矿用电机车动力锂电池的SOC估算研究  

Research on SOC Estimation of Power Lithium Battery for Mining Electric Locomotive

作　　者：张梅[1] 冯涛 Zhang Mei;Feng Tao(School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《煤矿机械》2023年第9期55-58,共4页Coal Mine Machinery

基　　金：国家自然科学基金:资助项目(51874010);安徽高校自然科学研究项目(KJ2020A0309)。

摘　　要：矿用电机车是煤矿井下的重要运输设备,其驱动能源锂电池由电池管理系统(BMS)进行管理与监测。电池的荷电状态(SOC)是BMS中的关键指标之一,对SOC的精确估计可以延长电池循环寿命,从而提高煤矿的产能和经济效益。以矿用磷酸铁锂电池为研究对象,模拟了与矿用电机车锂电池循环工况中类似的工况实验,首先搭建了二阶RC等效电路模型,再利用变量遗忘因子最小二乘法(VFFRLS)对采集的实验数据进行模型的参数辨识,最后利用多种改进的优化算法完成电池的SOC估算。实验结果表明,多种优化算法中多新息自适应无迹卡尔曼滤波(MIAUKF)算法具有最佳鲁棒性和最高的估计精度。Mine electric locomotive is an important transportation equipment in coal mine.Its driving energy lithium battery is managed and monitored by battery management system(BMS).The state of charge(SOC)of the battery is one of the key indicators in BMS.The accurate estimation of SOC can prolong the cycle life of the battery,thus improves the production capacity and economic benefits of the coal mine.The mine lithium iron phosphate battery was taken as the research object,and the charging and discharging experiment of the electric locomotive battery in the underground working temperature was simulated.Firstly,the second-order RC equivalent circuit model was built,and then the parameter identification of the model was carried out by using the variable forgetting factor least squares method(VFFRLS).Finally,a variety of improved optimization algorithms were used to complete the SOC estimation of the battery.The experimental results show that the multi innovation adaptive unscented Kalman filter(MIAUKF)algorithm has the best robustness and high estimation accuracy.

关 键 词：矿用电机车 锂电池 荷电状态 VFFRLS MIAUKF 

分 类 号：TD524[矿业工程—矿山机电]