基于PLSR-CNN的锂电池热失控特征气体拉曼检测方法  

Analysis of thermal runaway gases in lithium batteries based on Raman PLSR-CNN

作　　者：张伟[1] 杨旭 黄鑫 张海军 Zhang Wei;Yang Xu;Huang Xin;Zhang Haijun(College of Transportation Science and Engineering,Civil Aviation University of China,Tianjin 300300,China;College of Safety Science and Engineering,Civil Aviation University of China,Tianjin 300300,China;The Key Laboratory of Civil Aviation Thermal Disaster Prevention and Emergency Response,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学交通科学与工程学院,天津300300 [2]中国民航大学安全科学与工程学院,天津300300 [3]中国民航大学民航热灾害防控与应急重点实验室,天津300300

出　　处：《消防科学与技术》2025年第4期525-531,共7页Fire Science and Technology

基　　金：国家自然科学基金民航联合研究基金(U2133201);中央高校基本科研业务费专项(3122020048);2022天津市研究生科研创新项目(2022SKY154)。

摘　　要：针对锂离子电池热失控早期快速监测预警,提出了一种热失控特征气体拉曼检测识别算法,该方法创新性开发了最小二乘-卷积神经网络(PLSR-CNN)的方法 ,从低信噪比的拉曼气体信号中准确提取本征信号,并结合偏最小二乘回归技术(PLSR)进行定量分析,极大提升了拉曼气体检测技术检测预警效果。结果表明,该方法在定性分析方面准确率高达99.8%,在定量分析方面准确率高达96.4%。因此,该方法在检测领域方面具有良好的应用前景,可为进一步提高热失控气体识别准确率,完善气体识别模型提供理论和技术支持。Against the early and rapid monitoring and warning of thermal runaway of lithium-ion batteries,this article proposes a fast Raman detection and recognition algorithm for thermal runaway characteristic gases.The method innovatively develops a least squares convolutional neural network(PLSR-CNN)algorithm,which accurately extracts intrinsic signals from low signal-to-noise ratio Raman gas signals and combines partial least squares regression(PLSR)technology for quantitative analysis,greatly improving the detection and warning effect of Raman gas detection technology.The results show that the accuracy of this method in qualitative analysis is as high as 99.8%,and in quantitative analysis it is as high as 96.4%.Therefore,this method has good application prospects in the field of detection,providing theoretical and technical support for further improving the accuracy of thermal runaway gas identification and improving gas identification models.

关 键 词：热失控气体 拉曼 CNN PLSR 

分 类 号：X932[环境科学与工程—安全科学] TM912.9[电气工程—电力电子与电力传动]