基于卡尔曼滤波的井下蓄电池运输车辆锂电池SOC估计  

The SOC estimation based on Kalman filtering of the lithium-ion battery used on electric vehicles in underground coal mines

作　　者：徐治仁 廖广博 董秀秀 王一男 程博威 李博文 

机构地区：[1]中国矿业大学(北京)机电与信息工程学院,北京100083

出　　处：《科技风》2018年第3期91-93,共3页

摘　　要：我国煤矿井下广泛使用防爆蓄电池无轨辅助运输车辆,由于实际工程应用环境十分恶劣,为了准确估算车辆蓄电池的荷电状态SOC(State of Charge),在研究电池电特性的基础上采用改进电池简化等效电路模型,且基于此模型结合安时积分法和卡尔曼滤波法对锂电池的SOC进行估计,并使用Matlab软件进行仿真和验证,将结果与小波滤波方法进行比较分析,仿真结果表明卡尔曼滤波算法能够实时修正估计误差,具有更好的估算精度。同时,为了更好地演示、分析与便于用户使用,还基于此设计并开发了Matlab GUI程序。In China, explosion-proof battery auxiliary trackless transport vehicles are widely used in the coal mine. In order to estimate the vehicle battery state of charge SOC （ State of Charge） accurately, plus the tough actual application environment, applies an improved simplified battery circuit model through the electrical characteristics. Makes an estimation of the lithium battery SOC with a Kalman filter based on the model, combined with the Ampere-Hour Method, which is simulated and verified with the Matlab software, and compared the results with ones of wavelet filtering. The results show that the Kalman filtering algorithm makes a real-time correction of the estimation error and the accuracy is better. At the same time, in order to demonstrate, analyze better and facilitate the users, a Matlab GUI program is de- signed and developed.

关 键 词：荷电状态 卡尔曼滤波 等效电路模型 锂电池 井下机车 

分 类 号：TN402[电子电信—微电子学与固体电子学]