基于红外视频识别的锂电池健康状态快速检测

Rapid detection of lithium battery health status based on infrared video recognition

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。

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.