A modelling approach for laser welding of busbars to lithium-ion prismatic cell terminals to enhance failure prediction

焊接 激光束焊接 母线 材料科学 结构工程 激光器 机械工程 工程类 复合材料 电气工程 光学 物理
作者
Nikhil Kumar,Richard Beaumont,Javier Sanguesa Gomez,Christopher Harris,Iain Masters
出处
期刊:Optics and Laser Technology [Elsevier BV]
卷期号:176: 110989-110989 被引量:7
标识
DOI:10.1016/j.optlastec.2024.110989
摘要

Laser welding is increasingly being used to connect automotive battery packs and weld busbars to prismatic cell terminals due to its ability to weld a wide variety of materials at high speed. The choice of material for the connecting busbars is primarily based on weldability, weight, electrical/thermal conductivity and cost. Aluminum (Al) is increasingly being chosen as busbar material to meet these criteria. This paper investigates laser overlap welding of Al for producing busbar-to-terminal interconnects for prismatic cell assembly. To understand the strength of these laser-welded joints in the early stages of design, the joints must be modelled. In addition, the structural modelling of battery module connections is critical to understanding the impact resistance (crash safety) of electric vehicles. Fusion-based modelling of laser welding is computationally inefficient and difficult to integrate into large structural models e.g., full vehicle crash models. Instead, it is more computationally efficient to generate a structural model of the weld, tuned to experimental test data, which can be easily integrated into larger models. LS-DYNA was employed to model the mechanical behavior of 1 mm to 1 mm AA1050 laser welds. Welding was performed using a wobble head integrated with a 1 kW CW fiber laser system. The simulation is based on laser weld with a rectangular pattern and was developed from experimental data from lap shear, T-peel, cross-tension and torsion tests. This modelling approach was extended to obtain joint strength for laser welding a 1.5 mm 1060 Al busbar to a 4 mm 1060 Al prismatic cell terminal. The predicted load–displacement curves closely matched the test data.
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