Modeling and experimental investigation of high-potential testing for metal particle detection in lithium-ion battery manufacturing

Metal particle contamination in lithium-ion battery manufacturing presents serious safety hazards, including abnormal self-discharge and potential thermal runaway. Although high-potential testing is widely employed for defect screening, its sensitivity to metal particle detection remains inadequate. This study comprehensively investigates the detection mechanism of high-potential testing for metallic contaminants and develops effective enhancement strategies. To address critical research gaps—particularly the neglected mechanical-electrical coupling during hot-pressing, absence of dynamic breakdown process simulation, and insufficient parameter analysis, we first prepare battery samples with precisely controlled metal particle contaminants to acquire essential material properties. Utilizing these parameters, we establish a three-dimensional finite element model that concurrently couples mechanical pressure effects and dynamic electrical breakdown physics. This model successfully simulates electric field distortion and breakdown behavior near contamination sites during high-potential testing, revealing the core detection principle for metal particles foreign matter. Through systematic parameter studies, we systematically evaluate the influences of applied voltage, mechanical pressure, and defect characteristics (shape/location/size) on detection effectiveness. The experimental results validate the regularities identified through the model-based analysis. Based on these insights, we propose optimized strategies to improve foreign matter detection rates. The developed 3D model, integrating pressure and breakdown dynamics, provides a robust theoretical framework and simulation tool for deep mechanistic understanding of high-potential testing and process optimization. This work offers significant implications for enhancing lithium-ion battery manufacturing quality and safety.