期刊
IEEE SENSORS JOURNAL
卷 21, 期 2, 页码 1453-1460出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.3016080
关键词
Temperature sensors; Temperature measurement; Strain; Optical fiber sensors; Lithium-ion batteries; Fiber Bragg grating; strain sensor; state-of-charge estimation; dynamic time warping
资金
- Royal Academy of Engineering
- EPSRC [EP/R030243/1, EP/L001063/1, EP/P004636/1] Funding Source: UKRI
This study introduces a real-time state-of-charge (SOC) estimator based on Fibre Bragg Grating (FBG) sensor signals, achieving approximately 2% accuracy through a dynamic time-warping algorithm and supervised learning approach. Successfully applied to a battery-operated train, the system demonstrates potential for enhancing safety in the growing electric vehicle industry.
A real-time state-of-charge (SOC) estimator based on the signals obtained from a Fibre Bragg Grating (FBG)-based sensor system is reported. The estimator has used a dynamic time-warping algorithm to determine the best fit, employing previously obtained experimental data. The strain data used were obtained from the optical signal monitored, providing the input to a supervised learning algorithm. The results achieved show a good match with those from conventional techniques, achieving a similar to 2% accuracy with a similar to 1% SOC resolution. The system has been successfully applied to a 'proof of concept' demonstrator, using a battery-operated train, illustrating as a result the way in which the real-time SOC estimator could be employed to enhance safety in the growing electrical vehicle industry.
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