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 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.

Automated summary

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.