About This Session
The rapid scale‑up of lithium‑ion battery production for electric vehicles (EVs) and stationary energy storage systems (ESS) has introduced unprecedented challenges in leak detection and quality assurance. Battery packs integrate hundreds of cells, multiple sealing interfaces, cooling circuits, and safety‑critical enclosures, where even micro‑leaks can compromise thermal management, accelerate degradation, or create hazardous conditions. Ensuring hermetic integrity across complex geometries while maintaining production throughput remains a key unresolved issue.
This paper presents a robotic tracer gas sniffing approach for high‑precision leak localization in EV battery packs and energy storage assemblies. The system combines industrial collaborative robotics with high‑sensitivity helium and hydrogen tracer gas detection to overcome the limitations of conventional manual sniffing methods. By enforcing deterministic scan paths, controlled probe speed, and repeatable positioning, robotic sniffing decouples leak detection performance from operator variability and enables consistent inspection of complex battery architectures.
The proposed architecture integrates a multi‑axis robotic platform with tracer gas detectors capable of detecting leak rates down to the 10⁻⁸ atm·cc/s range and sub‑second response times. Robotic motion control allows uniform coverage of battery trays, modules, pack enclosures, cooling manifolds, and sealed electronic housings, including areas with restricted access or high geometric complexity. This deterministic scanning capability is particularly relevant for battery systems where sealing performance must be verified across large surfaces and multiple interfaces.
Beyond detection sensitivity, the system emphasizes data continuity and traceability, critical for battery manufacturing and second‑life repair workflows. Each battery pack or module is uniquely identified through barcode scanning prior to inspection. Leak events are automatically associated with the scanned identifier and spatially mapped onto a digital representation of the component. This enables precise localization of defects at the cell, module, or enclosure level, supporting targeted rework, root‑cause analysis, and quality trend monitoring throughout the battery lifecycle.
Comparative analysis between robotic and manual tracer gas sniffing highlights significant improvements in repeatability, inspection consistency, and downtime reduction. While manual sniffing remains highly operator‑dependent and difficult to standardize at production scale, robotic inspection provides reproducible results aligned with the requirements of high‑volume EV production and safety‑critical energy storage systems. The approach supports both end‑of‑line testing and repair or remanufacturing contexts, where accurate leak localization is essential for cost‑effective recovery of high‑value battery components.
The paper discusses:
Leak mechanisms and sealing challenges specific to EV battery packs and ESS
Robotic tracer gas sniffing system architecture and integration
Measurement repeatability and localization performance
Digital traceability using barcode identification and leak mapping
Application scenarios including pack assembly, module repair, and repurposing for second‑life energy storage
This contribution demonstrates how robotic tracer gas sniffing enables a shift from qualitative, operator‑dependent inspection toward a quantitative, traceable, and automation‑ready leak detection strategy for battery systems. The approach supports higher safety margins, improved manufacturing robustness, and data‑driven quality control for next‑generation electric mobility and energy storage infrastructures.