Online diagnosis and prognosis of electric vehicle battery health by acoustic emission

Efficient monitoring of battery safety and health is crucial to design high-performance and long-life electric vehicles for a sustainable and circular society.

Herein, we propose to use acoustic emission (AE) sensing to monitor battery health online, identify battery failure modes, and predict remaining life.

Currently, AE is widely applied to detect cracks in constructions, but its use in batteries has hitherto been limited by the complexity of AE signals stemming from a multitude of mechano-electro-chemical processes.

The challenge will be tackled by a group of highly interdisciplinary experts from Uppsala University and Volvo Cars, by exploiting analytical characterizations, automation coupled with machine-learning, realistic cell testing, and techno-economic analysis. We aim to develop a reliable AE method to interpret and predict battery aging.

Ultimately, our approach will support fundamental research and assist OEMs to get the best out of their Li-ion batteries at end of life.