基于DEKF的储能电池系统SOC估计方法研究

电池管理系统(BMS)是储能电池系统安全稳定运行的重要保障。为了保障储能电池系统的运行可靠性,在BMS投入运行前进行系统测试具有重要意义,而目前对于储能系统BMS的荷电状态(SOC)估计方法缺乏测试规范和标准。因此,文中针对储能电站BMS建立了入网测试平台,根据电池外特性信息建立Thevenin等效电路模型,电池开路电压曲线获取采用了电池倍率放电曲线外推的方法,结合双扩展卡尔曼滤波(DEKF)算法实现SOC的准确估计,并与EKF方法进行了对比。结果表明,DEKF方法在收敛速度和SOC估计精度上存在优势,分别在典型联邦城市运行工况(FUDS)和动态应力测试(DST)测试工况下,运用DEKF方法和EKF方法估计得到的SOC误差都低于1%,电池端电压误差分别在±10 mV和±20 mV以内,平均绝对误差分别为2.7 mV和3.8 mV。

SOC estimation method of battery energy storage system for BMS test platform

Battery management system (BMS) is an important part for battery energy storage system to guarantee the safety operation. It is of great significance for the operation and maintenance of the energy storage power station to test the system before the BMS is put into operation. However, at present there is no test specification and standard for the battery energy storage system BMS in the field of State of Charge(SOC) estimation. Therefore, this paper establishes a test platform for BMS of battery energy storage system, Thevenin equivalent circuit model based on the information of external characteristics of battery is utilized, and the method of extrapolation of battery multiple discharge curve is used to obtain the open circuit voltage curve of battery. Besides, the dual extended Kalman filter (DEKF) algorithm is proposed to realize the accurate estimation of SOC, and compares it with the EKF method. DEKF method has ad-vantages in convergence speed and SOC estimation accuracy. Under the typical Federal Urban Driving Schedule (FUDS) and Dynamic Stress Test (DST) test conditions, the SOC error estimated by DEKF method and EKF method is less than 1%. The battery terminal voltage error is within ± 10mV and ± 20mV respectively, and the average absolute error is within ± 20mV respectively 2.7mV and 3.8mV.