characteristics influenced by
loading conditions

13. © Fraunhofer
Experiences from real world applications
Vehicle vibration sources
Type of vibrations in a vehicle
All nonstationary
All stationary
Filtered noise
White noise
other
 Online database: http://realvibrations.nipslab.org/

14. © Fraunhofer
Categories of Energy Harvesting Sources
 Energy Sources can be classified
 Controllability:
 Energy can be generated
when desired
 Predictability:
 Time and amount of
energy can be predicted

15. © Fraunhofer
Design of wireless self-powered smart sensor systems
Energy generation/
storage
System size and
mass
Computational
effort
Energy consumption
On board analysis/
data reduction
Raw data
transmission
 Highly integrated system
 Interaction of components
System
performance
System
robustness

16. © Fraunhofer
Example: Oil Pump Monitoring in Ships
 Routing power and signal cables on ships is frequently very difficult due the
presence of thick compartment walls, limited free space for cable trays and conduits, and
watertight compartment requirements.
 Condition monitoring of an oil pump (target frequency 7800 cps (130 Hz).
 Uncertainties
 expected duty cycle or operating characteristics of the machinery providing
the source of power
 Level of vibration and frequency variations
 Experiences:
 Tuning of the harvester difficult, variation of rotational speed lowers generated
energy
 Devices have to be hardened against the environment
 Collaborative development and in-field technology evaluation can
accelerate development – the complexity of highly distributed, remote
technology development and deployment has been addressed by the significant up-
front laboratory testing, data analysis, documented field procedures, site surveys,
and trained staff members.
Discenzo, Fred M., K. A. Loparo, H. Cassar, und D. Chung. „Machinery condition
monitoring using wireless self-powered sensor nodes“. In Proc. 24th Int. Modal
Analysis Conf.(St. Louis, MO, Jan.–Feb.), 2006.

17. © Fraunhofer
Design challenges for self-powered systems
 Variability of energy source
 Vibration may be non stationary
 Vibration is influenced by mounting position
 Complex system with design conflicts
 Robustness
 Limited energy supply
 Minimum of function required to gain benefits (e.g. valuable
information from sensor data)
 High effort for field tests in mobile applications, infrastructure,…
 Need for a methodic development process
 Simultaneous engineering process necessary

18. © Fraunhofer
Development methods
Waterfall model (V-model)
Component
Implementation
System Integration
Validation /
Testing
Component
Design
System
Requirements
 Stage-gate type process from requirements collection to system
realisation
 Simultaneous engineering by division of complex system development
into development of single components

19. © Fraunhofer
Development methods:
Incremental and iterative development – Agile methods
 Iterative development of the system as-a-whole
 Enabler for cooperation in cross-functional teams
 Frontloading principle: evaluate the system performance as early as possible
 Continuous delivery of incrementally improved systems
Require-
ments
Design
Development
Testing

20. © Fraunhofer
Test Driven Development
Collect Requirements
System Design
Write Code
Write Test
Run Test
Improve
Collect Requirements
Write Test
System Design
Write Code
Run Test
Improve
Waterfall development Test driven development
Derive test cases from requirementsDerive system design from requirements

21. © Fraunhofer
Comparison
Waterfall
Incremental & Iterative
Development
Single-shot, stage gate Iterative process
„Right first time“ „Wrong first time“
Suitable for incremental
innovations (e.g. improved version
of known mechatronic product)
Suitable for radical innovations,
uncertainties – the scientific
method
1 system test (final) Testing of incremental builds
Popular in software development – challenge: transfer to mechatronic
systems

22. © Fraunhofer
Development Process for a Self Powered System
 Iterative validation of the system performance with respect to defined
requirements
 Integration of real data from the early design stage
 Continuous test program for the energy harvester in interaction with the
rest of the system
 Iterative development
 Incremental implementation from concept to in-service deployment

23. © Fraunhofer
Main criterion for assessment of a self-powered system
 Condition for autonomous operation:
 The energy harvester has to deliver more energy than the system
consumes over time
 Design relevant tests for the evaluation during the development process
� 𝑃𝑃𝐸𝐸𝐸𝐸 𝑑𝑑𝑑𝑑 ≥ � 𝑃𝑃𝑆𝑆𝑆𝑆𝑆𝑆 𝑑𝑑𝑑𝑑

24. © Fraunhofer
Development process for an Energy Harvesting System
Test
environment
Desktop Computer Laboratory Laboratory In-Service
System
Components
Concept
validation
System design
validation
Component
design
validation (I)
Component
design
validation (II)
System
validation
Excitation Data model Simulated Simulated (rt) Shaker Real
Energy
Harvester
Analytical
model
Simulated Simulated (rt) Hardware Hardware
Energy storage
Analytical
model
Simulated Simulated (rt) Hardware Hardware
Sensor node
Analytical
model/ data
Simulated Hardware Hardware Hardware
Input data
Requirements Prototype

25. © Fraunhofer
Collection of input data
 Analyse the vibrations (a.k.a. the energy source) exhaustively
 Long term testing /adequate simulation / literature data
 Classify the vibrations with respect to the operation modes
200
1200
2200
3200
4200
0
50
100
0
1
2
3
Radius
Zeitabschnitte: 20 s, unbeladen, Gesamt: 1125 BZ
Geschwindigkeit
AnzahlBZ[log10]
200
1200
2200
3200
4200
0
50
100
0
1
2
3
Radius
Zeitabschnitte: 20 s, beladen, Gesamt: 1596 BZ
Geschwindigkeit
AnzahlBZ[log10]

26. © Fraunhofer
Generation of a representative source profile
 Compose a time series from measurement data
 Consider both representative states and the order of the states

27. © Fraunhofer
Estimation of generated power with simple
approximations
 Apply simple analytical models of the EH system to the input vibrations
 Example: Power dissipated in a damped oscillator
𝐸𝐸 = �𝐷𝐷 ̇𝑧𝑧 2
𝑡𝑡 𝑑𝑑𝑡𝑡Dissipated energy over time
𝑚𝑚 ̈𝑧𝑧 + 𝐷𝐷 ̇𝑧𝑧 + 𝑘𝑘𝑘𝑘 = −𝑚𝑚 ̈𝑦𝑦Equation of motion
Dissipated power
for harmonic vibration 𝑃𝑃 = 𝐷𝐷 ̇𝑧𝑧 2
 The damping is representing both mechanical
and electrical dissipation
 Best case:
𝐷𝐷
𝑚𝑚
= 𝛾𝛾 = 𝛾𝛾𝑒𝑒𝑒𝑒 + 𝛾𝛾𝑚𝑚𝑚𝑚𝑚𝑚𝑚
𝛾𝛾𝑒𝑒𝑒𝑒 ≫ 𝛾𝛾𝑚𝑚𝑚𝑚𝑚𝑚𝑚
𝑃𝑃 = 𝐷𝐷 �
0
∞
𝜔𝜔2 ̂𝑆𝑆𝑧𝑧 𝜔𝜔 𝑑𝑑𝑑𝑑

28. © Fraunhofer
Estimation of generated power with simple
approximations
 Approximation: White noise excitation
 Acceleration power
spectral density
 Dissipated power
 Arbtitrary excitation spectrum
 Dissipated power
 Upper boundary for generated power
 estimation of needed inertial mass
̂𝑆𝑆𝑎𝑎 𝜔𝜔 = ̂𝑆𝑆𝑎𝑎 = const.
𝑃𝑃 =
1
4
𝑚𝑚 ̂𝑆𝑆𝑎𝑎
𝑃𝑃 ≤
1
4
𝑚𝑚 ̂𝑆𝑆𝑎𝑎,𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝
̂𝑆𝑆𝑎𝑎
𝑧𝑧(𝑗𝑗 𝑗𝑗)
̂𝑆𝑆𝑎𝑎,𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝
𝑧𝑧(𝑗𝑗 𝑗𝑗)
𝜔𝜔0

29. © Fraunhofer
Estimation of power consumption
P P
 Definition of hardware platform (uC, sensors,
transmitters)
 Duty cycle definition
 Large enough to meet sensor requirements
 Small enough to save energy
 Estimation of consumed power
 Data sheets or literature
 Bench test with electronics protoype
Sleep Mode Active Mode
 Duty cycle:
 Averaged power consumption:
𝐷𝐷 =
𝑇𝑇𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎
𝑇𝑇𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 + 𝑇𝑇𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎
𝑃𝑃𝑆𝑆𝑆𝑆𝑆𝑆 = (1 − 𝐷𝐷)𝑃𝑃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 + 𝐷𝐷𝑇𝑇𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎

30. © Fraunhofer
Estimation of power consumption by literature data
Computing platform
Microntroller, sleep mode 1 uA@3V
Microcontroller, active 2 mA @3V
Microcontroller, communication 7 mA @3V
Sensors
MEMS accelerometer 2 uA @3V
GPS acquisition/tracking 6mA/20mA
@3.3V
Wireless Communication
LoRa 20mA @5V
ZigBee 8mA @5V
WiFi 160mA@5V

31. © Fraunhofer
Estimation of necessary energy storage
 For unsteady source profiles, consumption and
generation can
mismatch:
 Energy storage necessary to smooth the balance:
𝑃𝑃𝐸𝐸𝐸𝐸
𝑃𝑃𝑆𝑆𝑆𝑆𝑆𝑆
𝑃𝑃𝑆𝑆𝑆𝑆𝑆𝑆 > 𝑃𝑃𝐸𝐸𝐸𝐸
𝐸𝐸𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ≥ max � 𝑃𝑃𝑆𝑆𝑆𝑆𝑆𝑆 − 𝑃𝑃𝐸𝐸𝐸𝐸 𝑑𝑑𝑑𝑑

32. © Fraunhofer
Component Design: Energy Harvester
 Simple example
 Mechanical oscillator, tuned to a resonance of the structure
 Bending beam
 Tip mass
 Integration of electromechanical transducers (e.g. piezo
elements)
 Assessment of mechanical properties by FE analyses

33. © Fraunhofer
Derivation of state space
formulation
Fitting of analytical
model
Derivation of models for a system level simulation
Finite Element model
of the coupled system
(~10000 DOF)
Model Order
Reduction Methods
̇𝒙𝒙 𝑡𝑡 = 𝐴𝐴𝒙𝒙 𝑡𝑡 + 𝐵𝐵𝐵𝐵 𝑡𝑡
𝑦𝑦 𝑡𝑡 = 𝐶𝐶𝒙𝒙 𝑡𝑡 + 𝐷𝐷𝐷𝐷(𝑡𝑡)
 Reduced order models
required for time-domain
integration schemes
Finite element models usually not
suitable for integration into
efficient system level simulation
MOR techniques for
electromechanically coupled
systems still non-standard

34. © Fraunhofer
System level simulation
 Time domain simulation including (simplified) models of all components
 Validation of energy balance and component designs
M O R
vibration data ̇𝑥𝑥 𝑡𝑡
Source admittance 𝑌𝑌(𝑗𝑗 𝑗𝑗)
Reduced order
models of energy
harvester
Nonlinearities due to
AC-DC conversion
Time variance of
energy management
(step up converter)
Time variance
of load
due to different states
of the sensor node

35. © Fraunhofer
Incremental implementation of the system
 Evaluation of the system performance (energy balance) in case only one
component (e.g. electronics or harvester) is implemented in hardware

36. © Fraunhofer
Testing of real electronics with simulated environment
Rocket Science: Hardware in the Loop (HITL)
 Developed for testing avionics of a
(manned) spacecraft
 IBM Gemini Mission Verification
Simulation (1967)
“Will the actual Gemini digital computer,
together with its operational program,
indeed function adequately within the
operational interface environment
expected during actual Gemini missions?”
Validation of the system without need
to realize the complete hardware
http://en.wikipedia.org

37. © Fraunhofer
Hardware-In-The-Loop testing: Signal level
 Testing of electronic control
hardware
 Real time simulation of the
environment
 Actuators
 Sensors
 …
 Transmission of signals
(information) between
simulation and hardware
 Broad range of applications:
 Automotive electronics
 Aviation
 Robotics
 …
Main benefits of HiL testing:
 Less effort for expensive in-service tests
 Fault injection – simulation of
critical situations in the lab
 Repeatable test conditions
 Minimum hardware effort (protoypes)
 Automation of testing

38. © Fraunhofer
Power level HiL testing
 Transmission of electrical power at the
interface between RT-simulation and
DUT
 Realisation of the interface with a
controlled voltage (current) source
 Applications
 Testing of motor electronics –
simulation of the electromechanical
feedback of the drivetrain
 Test of wind turbine power
electronics – simulation of the grid
feedback
Real
HW
Simulation

39. © Fraunhofer
Hardware In The Loop Simulation
Real-time simulation:
energy harvesting and
energy management
Electrical power interface:
Controlled voltage source
simulates storage capacitor
Current probe for feed back
of load current
 Power hardware-in-the loop
Hardware:
Sensor Node
 Reproducible system tests without need for hardware implementation

40. © Fraunhofer
Power Hardware in the Loop Simulation of EH system

41. © Fraunhofer
Hardware-in-the-Loop-Testing
Application to Self-Powered-Sensors
 Hardware implementation
 Smart Sensor by
prototype hardware
(Libelium Wasp Mote)
 Real time analysis of
consumed electrical
power
 Real-Time emulation
(dSpace real time system)
 Energy Harvesting system
 Energy storage
 Input signals

42. © Fraunhofer
Laboratory test
 Hardware implementation of all system
components
 Simulation of acceleration by
electrodynamic shaker
 Validation of the simulation models
 System reliability and durability assessment

43. © Fraunhofer
In-service testing for final validation of the system
 Instrumentation of the host structure with the self-powered sensor system
 Parallel instrumentation
 Accelerometers, GPS – validation of input data and harvester
 Further sensors (e.g. temperature) – validation of sensor system
Temperature data acquired

44. © Fraunhofer
Summary
 Self powered systems impose challenges to the designer
 Design conflicts
 Uncertainties
 Evaluation at system level from the beginning accelerates the
development process
 Derive tests from requirements before designing systems
 Start with simple validation and iterate
 Advanced simulation and testing methods enable realistic validation
in the laboratory

45. © Fraunhofer
References and Further Readings
 http://revibeenergy.com/vibrationenergyharvesting/
 Sazonov, E., Haodong Li, D. Curry, und P. Pillay. „Self-Powered Sensors for Monitoring of Highway Bridges“. Sensors Journal, IEEE 9,
Nr. 11 (November 2009): 1422–29. https://doi.org/10.1109/JSEN.2009.2019333.
 Koch, M., Kaal, W. Investigation of the operational vibration characteristics of a freight car to design energy harvesting sensors. In:
Kempten University: 31th Danubia-Adria Symposium, (2014), Germany: Kempten.
 Neri, Igor, Flavio Travasso, Riccardo Mincigrucci, Helios Vocca, Francesco Orfei, und Luca Gammaitoni. „A Real Vibration Database
for Kinetic Energy Harvesting Application“. Journal of Intelligent Material Systems and Structures, 6. Mai 2012.
https://doi.org/10.1177/1045389X12444488.
 Rantz, Robert, und Shad Roundy. „Characterization of Real-world Vibration Sources and Application to Nonlinear Vibration Energy
Harvesters“. Energy Harvesting and Systems 4, Nr. 2 (2017): 67–76. https://doi.org/10.1515/ehs-2016-0021.
 Discenzo, Fred M., K. A. Loparo, H. Cassar, und D. Chung. „Machinery condition monitoring using wireless self-powered sensor
nodes“. In Proc. 24th Int. Modal Analysis Conf.(St. Louis, MO, Jan.–Feb.), 2006.
 Isermann, R. „Modeling and design methodology for mechatronic systems“. IEEE/ASME Transactions on Mechatronics 1, Nr. 1 (03/
1996): 16–28. https://doi.org/10.1109/3516.491406.
 Larman, C., V. R. Basili. „Iterative and incremental developments. a brief history“. Computer 36, Nr. 6 (June 2003): 47–56.
https://doi.org/10.1109/MC.2003.1204375.
 Koch, Michael, Matthias Kurch, und Dirk Mayer. „On a Methodical Design Approach for Train Self-Powered Hot Box Detectors“. In
Proceedings of the First International Conference on Railway Technology: Research, Development and Maintenance, 2012.
https://doi.org/10.4203/ccp.98.90.
 Jawad, Haider Mahmood; Nordin, Rosdiadee; Gharghan, Sadik Kamel; Jawad, Aqeel Mahmood; Ismail, Mahamod (2017): Energy-
Efficient Wireless Sensor Networks for Precision Agriculture. A Review. In: Sensors 17 (8). DOI: 10.3390/s17081781.
 „Calculating Battery Life in IoT Applications | Farnell element14“. 2. Jan. 2018. http://de.farnell.com/calculating-battery-life-in-iot-
applications.

46. © Fraunhofer
References and Further Readings
 Martinez, B., M. Montón, I. Vilajosana, und J. D. Prades. „The Power of Models: Modeling Power Consumption for IoT Devices“. IEEE
Sensors Journal 15, Nr. 10 (Oktober 2015): 5777–89. https://doi.org/10.1109/JSEN.2015.2445094.
 GPS low power receiver GNS601uLP Datasheet, http://www.actesolutions.se/media/6129/gns601ulp_datasheet-1.pdf
 Mitcheson, P. D., E. M. Yeatman, G. K. Rao, A. S. Holmes, und T. C. Green. „Energy Harvesting From Human and Machine Motion for
Wireless Electronic Devices“. Proceedings of the IEEE 96, Nr. 9 (September 2008): 1457–86.
https://doi.org/10.1109/JPROC.2008.927494.
 Ananthakrishnan, Akshay, Inna Kozinsky, und Igor Bargatin. „Limits to inertial vibration power harvesting: power-spectral-density
approach and its applications“. arXiv preprint arXiv:1410.4734, 2014.
 Mitcheson, P. D., T. C. Green, E. M. Yeatman, und A. S. Holmes. „Architectures for vibration-driven micropower generators“. Journal
of Microelectromechanical Systems 13, Nr. 3 (June 2004): 429–40. https://doi.org/10.1109/JMEMS.2004.830151.
 Seah, Winston, und Yen Kheng Tan. Sustainable Wireless Sensor Networks. Rijeka, Croatia: InTech, 2010.
https://www.intechopen.com/books/sustainable-wireless-sensor-networks
 Kansal, Aman, Jason Hsu, Sadaf Zahedi, und Mani B. Srivastava. „Power management in energy harvesting sensor networks“. ACM
Trans. on Embedded Computing Sys. 6 (December 2007). https://www.microsoft.com/en-us/research/publication/power-management-
in-energy-harvesting-sensor-networks/.