Lithium-Ion Battery State-of-Charge Estimator Based on FBG-Based Strain Sensor and Employing Machine Learning

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 ob...

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Bibliographic Details
Published in:IEEE sensors journal 2021-01, Vol.21 (2), p.1453-1460
Main Authors: Rente, Bruno, Fabian, Matthias, Vidakovic, Miodrag, Liu, Xuan, Li, Xiang, Li, Kang, Sun, Tong, Grattan, Kenneth T. V.
Format: Article
Language:English
Subjects:
Algorithms
Bragg gratings
dynamic time warping
Fiber Bragg grating
Lithium
Lithium-ion batteries
Machine learning
Optical communication
Optical fiber sensors
Real time
Rechargeable batteries
Signal monitoring
State of charge
state-of-charge estimation
Strain
strain sensor
Temperature measurement
Temperature sensors
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Summary: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 ~2% accuracy with a ~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.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3016080