Session: 18: Student poster competition

Paper Number: 108351

108351 - The Correlation Between Ultrasound Speed and the State of Health of a Li-Ion Prismatic Cell 

Electric vehicles (EVs) are nowadays becoming the dominant transportation tools. The energy storage of EVs relies on Li-ion batteries. With repeated charge and discharge, the batteries will degrade in terms of capacity retention. When the capacity retention decreases to 70%~80%, the batteries need to be replaced. However, it is hard to determine the current capacity retention of a battery onsite because a battery in service does usually not experience full charges or discharges. Therefore, it is expected to have a rapid testing technique for the online state of health (SOH) estimation. Moreover, retired batteries may have a second life usage for energy storage facilities, and rapid testing of massive retired batteries in terms of SOH is required. The SOH of a battery can definitely be determined if the battery experiences a full charge-discharge cycle  with a battery testing system (BTS), but it is time-consuming. Ultrasonic testing techniques are promising due to their efficient, portable, and non-destructive features, and pioneering research on the ultrasonic state of charge (SOC) estimation has been done in the literature. While the industry is seeking ultrasonic SOH estimation techniques based on data-driven methods, these techniques are unreliable and expensive without knowing the underlying physics. This work experimentally confirmed the correlation between ultrasound speed and SOH at first. Then, the correlation is explained by microscopic simulation and macroscopic theory of ultrasound propagation in porous media. Finally, the explanation is verified with several orthogonal experiments. The explanation brings insights into the application scope of ultrasonic SOH estimation techniques and the physical model used for the explanation may improve the data-driven methods in the future.

Presenting Author: Shengyuan Zhang Nanyang Technological University

Presenting Author Biography: Zhang Shengyuan obtained his Bachelor's degree in Mechanical Engineering from Beihang University in 2018 and his Master of Philosophy in Mechanical Engineering from The Hong Kong University of Science and Technology in 2020. He joined the NDT group led by Prof. Zheng (David) Fan at Nanyang Technological University as a Ph.D. student in August 2021. His research interest is numerical analysis and machine learning for ultrasonic NDT/SHM or other inverse problems. His current research topic is ultrasonic battery testing.