Ultrasonic interrogation of AGM-VRLA batteries

Nicolas Guillet (1) , Vincent Gau (1) , Angel Kirchev (1)

1 LITEN - Laboratoire d'Innovation pour les Technologies des Energies Nouvelles et les nanomatériaux

This work propose a simple, non-destructive and non-intrusive characterization technique, applied to lead acid batteries for battery monitoring during operation. It consists in installing two piezoelectric transducers on the surface of a battery and studying the transport of ultrasonic acoustic waves through the battery. The acoustic signal transmitted through a commercial 6 V/195 Ah AGM-VRLA battery shows a significant and higly repeatable evolution with the state of charge over cycling, related to physical changes in the battery. From these observations, a light machine learning algorithme was applied to obtain an accurate and direct estimation of the state of charge from the characteristics of the waveform recorded, and could be used for a better management of this type of batteries.

Mots clés: Lead-Acid; Ultrasound Acoustic Interrogation; Machine Learning; SoC Estimation; BMS

Domaines: Acoustique [physics.class-ph]

Nicolas Guillet, Vincent Gau, Angel Kirchev. Ultrasonic interrogation of AGM-VRLA batteries. LABAT'21 - 11th International Conference on Lead-Acid Batteries, The Bulgarian Academy of Sciences, Jun 2021, VIRTUAL event, Bulgaria. ⟨cea-03215022⟩