Industry applications of identifying spot laser welded nugget for automatic ESS manufacturing process

Abstract

Abstract Recent advancements in energy storage along with power electronic technology have made battery energy storage systems (ESS) a feasible alternative for current power applications. Battery packs with lithium-ion (Li-ion) pouch cells are the main source of ESS. However, it is a big trouble that improper voltage and impedance of laser welding significantly affect the whole battery module during battery pack manufacturing stages, causing the cell imbalance inside and eventually resulting in a thermal runaway of battery pack and non-durable use. Importantly, the formation of nuggets welded can be classified as good (GD) and not-good (NG) based on the labels after peeling off the flyer of Li-ion pouch cell. Interestingly, it is usually a standard practice in this industry to include substantial numbers of redundant welds to gain confidence in the structural stability of the welded component. Thus, a non-destroyed and low-cost detection for identifying the nuggets is absolutely necessary. An effective methodology is motivated and proposed with three procedures for the identification of laser-welded nuggets. At first, the nuggets are detected and separated from a grayscale image. Image features are extracted to train the nugget images on the advanced detector model constructed to identify the GD and NG nuggets. Second, this research develops five models for achieving this purpose of detector; one is called the nugget model developed in convolution neural network (CNN) technique, and the others use the transfer learning of the most popular pre-trained models. From the comparative studies, it is found that the residual network (ResNet) model more effectively classifies the nuggets with 100% accuracy rate than that of the other listed models. Finally, this research has significant application contributions of battery manufacturing industries to produce highly efficient welded nugget products by overcoming the cost-ineffective problems of manual inspection; thus, it further helps this industry simultaneously reduce productive inspection time and increase the manufacturing efficiency of ESS at a lower cost without human intervention than the past.