Design of a visual detection algorithm for condensation inside energy-storage lithium-ion battery packs

In recent years, with the rapid development of the new energy industry, the scale of new energy storage facilities centered around lithium-ion batteries has continued to climb. Additionally, there have been several significant safety incidents in energy storage power plants across the globe in recent years, raising concerns for the industry. It is worth noting that the widely used traditional Battery Management Systems (BMS) can only detect the structural integrity and physical parameter abnormalities of the battery, which has obvious monitoring limitations and cannot detect condensation inside of the battery pack. In this paper, we propose an Edge Aware Instance Segmentation Network (EAIS-Net) based on the visual features of condensation inside of battery packs. Specifically, the proposed EAIS-Net is used to enhance the perception ability of condensation phenomenon in battery images, and its core components is the Edge Perception Module (EPM). EPM is committed to enhancing the blurred edge structure of condensation on the surface of battery cell caused by factors such as light exposure and scattering, highlighting the edge characteristics of condensation. The proposed algorithm can provide early warning for energy storage power plants, and experimental results show that the proposed network is superior to other advanced algorithms.