基于产线大数据的锂离子电池一致性动态特性分选方法

As lithium-ion battery production rapidly expands, manufacturers urgently require high-precision and high-efficiency sorting methods to improve the consistency, lifespan, safety, and energy density of battery packs. Traditional techniques that rely on capacity and internal resistance address static consistency postgrouping but fail to ensure dynamic consistency within the same group. Addressing this, our study focuses on the dynamic characteristics of the charge-discharge voltage curve to propose a next-generation sorting approach. We extract key dynamic features from the voltage curve during the battery capacity grading process, utilizing big data from the production line, and employ K-means clustering for battery sorting. Furthermore, we assess battery performance consistency by analyzing metrics from the recharging stage postcapacity grading, devising an evaluation method based on the standard deviation of these metrics. Our proposed sorting method demonstrates a 15.65% improvement in the overall performance consistency of batteries compared to conventional approaches.