To address the growing global problem of lithium-ion battery-related fires, researchers at Drexel University have developed a benchtop ultrasonic diagnostic tool that can quickly and cost-effectively detect internal defects, providing a new layer of safety assurance for batteries in electric vehicles, smartphones, and other devices.
The technology uses a scanning acoustic microscope to transmit low-energy sound waves through commercial pouch cells. As the waves travel through different materials inside the battery, their speed changes, allowing researchers to infer structural and mechanical properties from these variations. The team states in their paper: "Differences in sound-wave interactions with the sample reveal a variety of structural features."
Compared to traditional X-ray inspection, ultrasonic technology offers clear advantages: X-ray imaging is slow, expensive, and provides limited information, while ultrasonic detection can real-time identify critical risks such as gas pockets, dry regions, internal cracks, and component misalignment. Gas detection is especially vital—dry zones can cause short circuits, leading to overheating or thermal runaway.
Currently, battery manufacturers rely primarily on visual inspection, limited sampling, and X-ray for quality validation. However, with surging demand for electric vehicles and consumer electronics, battery production has skyrocketed. According to consumer affairs data, the average person now uses 3–4 battery-powered devices daily—double the number from five years ago. This trend has heightened the risk of defective batteries reaching the market.
Lead researcher and assistant professor Dr. Wes Chang said: "While the vast majority of lithium-ion batteries perform reliably, electric vehicles use thousands of cells, and millions of vehicles are produced annually—making defective cells inevitable." Ultrasonic inspection offers a new solution to this challenge.
Beyond manufacturing, the technology can also assist research labs in developing next-generation battery chemistries. Dr. Chang's team collaborated with lithium-metal battery startup SES AI, validating the tool's effectiveness in real-world R&D settings. Engineers used real-time feedback to rapidly optimize battery designs, demonstrating its potential in innovative applications.
The team is now refining detection algorithms to improve sensitivity to micro-scale defects. The technology is expected to be commercialized within the next two years, delivering a more efficient safety solution across the battery supply chain.
