基于LabVIEW的锂电池实时监测系统及SOC估算研究

基于单片机和LabVIEW平台开发出了一套锂电池的在线实时监测系统,能实现锂电池参数的监测和SOC的估算。选择LM算法,通过编写MATLAB程序建立了以BP神经网络技术为主要应用蓝本的面向锂电池的SOC预测模型,并针对开发出的新模型系统利用已有的样本数据进行不断学习,最终使SOC预测值逐渐逼近实测值。目前本系统可以成功实现对于锂电池运行状态的实施监测与显示,并利用配套的存储设备对相关状态参数进行实时存储。总体而言,本估算模型具有估算精度高、可靠性高、便于实施等突出优点。

An online real-time monitoring system for lithium battery is developed based on single chip computer and LabVIEW platform. It can realize the monitoring of lithium battery parameters and the estimation of SOC. The LM algorithm is selected to build the SOC prediction model for lithium battery based on BP neural network technology by writing MATLAB program, and the new model system is continuously studied by using the existing sample data. Finally, the predicted value of SOC is gradually approaching the measured value. At present, the system can successfully monitor and display the operation state of lithium battery, and use the supporting storage equipment to store the relevant state parameters in real time. In general, this model has the advantages of high accuracy, high reliability, easy to implement and so on.