基于红外视频识别的锂电池健康状态快速检测
基于红外视频识别的锂电池健康状态快速检测
作者单位:
东华理工大学机械与电子工程学院 南昌 330013
基金项目:
江西省科技合作专项重点项目（20212BDH80008）、国家自然科学基金(12165001)、科技部常规性科技援外项目（KY201702002）、江西省重点研发计划项目（20181BBE58006）资助
Rapid detection of lithium battery health status based on infrared video recognition
Author:
Wang ZhichengWang Zhicheng
School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
在期刊界中查找
在百度中查找
在本站中查找
Wang Zhe
School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
在期刊界中查找
在百度中查找
在本站中查找
Wang Zewang
School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
在期刊界中查找
在百度中查找
在本站中查找
Zhao Jie
School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
在期刊界中查找
在百度中查找
在本站中查找
Shu Dengfeng
School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
在期刊界中查找
在百度中查找
在本站中查找
Affiliation:
School of Mechanical and Electronic Engineering，East China University of Technology, Nanchang 330013, China
摘要 | | 访问统计 | | | || 文章评论摘要:
针对退役动力电池梯次利用过程中对电池健康状态快速检测的需求，本文以软包磷酸铁锂电池为研究对象，提出基于红外热成像的锂电池健康状态快速检测方法。通过改变电池充电和放电电流倍率，研究不同老化程度的电池在放电过程中的温度变化情况，采集放电过程中的红外热成像视频，建立电池健康状态与红外热成像特征的对应关系，以此作为电池健康状态检测的健康因子；构建基于SlowFast-LSTM深度学习网络模型的改进型视频识别算法，对于电池健康状态0~40%、40%~50%、50%~60%、60%~70%、70%~80%、80%~100%这6种类别的识别率达到80.78%，单次电池检测时间3 min，实现电池健康状态的快速检测。
Abstract:
To meet the demand for rapid detection of battery health status in the process of retired power battery recycling, this paper takes soft pack lithium iron phosphate batteries as the research object and proposes a rapid detection method of lithium battery health status based on infrared thermal imaging. By changing the battery charging and discharging current multipliers, the temperature changes of batteries with different aging degrees during the discharge process are studied, and the infrared thermographic video during the discharge process is collected to establish the correspondence between the battery health state and the infrared thermographic features, which is used as the health factor for battery health state detection; an improved video recognition algorithm based on SlowFast-LSTM deep learning network model is constructed for battery health state detection. The improved video recognition algorithm achieves an average recognition rate of 80.78% for the six categories of battery health state 0~40%, 40%~50%, 50%~60%, 60%~70%, 70%~80% and 80%~100%, and a single battery detection time of 3 minutes, which enables fast detection of battery health state.
引用本文汪志成,王哲,王泽旺,赵杰,束登峰.基于红外视频识别的锂电池健康状态快速检测[J].电子测量技术,2023,46(13):185-192
复制
分享 文章指标 点击次数:1053 下载次数: 819 HTML阅读次数: 0 文章二维码相关知识
基于红外视频识别的锂电池健康状态快速检测
一种锂电池健康状态快速检测装置及方法2024.pdf专利下载
» 锂电池健康状态快速检测解决方案：SBT 1000 Series
笔记本电池状态和电池检测方法详解【钜大锂电】
及云智能申请车辆电池健康状态检测专利，实现对磷酸铁锂电池健康状态的有效检测
一种锂电池健康状态快速检测方法及系统2024.pdf专利下载
阻抗法分析监测锂电的荷电状态，健康状态和内部温度（阻抗知识不扎实更要看）
基于新健康因子的锂电池健康状态估计和剩余寿命预测
电池荷电量及电池健康状态的检测、诊断方法.pdf
电池荷电量及电池健康状态的检测、诊断方法
网址: 基于红外视频识别的锂电池健康状态快速检测 https://m.trfsz.com/newsview1388036.html
