The Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries

Authors

  • Kacper Cierpiak Gdańsk University of Technology, 11/12 Narutowicza Street, 80-233 Gdańsk, Poland
  • Marta Szczerska Gdańsk University of Technology, 11/12 Narutowicza Street, 80-233 Gdańsk, Poland
  • Pawel Wierzba Gdansk University of Technology

DOI:

https://doi.org/10.4302/plp.v15i3.1207

Abstract

Optical fiber sensors using low-coherence interferometry require processing of the output spectrum or interferogram to determine the instantaneous value of the measured quantity, such as temperature, quickly and accurately. Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber sensor of temperature is demonstrated. Using a ZnO-coated sensing interferometer and spectral detection, the sensor is intended for monitoring lithium-ion rechargeable batteries. While the performance of all methods was good, some of them seem to be better suited for this application.

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Author Biographies

Kacper Cierpiak, Gdańsk University of Technology, 11/12 Narutowicza Street, 80-233 Gdańsk, Poland

Department of Metrology and Optoelectronics, Faculty of Electronics, Telecommunications and Informatics,

Marta Szczerska, Gdańsk University of Technology, 11/12 Narutowicza Street, 80-233 Gdańsk, Poland

Faculty of Management and Economics

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Published

2023-09-30

How to Cite

[1]
K. Cierpiak, M. Szczerska, and P. Wierzba, “The Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries”, Photonics Lett. Pol., vol. 15, no. 3, pp. 36–38, Sep. 2023.

Issue

Vol. 15 No. 3 (2023)

Section

Articles

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