Large format battery SoC estimation: An ultrasonic sensing and deep transfer learning predictions for heterogeneity

Accurate state of charge (SoC) estimation is vital for safe and efficient operation of lithium-ion batteries. Methods such as Coulomb counting and open-circuit voltage measurements face challenges related to drift and accuracy, especially in large-format cells with spatial gradients in electric vehicles and grid storage usage. This study investigates ultrasonic sensing as a non-invasive and real-time technique for SoC estimation. It explores the opportunity of sensor placement using machine learning models to identify optimal actuator–receiver paths based on signal quality and pinpoints the maximum accuracy that can be achieved for SoC estimation. Based on experimentally collected ultrasound signals transmitted between four sensors installed on a large format pouch cell, a novel and customised deep learning framework enhanced by convolutional neural networks is developed to process ultrasonic signals through transformation to waveform images and leverage transfer learning from strong pre-trained models. The results demonstrate that combining bidirectional signal transmission with a dynamic deep learning-based strategy for actuator and receiver selection significantly enhances the effectiveness of ultrasonic sensing compared to traditional data analysis and pave the way for a robust and scalable SoC monitoring in large-format battery cells. Furthermore, preliminary pathways towards self-supervision are explored by examining the differentiability of ultrasonic signals with respect to SoC, offering a promising route to reduce reliance on conventional ground truths and enhance the scalability of ultrasound-based SoC estimation. The data and source code will be made available at https://github.com/hfarhaditolie/Ultrasonic-SoC.