Wave-based battery health monitoring system
Project Description

Lithium-ion batteries are widely used as the power supply for products ranging from portable consumer products to transportation vehicles. However, they are prone to degradation or sudden failures during operation. An online monitoring system is strongly desired to optimize battery lifespan and to ensure its safety by identifying internal changes that lead to degradation and failure.

Elastic waves are sensitive to internal mechanical changes in structures. Therefore, wave-based battery health monitoring would be a promising technique for remaining life prediction and early detection of failure. In this project, wave-based monitoring techniques will be developed. The relationship between wave features/properties and battery conditions will be studied using an experimental approach combined with data analysis.

Supervisor
YE Wenjing
Quota
2
Course type
UROP1100
Applicant's Roles

Students will be working with a graduate student collecting wave signals propagating in batteries during normal cycling and overcharging cases, and performing data analysis using machine learning methods.

Applicant's Learning Objectives

Students will acquire knowledges on battery degradation mechanisms, elastic wave and the state of art health monitoring technique. They will be also gain hand-on experience on conducting wave detection experiments and signal analysis.

Complexity of the project
Easy