A major challenge for energy storage batteries is thermal runaway, which can cause uncontrollable increases in temperature and pressure within a battery cell resulting in explosions or fires. To address these fire safety concerns as the usage of battery energy storage systems in commercial, industrial, and residential sectors increases, SGS, a test, inspection, and certification company, launched an AI-powered automated thermal runaway testing system for energy storage batteries.
The automated system provides faster certification turnaround and improved test transparency, providing customers with faster access to market, lower costs, and higher testing precision, SGS said.
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Developed in collaboration with the Chongqing Energy College, the AI-powered solution, claimed as the industry’s first for energy storage batteries, leverages deep learning to customize data processing of temperature, voltage, gas emissions, and combustion collected during tests. Proprietary algorithms automatically adjust testing parameters, collect data in real time, and use computer vision based on deep learning models such as Yolo, TensorFlow, ResNet, and VGO to detect smoke and fire occurrences. They also minimize manual intervention and enhance accuracy.
Key features of the thermal runaway test system include full automation whereby centralized software controls the testing equipment and automates initialization, parameter adjustments, and data collection; and smart detection and recording so that advanced computer vision accurately identifies and logs smoke and fire events for reliable data. The test system also provides enhanced safety with automation by reducing personnel exposure to hazardous conditions.
The automated test system is now in operation at the Chongqing Renewable & Advanced Energy Laboratory. The system aligns with the ANSI/CAN/UL 9540A:2025 standard to assess thermal runaway fire propagation and provide data on risks during thermal runaway.
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