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

Rente, B., Fabian, M., Vidakovic, M. , Liu, X., Li, X., Li, K., Sun, T. ORCID: 0000-0003-3861-8933 & Grattan, K. T. V. ORCID: 0000-0003-2250-3832 (2021). Lithium-Ion Battery State-of-Charge Estimator Based on FBG-Based Strain Sensor and Employing Machine Learning. IEEE Sensors Journal, 21(2), pp. 1453-1460. 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.

Publication Type: Article
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Fiber Bragg Grating, strain sensor, state-of-charge estimation, dynamic time warping
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Science & Technology > Engineering
SWORD Depositor:
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