Lithium-Ion Battery State-of-Charge Estimator Based on FBG-Based Strain Sensor and Employing Machine Learning
Bruno Rente, Matthias Fabian, Miodrag Vidakovic, Xuan Liu, Xiang Li, Kang Li, Tong Sun, K. T. V. Grattan
Abstract
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.