Spatial Imaging of Local Degradations in Li-Ion Batteries Using Sparsely Distributed Active Sensors

Area of Science:

• Materials Science
• Electrical Engineering
• Non-destructive Testing

Background:

• Safety concerns persist with lithium-ion batteries (LIBs) in conventional battery management systems (BMSs).
• Unpredictable LIB failures necessitate advanced, noninvasive monitoring techniques.

Purpose of the Study:

• To develop a novel noninvasive imaging method for inspecting LIB health status over large spatial areas.
• To address safety concerns by enabling early detection of localized mechanical degradation.

Main Methods:

• Utilized guided ultrasonic waves (GUWs) generated by an active sensing network with nine piezoelectric transducers on a LiFePO4 (LFP) battery.
• Simulated localized mechanical degradation by attaching weights, causing acoustic mismatch and wave interference.
• Employed continuous wavelet transform (CWT) for a status index and probabilistic reconstruction algorithm (PRA) for imaging.

Main Results:

• Numerical simulations indicated frequency and time domain variations in acoustic signals due to mechanical degradation.
• The method successfully validated on an actual LFP battery, identifying degraded regions.
• Achieved a high contrast-to-noise ratio (CNR) exceeding 19% for suspicious spatial regions.

Conclusions:

• The proposed GUW-based imaging method offers superior performance for LIB integrity inspection compared to existing indices.
• This noninvasive technique shows promise for monitoring specific spatial areas within LIBs, enhancing overall safety.