High-performance fibre-based and silicon photonics-based optical frequency domain reflectometry for battery sensing
High-performance fibre-based and silicon photonics-based optical frequency domain reflectometry for battery sensing
Optical fibre sensors have attracted increasing attention in battery sensing. This thesis is dedicated to the development of two outstanding distributed optical fibre sensors based on optical frequency domain reflectometry (OFDR) for batteries. The research commences with a comprehensive analysis of the OFDR system, achieved through the establishment of a function-module-based theoretical model. This model enables modular analysis, single-device verification, and systematic comparison, providing a solid theoretical foundation for the research. Especially, an innovative equal frequency resampling method is proposed in this thesis to effectively compensate for the nonlinear frequency tuning noise, significantly enhancing the performance of the OFDR system. A fibre-based OFDR, optimizing the operating parameters and implementing the equal frequency resampling method, achieves remarkable experimental results. In this thesis, an unprecedented spatial resolution of 12.1 µm is obtained, setting a world record. This spatial resolution represents the ultimate spatial resolution attainable by a fibre-based OFDR up to this point in time. Moreover, this fibre-based OFDR is employed to conduct distributed temperature and strain sensing with exceptional performance. In this thesis, the distributed temperature measurements showcase a temperature uncertainty of 0.13 °C over an 8 m measurement range and 0.15 °C over a 102 m measurement range. Similarly, distributed strain measurements exhibit a strain accuracy of 0.51 µε at a 4 m fibre end and 1.86 µε at a 104 m fibre end, with an effective sensing spatial resolution of 5 mm. Additionally, a strain accuracy of 19.31 µε is achieved with an effective sensing spatial resolution of 0.5 mm. Furthermore, a real on-chip OFDR based on Rayleigh backscattering is proposed and demonstrated for the first time. In this thesis, an integrated OFDR utilizing silicon photonics technology is fabricated on a Silicon-on-Insulator (SOI) platform, replacing the fibre-based main interferometer module and auxiliary interferometer module with the on-chip photonic circuits. A distributed refractive index measurement confirms the potential and effectiveness of the silicon photonics-based OFDR, achieving an impressive experimental spatial resolution of 8.28 µm which is reaching its theoretical level. Both the fibre-based OFDR and the silicon photonics-based OFDR demonstrate exceptional sensing performance, fulfilling the requirements for battery sensing. The outcomes of this thesis contribute to the advancement of battery sensing and pave the way for the development of smart batteries.
University of Southampton
Han, Gaoce
122baece-e361-472a-8256-513369c62a78
April 2024
Han, Gaoce
122baece-e361-472a-8256-513369c62a78
Yan, Jize
786dc090-843b-435d-adbe-1d35e8fc5828
Han, Gaoce
(2024)
High-performance fibre-based and silicon photonics-based optical frequency domain reflectometry for battery sensing.
University of Southampton, Doctoral Thesis, 136pp.
Record type:
Thesis
(Doctoral)
Abstract
Optical fibre sensors have attracted increasing attention in battery sensing. This thesis is dedicated to the development of two outstanding distributed optical fibre sensors based on optical frequency domain reflectometry (OFDR) for batteries. The research commences with a comprehensive analysis of the OFDR system, achieved through the establishment of a function-module-based theoretical model. This model enables modular analysis, single-device verification, and systematic comparison, providing a solid theoretical foundation for the research. Especially, an innovative equal frequency resampling method is proposed in this thesis to effectively compensate for the nonlinear frequency tuning noise, significantly enhancing the performance of the OFDR system. A fibre-based OFDR, optimizing the operating parameters and implementing the equal frequency resampling method, achieves remarkable experimental results. In this thesis, an unprecedented spatial resolution of 12.1 µm is obtained, setting a world record. This spatial resolution represents the ultimate spatial resolution attainable by a fibre-based OFDR up to this point in time. Moreover, this fibre-based OFDR is employed to conduct distributed temperature and strain sensing with exceptional performance. In this thesis, the distributed temperature measurements showcase a temperature uncertainty of 0.13 °C over an 8 m measurement range and 0.15 °C over a 102 m measurement range. Similarly, distributed strain measurements exhibit a strain accuracy of 0.51 µε at a 4 m fibre end and 1.86 µε at a 104 m fibre end, with an effective sensing spatial resolution of 5 mm. Additionally, a strain accuracy of 19.31 µε is achieved with an effective sensing spatial resolution of 0.5 mm. Furthermore, a real on-chip OFDR based on Rayleigh backscattering is proposed and demonstrated for the first time. In this thesis, an integrated OFDR utilizing silicon photonics technology is fabricated on a Silicon-on-Insulator (SOI) platform, replacing the fibre-based main interferometer module and auxiliary interferometer module with the on-chip photonic circuits. A distributed refractive index measurement confirms the potential and effectiveness of the silicon photonics-based OFDR, achieving an impressive experimental spatial resolution of 8.28 µm which is reaching its theoretical level. Both the fibre-based OFDR and the silicon photonics-based OFDR demonstrate exceptional sensing performance, fulfilling the requirements for battery sensing. The outcomes of this thesis contribute to the advancement of battery sensing and pave the way for the development of smart batteries.
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High-performance Fibre-based and Silicon Photonics-based Optical Frequency Domain Reflectometry for Battery Sensing
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Published date: April 2024
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Local EPrints ID: 489528
URI: http://eprints.soton.ac.uk/id/eprint/489528
PURE UUID: 9c128cd9-e677-4e10-a38e-0d9539d41ec1
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Date deposited: 26 Apr 2024 16:40
Last modified: 17 Aug 2024 01:57
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Gaoce Han
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