Podcasts›Visions: A Machine Vision and Automation Solutions Podcast›BV Seal: GM's AI-Powered Leak Detection for EV Battery PacksLike💬 Be the first to comment →AudioSeptember 15, 2026BV Seal: GM's AI-Powered Leak Detection for EV Battery PacksFrom Visions: A Machine Vision and Automation Solutions Podcast, hosted by Sharon Spielman and Jim TatumGM's BV Seal, which uses gas imaging, collaborative robots, and edge computing to find leaks in EV battery packs.Play episodeSaveOpen the audio file TranscriptTranscript from the show's feedSearch the transcript0:02Well, hello and welcome to Visions, a machine vision and automation solutions podcast. I'm your host, Jim Tatum, Senior Editor of Vision Systems Design, and Visions is an Endeavor business media production from your friends at Vision Systems Design. Here you'll find the latest on everything from end-user machine vision solutions to trends, developments, and perspectives on all things machine vision and imaging. Whether you've been working in the industry for a while, or you're just starting to take a closer look at it, this podcast is designed to grow your knowledge and bring greater focus to your understanding of the imaging and machine vision industry.0:39And now onto our show. Well, hi everyone, and welcome to Visions. I'm Jim Tatum, and today we're going to look at some very interesting work being done in the realm of electric vehicles. To be a little more specific, we're going to get into an important and somewhat expensive component of electric vehicles, that is the batteries. You know, when we talk about electric vehicle batteries, most people focus on range, charging speed, or performance. But behind the scenes, manufacturers are working diligently with something much more fundamental keeping battery packs sealed and leak free, at general motors that challenge led to the development of an in-house automated inspection system called bv seal a technology that can identify and pinpoint battery pack leaks in real time right on the production line, to understand why that's important let's start with the battery itself reducing weight is one of the keys to improving ev range and efficiency.1:39To make battery trays and packs lighter, GM uses advanced high-strength steels, new aluminum alloys, and resistance spot welding during assembly. But there's a trade-off. The rapid heating and cooling involved in resistance spot welding can sometimes create microscopic cracks and become leak paths. Traditionally, those leaks were found only at the end of the manufacturing process. The problem was, an end-of-line test could tell engineers that a leak existed, but it couldn't say exactly where it was or how serious it might be.2:15By the time the defect was discovered, the battery tray or pack was already fully assembled, often leaving scrapping the entire item as the only option left. According to Blair Carlson, future factory program lead and senior technical fellow at GM Research and Development, The project began with a simple manufacturing question. That is, could cracks in resistant spot welds on high-strength steel create leak paths? The engineering team explored several possible solutions.2:47They looked at soap bubble duct tanks, for example. This is one of the oldest and most tried-and-true leak detection methods out there. It's used often in automotive manufacturing. They looked at robotic helium sniffers, which is a much more sensitive and quite a bit more expensive methodology. And they looked at various acoustic-based leak detection methods. All of these methods are effective detection methods, but none of them could reliably identify the precise location of a leak.3:18In fact, even advanced CT scans of leaking welds weren't providing definitive answers. The breakthrough came through when the team shifted its thinking. Instead of trying to image the crack itself, they focused instead on imaging the escaping gas. A technical discovery was that weld cracks and defects are three-dimensional and not practical to image in a high-volume production environment, so the breakthrough came from combining specialized equipment that could image-trace gas with an algorithm capable of distinguishing gas plumes from the surrounding atmosphere.3:57That insight led GM to a carbon dioxide imaging approach. The company selected CO2 because it's relatively inexpensive, widely available, and capable of revealing even very small leaks. While helium is commonly used in leak detection, Carlson said its higher cost and inability to provide precise leak point identification made CO2 the better choice for BB seal. So, how does the system actually work? Well, a battery pack arrives at an inspection station on the line via an autonomous mobile robot.4:33On either side of that station are FANUC-CRX collaborative robots, each equipped with a specialized gas imaging camera. The system also includes an internally developed gas excitation unit, an Advantek GPU-based edge computer, and AI software developed by GM. One of the advantages of the COBOT-based approach is flexibility. Because collaborative robots have built-in safety systems, they can operate without the fixed fencing required by traditional industrial robots.5:09That makes it easier to move large battery packs into and out of the inspection station. As the cameras observe the pack, a sensor activates and highlights any escaping trace gas. The gas imaging cameras capture the resulting plume, and those images are sent to the Edge computer, where an AI model analyzes the flow pattern. Within seconds, the software tracks the gas movement back to its source and pinpoints the exact location of the leak. At that point, a human operator can examine the defect and determine whether it can be repaired or whether the battery pack should be scrapped.5:46Notably, the system doesn't require specialized production lighting. It operates using the standard overhead lighting already present in a factory. While the inspection itself happens at the edge for real-time decision-making, the data is also stored in the cloud, enabling long-term analytics, reporting, and process improvement efforts. So, did it work? Well, according to Carlson, the answer is yes. By detecting defects before battery packs are fully completed, BVSeal is reducing rebuilds, teardowns, and scrap rates.6:19That translates directly into savings on labor, materials, production time, and overall manufacturing costs. Perhaps more importantly, GM designed BVSeal as more than a single-purpose solution. The company sees the technology as a platform that could be applied anywhere SEAL integrity matters. Future applications could easily include engine assembly, powertrain manufacturing, and other production environments where even small leaks can create major quality issues.6:50So what began as a question about microscopic weld cracks has evolved into an AI-powered inspection platform that helps manufacturers identify defects in real time, make faster decisions, and reduce waste before products ever leave the line. Pretty ingenious, we'd say. And that's yet another glimpse into how machine vision, AI, and robotics are shaping quality inspection on the factory floor. Well, that's a wrap for this episode of Visions, produced by Endeavor Business Media, a division of Endeavor B2B.7:27Thanks very much for tuning in. If you enjoyed today's show, be sure to subscribe to the podcast and share this episode with a colleague who would find it helpful. Until our next episode, you can find us at vision-systems.com or on LinkedIn, Facebook, or X for more insights, updates, and breaking news to keep you in the know. Thanks for tuning in. Until next time, stay focused on your visions.
