基于LabVIEW的锂电池监测系统及SOC预测研究

摘要

In order to realize the real-time monitoring of battery parameters and the prediction of SOC. In this paper, the battery monitoring system is developed based on single chip and LabVIEW platform. With LM algorithm, build the SOC prediction model based on BP neural network and use sample data to experiment the model. Through continuous learning, the SOC predicted by BP neural network approach the actual SOC gradually. The results show that the system can display the real-time parameters of battery and store data;the prediction model realized the high nonlinear mapping between the input and the output of lithium battery, the model has high precision and is feasible.%为了实现对电池参数的实时监测和SOC的预测，中文以单片机和LabVIEW平台开发出一套电池管理系统。采用LM算法，建立了基于BP神经网络的锂电池SOC预测模型，利用样本数据进行模型实验。通过不断的学习，BP神经网络预测的SOC逐渐逼近实际的SOC。结果表明，该系统能实时显示电池参数，实现数据存储；该预测模型实现了锂电池SOC预测中输入与输出之间的高度非线性映射，预测精度高，具有可行性。