使用激光诱导分解光谱学快速识别充电堆塞材料
Lidan Chen1, Tiantian Pan2, Liuye Cao2
1College of Automotive Technology, Zhejiang Technical Institute of Economics, Hangzhou, China.
PloS one
|January 30, 2026
概括
激光诱导分解光谱 (LIBS) 快速识别电动汽车充电插头的铜,红铜和黄铜. 这确保了安全性,可靠性,并支持不断增长的电动汽车行业.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 电气工程 电气工程
背景情况:
- 电动汽车 (EV) 需要可靠的充电基础设施,充电插头是关键组件.
- 传统的铜材料对电动汽车充电插头有局限性;铜是一种合适的替代品.
- 准确的材料识别对于确保充电插头的电性能,安全性和回收价值至关重要.
研究的目的:
- 开发一种快速准确的方法来识别用于电动汽车充电插头的铜,红铜和黄铜.
- 优化激光诱导分解光谱 (LIBS) 参数用于材料分析.
- 评估用于分类这些铜合金的机器学习模型.
主要方法:
- 激光诱导分解光谱 (LIBS) 用于元素和合金分析.
- 使用K-近邻 (KNN),随机森林 (RF) 和卷积神经网络 (CNN) 作为分类模型.
- 应用了特征选择方法,包括知识驱动,连续预测算法 (SPA) 和竞争性适应性重权取样 (CARS).
主要成果:
- LIBS成功地确定了合金中的,并允许进行参数优化.
- 机器学习模型,特别是最佳模型,在区分铜,红铜和黄铜方面实现了100%的准确性.
- 特征提取技术有效地选择了相关的光谱频段以进行准确的分类.
结论:
- LIBS提供了一种快速且高度准确的方法,用于识别电动汽车充电插头中的关键材料.
- 这种技术提高了充电站的安全性和可靠性,为电动汽车行业的可持续发展做出了贡献.
- 开发的基于LIBS的方法支持电动汽车行业的质量控制和回收过程.
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