Power and Voltage Optimization Approach

  • Dirk Spreemann
  • Yiannos Manoli
Part of the Springer Series in Advanced Microelectronics book series (MICROELECTR., volume 35)


The previous chapter presented the theoretical basis for the analysis of electromagnetic vibration transducers. Therein closed form solutions for first order power estimations have been obtained based on results from literature. These expressions consider harmonic excitation at one single frequency. Because many realistic vibration sources have a rich spectral content, it has been shown how the results can be used to identify most energetic frequencies and how first order power estimation can be performed also in case of random vibration sources. A popular parameter optimization approach in the analytical analysis considers the ratio of the electromagnetic to parasitic damping factor. A slightly advanced model with a constrained construction volume condition has shown that this commonly discussed optimization approach is important for estimating the maximum possible output power but has only marginal relevance for the design of electromagnetic vibration transducers. As demonstrated in Chap. 2 it is more reasonable to find the geometrical parameters of magnet and coil which yield the maximum output power instead of optimizing the damping ratio. The analytical model for comprehending this takes the magnetic field on the centre axis of the magnet into account which is still a simplifying assumption since leakage field effects are neglected. Beside this, the results are restricted to specific architectures and cannot generally be applied to all the architectures presented in the literature overview in Chap. 1. For this reason a very fundamental question to be answered is how the optimized geometrical parameters can be obtained for the different coupling architectures and do architectures exist that have inherently a higher output power and output voltage generation capability?


Magnetic Flux Magnetic Flux Leakage Coil Turn Cylindrical Magnet Transduction Factor 
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  1. 9.
    Available in the internet:, state April 2010
  2. 22.
    D. Spreemann, D. Hoffmann, B. Folkmer, Y. Manoli, Numerical optimization approach for resonant electromagnetic vibration transducer designed for random vibration. J. Micromech. Microeng. 10, 104001 (2008)CrossRefGoogle Scholar
  3. 40.
    I. Rechenberg, Evolutionsstrategie – optimierung technischer systeme nach prinzipien der biologischen evolution (Frommann Holzboog, Stuttgart, 1973)Google Scholar
  4. 44.
    J.T. Tanabe, Iron Dominated Electromagnets: Design, Fabrication, Assembly and Measurement (World Scientific, Singapore, 2005). ISBN 981-256327-XCrossRefGoogle Scholar
  5. 45.
    K. Foelsch, Magnetfeld und Induktivität einer zylindrischen Spule, Electrical Engineering (Archiv für Elektrotechnik). 30(3) (1936)Google Scholar
  6. 51.
    M. Bousonville, Optimierung von Lautsprechermagnetsystemen mit dem Finite–Elemente–Verfahren, diploma book, department Engineering sciences, Fachbereich Informationstechnologie und Elektrotechnik, WiesbadenGoogle Scholar
  7. 54.
    M. Rossi, Acoustics and Electroacoustics (Artech House, Norwood, 1988). ISBN ISBN–10 0890062552Google Scholar
  8. 75.
    T. Bäck, Evolutionary Algorithms in Theory and Practice (Oxford University Press, New York UK, 1996). ISBN 0-19-509971-0zbMATHGoogle Scholar
  9. 82.
    X. Gou, Y. Yang, X. Zheng, Analytic expression of magnetic field distribution of rectangular permanent magnets. Appl. Math. Mech. 25(3), 297–306 (2004)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Dirk Spreemann
    • 1
  • Yiannos Manoli
    • 2
  1. 1.Institut für Mikro and InformationstechnikHSG-IMITVillingen-SchwenningenGermany
  2. 2.IMTEKUniversity of FreiburgFreiburgGermany

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