基于一阶斯特林插值滤波器的电池SOC估计和硬件实现  

SOC Estimation of Battery Based on First-order Stirling Interpolation Filter and its Hardware Implementation

作　　者：程泽[1] 王莉[1] 刘继光[1] 

机构地区：[1]天津大学电气与自动化工程学院,天津300072

出　　处：《陕西电力》2015年第2期59-63,共5页Shanxi Electric Power

基　　金：国家自然科学基金资助项目(61374122)

摘　　要：锂离子电池荷电状态(SOC)的准确估计是电池管理系统最基本和最首要的任务。文中采用二阶RC模型和一阶斯特林插值滤波算法对锂电池的SOC进行估计。一阶斯特林插值滤波算法基于卡尔曼滤波算法,但是不需要每次都进行jacobi矩阵的更新计算。对二阶RC模型的参数进行了辨识,给出了一种基于一阶斯特林差值滤波器的锂电池SOC估计算法。搭建了基于DSP2812的硬件平台,用实验的方法与仿真结果进行了对比。仿真和实验结果表明,该模型能很好地模拟锂电池的动态特性,且一阶斯特林插值滤波算法有着比扩展卡尔曼滤波算法更精确的估计值。It is necessary to estimate the state of charge of lithium-ion battery for battery management system.This paper employs a second-order RC model and first-order Stirling interpolation filtering algorithm to estimate the SOC.The first-order Stirling interpolation filtering algorithm is based on the Kalman filter algorithm,but it doesn＇t need to update the Jacob matrix at every step of the calculation.The paper identifies the parameters of the second-order RC model,proposes an algorithm for estimating the SOC based on the first-order Stirling interpolation filtering algorithm,and establishes a DSP 2812 based hardware platform to carry out the experiment.The simulation and experimental results indicate that the battery model has a high fitting precision in dynamic characteristics simulation of the lithium ion battery and the first-order Stirling interpolation filtering algorithm can improve the estimation accuracy of the SOC when comparing to the Kalman filtering algorithm.

关 键 词：剩余电量 一阶斯特林差值滤波器 锂离子电池 硬件实现 

分 类 号：TM93[电气工程—电力电子与电力传动]