High Power LED Parametric Modelling Using a Double Polynomial Approximation

Authors

  • Karel Zaplatílek Department of Electrical Engineering, University of Defence, Brno, Czech Republic
  • Jan Leuchter Department of Radar Technology, University of Defence, Brno, Czech Republic

DOI:

https://doi.org/10.3849/aimt.01174

Keywords:

power LED module, optimal polynomial approximation, Euclidean norm of residues, MATLAB&Simulink,, Spice compatible model, Micro-Cap simulator

Abstract

This article describes calculations for an accurate mathematical model of high power LED modules using double optimal polynomial approximation. The model is based on unique tests of various LED types in a thermal chamber, providing a comprehensive list of parametric temperature profiles. This model was then implemented into MATLAB®&Simulink® and Micro-Cap programs as a Spice compatible electronic circuit model, utilising the newly created algorithm. To define an optimal degree of approximation polynomials, Euclidean norm of residues was used. The new described algorithm in this article was verified using real-life data tested at the author’s work site, where the corresponding research takes place. To maximise the test’s efficiency, an automated data collection system was created. This article describes one particular tested LED module whose characteristic was modelled in both the absolute and the normalised form for easy comparison.

References

WINDER, S. Power Supplies for LED Driving. Elsevier, 2008. 235 p.

KHAN, N. M. Understanding LED Illumination. CRC Press, 2014. 225 p.

LENK, R. and LENK, C. Practical Lighting Design with LEDs. IEEE Press, 2011. 235 p.

Ultralow-Power LED-Enabled On-Chip Optical Communication Designed in the III-Nitride and Silicon CMOS Process Integrated Platform. IEEE Design & Test of Computers, 2014, vol. 31, no. 1, p. 36-45.

YUNG, K. C., SUN, B., JIANG, X. Prognostic-based Qualification of High-Power White LEDs Using Lévy Process Approach. Mechanical Systems and Signal Processing, 2016, vol. 82, p. 206-216.

JANCZAK, A. Identification of Nonlinear Systems Using Neural Networks and Polynomial Models. A Block-Oriented Approach. Springer, 2009. 199 p.

COMINETTI, R., FACCHINEI, F., LASSERRE, B. J. Modern Optimization Modelling Techniques. Springer, 2012. 267 p.

ZHENING, L., SIMAI, H., SHUSHONG, Z. Approximation Methods for Polynomial Optimization. Models, Algorithms and Applications. Springer, 2012. 125 p.

BOGUSLAVSKIY, A. J. Dynamic Systems Models. New Methods of Parametric and State Estimation. Springer Basel, 2016. 201 p.

ZAPLATILEK, K. and LEUCHTER, J. Optimal polynomial approximation of photovoltaic panel characteristics using a stochastic approach. Advances of Military Technology, 2013, vol. 8, no. 2, p. 43-51.

ZAPLATÍLEK, Karel and LEUCHTER, Jan. Fuel Cell 3-D Modelling Using a Logarithmic Approximation in MATLAB®&Simulink®. Advances in Military Technology, 2016, vol. 11, no. 1, p. 53-62.

Vötsch industrial technique [on line]. Wien, Austria [cited 2017-01-01]. Available from: <http://www.v-it.com>.

Spectrum Software [on line]. Sunnyvale, CA USA [cited 2017-01-01]. Available from: <http://www.spectrum-soft.com>.

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Published

18-09-2017

Issue

Section

Research Paper

Categories

How to Cite

Zaplatílek, K., & Leuchter, J. (2017). High Power LED Parametric Modelling Using a Double Polynomial Approximation. Advances in Military Technology, 12(1), 49-60. https://doi.org/10.3849/aimt.01174

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