Lithium-Ion battery State of Charge (SoC) estimation with non-electrical parameter using uniform Fiber Bragg Grating (FBG)
Document Type
Article
Publication Date
8-1-2021
Abstract
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.
Keywords
State-of-Charge (SoC) estimation, Lithium-ion battery, Fiber Bragg Grating sensors, Battery strain monitoring, Deep Neural Network
Divisions
foundation,fsktm,PHYSICS,photonics
Publication Title
Journal of Energy Storage
Volume
40
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS