AI-Empowered Thermal Runaway Testing Solution for Energy Storage Batteries Debuts from SGS
SGS, a leading global certification and testing company, has launched an innovative solution designed to detect and analyze dangerous thermal runaway events in energy storage batteries. The AI-powered automated thermal runaway testing system is set to address fire safety concerns around the rapid global growth of battery energy storage systems (BESS) in commercial, industrial, and residential sectors.
Key Features and Advantages
The system leverages artificial intelligence models to continuously monitor and analyze battery thermal behavior, enabling real-time detection of anomalies that could lead to thermal runaway events.
Key features of the system include:
- AI integration for automation and precision: By automating the complex thermal runaway test procedures, the system eliminates manual testing errors, resulting in faster, more consistent, and reproducible results.
- Thermal imaging and predictive analytics: AI-driven thermal imaging and machine learning allow non-contact temperature monitoring and early anomaly detection, improving preventive safety measures.
- Scalability and adaptability: The AI models can be retrained for different battery chemistries and configurations, making the system useful for various types of energy storage batteries.
Benefits and Accreditation
The benefits of this system are numerous. It enhances safety by early and accurate identification of conditions that might lead to battery fires or explosions. Accelerated testing cycles reduce the time and cost of battery safety validation during manufacturing and development phases. Improved battery reliability and lifespan are also achieved through continuous monitoring and predictive maintenance insights.
The system has been launched by reputable organizations such as SGS, implying it meets stringent industrial standards for battery safety testing. Additionally, researchers from established institutes in China have validated the technology, indicating solid research backing.
Operation and Collaboration
The thermal runaway testing system is in operation at SGS's Chongqing Renewable & Advanced Energy Laboratory, a facility that has obtained the ISO/IEC 17025 accreditation in 2022, making it the first lab in China accredited to offer testing services according to UL9540A standards. The system's automation reduces personnel exposure to hazardous conditions, ensuring laboratory safety.
The system aligns with ANSI/CAN/UL 9540A:2025 standard to assess thermal runaway fire propagation in BESS. It was developed in collaboration with the Chongqing Energy College (CEC) in China, a full-time non-governmental college approved by the Chongqing Municipal Government and filed by the Ministry of Education.
Walter Zheng, from SGS, stated that the system addresses the need for internationally accredited safety testing, faster certification turnaround, and improved test transparency.
In summary, the first AI-powered automated thermal runaway testing system provides a cutting-edge, reliable, and scalable solution for energy storage battery safety, combining AI, thermal imaging, and predictive analytics, with accreditation credibility from established industry leaders like SGS.
The AI-powered automated thermal runaway testing system, launched by SGS, leverages artificial intelligence to continuously monitor and analyze battery thermal behavior, improving safety and reducing the cost and time of battery safety validation during manufacturing and development phases. This system, which is in operation at SGS's Chongqing Renewable & Advanced Energy Laboratory, utilizes AI integration for automation and precision, AI-driven thermal imaging for early anomaly detection, and can be scalable for different battery chemistries and configurations, making it a versatile solution for energy storage battery safety.
