Lithium-Ion Battery State of Charge (SoC) Estimation with Non-Electrical parameter using Uniform Fiber Bragg Grating (FBG)

Conventional SoC estimation methods mainly rely on electrical parameters such as the current and voltage of the battery. However, recent studies have shown that the non-electrical parameters such as strain and temperature that have non-linear relationships with the battery SoC can be adopted for SoC estimation. In this work, the use of non-electrical parameters for SoC estimation using Deep Neural Network (DNN) was proposed. Fiber Bragg Grating (FBG) sensors are employed for the simultaneous measurement of strain and temperature of the battery. Besides, Pseudohigh-Resolution interrogation (PHRI) method is adopted for demodulating the output spectra to improve the detection accuracy of the small wavelength signals from the FBG sensors. Our findings have shown a great improvement in the FBG signal quality based on a high up-sampling rate, k in the spectral processing using PHRI. This has a significant impact on the performance of the SoC estimation using DNN. In the comparison with SoC estimation based on electrical parameters, the proposed model based on non-electrical parameters has a better estimation performance.

Automated summary

This study proposes a new method for estimating battery State of Charge (SoC) using Fiber Bragg Grating sensors to measure strain and temperature of the battery, along with Deep Neural Networks. Applying Pseudohigh-Resolution Interrogation improves signal detection accuracy and estimation performance. The use of non-electrical parameters shows better estimation performance compared to traditional electrical parameter-based methods.