储能科学与技术 ›› 2024, Vol. 13 ›› Issue (10): 3669-3671.doi: 10.19799/j.cnki.2095-4239.2024.0783

• 储能测试与评价 • 上一篇    下一篇

基于声纹与AI分析技术的化学电池极片缺陷识别

李淼1(), 周凤颖1, 崔惠珊2, 方兰2, 李文雅2   

  1. 1.北京工业职业技术学院机电工程学院，北京 100042
    2.北京交通职业技术学院轨道交通系，北京 102200
  • 收稿日期:2024-08-22 修回日期:2024-09-23 出版日期:2024-10-28 发布日期:2024-10-30
  • 通讯作者: 李淼 E-mail:m15810251260@163.com
  • 作者简介:李淼（1979—），女，博士，副教授，研究方向为航天器热控制、传感器与检测技术，E-mail：m15810251260@163.com
  • 基金资助:
    北京工业职业技术学院重点项目(BGY2021KY-02Z)

Research on defect identification of chemical battery electrode based on voiceprint and AI analysis technology

Miao LI1(), Fengying ZHOU1, Huishan CUI2, Lan FANG2, Wenya LI2   

  1. 1.School of Mechatronic Engineering, Beijing Polytechnic College, Beijing 100042, China
    2.Department of Rail Transit, Beijing Jiaotong Vocational Technical College, Beijing 102200, China
  • Received:2024-08-22 Revised:2024-09-23 Online:2024-10-28 Published:2024-10-30
  • Contact: Miao LI E-mail:m15810251260@163.com

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摘要：

随着新能源产业的快速发展，化学电池作为其核心部件，其性能和质量对整体系统的稳定性和安全性至关重要。化学电池极片作为电池的重要组成部分，其制备过程中的缺陷会直接影响电池的电化学性能和安全性。本文对声纹与AI分析技术的化学电池极片缺陷识别方法相关研究进行综述，通过这两种技术的结合，可以提高缺陷检测的准确性和效率，为电池生产过程中的质量控制提供有力支持。

关键词: 声纹技术, 电池, 缺陷

Abstract:

With the rapid development of the new energy industry, chemical batteries, as its core components, are crucial for the stability and safety of the overall system in terms of their performance and quality. As an important component of batteries, the defects in the preparation process of chemical battery electrodes directly affect the electrochemical performance and safety of the battery. This article reviews the research on defect recognition methods for chemical battery electrodes using voiceprint and AI analysis techniques. By combining these two technologies, the accuracy and efficiency of defect detection can be improved, providing strong support for quality control in battery production processes.

Key words: voiceprint technology, battery, defects

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