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

• 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
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.3016080

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.