Acoustic Sensing Storage System, data fusion analysis of battery health, and multi-physics field coupling models for early warning system implementation... With the continuous advancement of research and development, monitoring and diagnosing the state of energy storage systems using acoustic sensing is becoming a reality.

Accurately and timely grasping the safety and health status of energy storage systems is a prerequisite for achieving proactive safety warnings in energy storage power stations," explained Dr. Tu Fangfang, Head of the Forward-looking Technology R&D Department at Narada.

Lithium iron phosphate batteries possess complex nonlinear characteristics, and the degradation process exhibits different signal features across multiple physical dimensions. Relying solely on existing monitoring methods such as voltage, current, and temperature makes it difficult to comprehensively perceive and evaluate the battery's operational status.

Narada, in collaboration with State Grid Shandong Electric Power Company, Zhejiang University, and University of Electronic Science and Technology of China, jointly developed the “Acoustic Sensing-based Battery Health Monitoring, Diagnosis, and Risk Warning System”, integrating various cutting-edge technologies, effectively compensating for the shortcomings of traditional monitoring techniques. The research team established an acoustic correlation database by studying the acoustic characteristics of various states during battery and module degradation, and designed a battery health monitoring and diagnosis method based on acoustic sensing.

“In simple terms, it can judge the health of the battery and module by listening to sound,” said Tu Fangfang. “Next, we will complete the manufacturing of the acoustic sensing prototype and design deployment plans for the energy storage system.”

The acoustic sensing monitoring and warning system can detect conditions such as deformation of battery and module shells, characteristic gas generation, electrolyte leakage, and micro-pressure changes, capturing abnormalities in real time.

This system will overcome the technical challenges of passive precise perception of multiple state variables, wireless rapid transmission, and intelligent efficient analysis in lithium battery energy storage systems. It will significantly enhance the safety and stability of power station operations, holding significant importance for the large-scale application of new energy storage power stations.