Research on Machine Learning Detailed by a Researcher at China University of Min ing and Technology (Thermal Runaway Warning of Lithium Battery Based on Electron ic Nose and Machine Learning Algorithms)

中国矿业大学研究员详细介绍的机器学习研究(基于电子ic鼻和机器学习算法的锂电池热失控报警)

Robotics & Machine Learning Daily News2024，Issue(Nov.25) ：7-8.

来源：

NETL
NSTL

Research on Machine Learning Detailed by a Researcher at China University of Min ing and Technology (Thermal Runaway Warning of Lithium Battery Based on Electron ic Nose and Machine Learning Algorithms)

中国矿业大学研究员详细介绍的机器学习研究(基于电子ic鼻和机器学习算法的锂电池热失控报警)

扫码查看

摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑新闻-关于人工智能的详细数据已经呈现。根据来自中华人民共和国徐州，NewsRx记者，研究称，“特征气体检测”是预测锂电池热失控程度的有效方法。在这份工作中，采用了由三个商用MOS传感器组成的传感器阵列来区分三个MOS传感器目标气体CO、H2和两者的混合物，它们是热处理过程中释放的特征气体锂B atteries失控"。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Data detailed on artificial intelligence have bee n presented. According to news reporting fromXuzhou, People’s Republic of China , by NewsRx journalists, research stated, “Characteristic gas detectioncan be a n efficient way to predict the degree of thermal runaway of a lithium battery. I n this work, asensor array consisting of three commercial MOS sensors was emplo yed to discriminate between threetarget gases, CO, H2 and a mixture of the two, which are characteristic gases released during the thermalrunaway of lithium b atteries.”

Key words

China University of Mining and Technolog y/Xuzhou/People’s Republic of China/Asia/Algorithms/Cyborgs/Emerging Techn ologies/Machine Learning

引用本文复制引用

出版年

2024

Robotics & Machine Learning Daily News

ISSN：

段落导航