基于改进PNGV模型的电池SOC估计算法研究  

Research on battery SOC estimation algorithm based on the improved PNGV model

作　　者：樊波[1] 栾新宇[1] 张瑞[1] 牛天林[1] 赵广胜[1] Fan Bo;Luan Xinyu;Zhang Rui;Niu Tianlin;Zhao Guangsheng(College of Air and Missile Defense,Air Force Engineering University,Xi′an 710051,China)

机构地区：[1]空军工程大学防空反导学院,西安710051

出　　处：《电测与仪表》2018年第20期46-51,共6页Electrical Measurement & Instrumentation

摘　　要：针对储能磷酸铁锂电池并根据磷酸铁锂电池电化学阻抗谱研究,提出一种双RC并联环节的改进PNGV模型,在HPPC实验下辨识模型参数。针对扩展卡尔曼滤波(EKF)算法在估计电池荷电状态(SOC)时不能实时估测噪声的缺点,将Sage-Husa自适应算法引入EKF算法得到自适应扩展卡尔曼滤波算法,并通过对噪声实时预测和修正来提高电池SOC估计精度。在Matlab/Simulink中搭建电池及SOC估计仿真模型并在模拟动态工况下进行仿真。仿真结果表明改进PNGV模型精度优于PNGV模型;自适应扩展卡尔曼滤波算法估计电池SOC时较EKF算法收敛速度更快,估计精度更高。模型及算法的改进取得较好的效果。According to the electrochemical impedance spectroscopy of LiFePO4 battery,an improved PNGV model of double RC parallel connection was proposed in this paper.Model parameters were identified under HPPC experiment.In view of the EKF algorithm cannot estimate the noise in real-time when estimating battery SOC,the Sage-Husa adaptive algorithm was introduced into the EKF algorithm,and the adaptive extended Kalman filter was obtained.The new algorithm improved the accuracy of SOC estimation by real-time prediction and correction of noise.The simulation models of battery and SOC estimation were built in Matlab/Simulink,and simulation was carried out under the simulated dynamic conditions.The simulation results showed that the accuracy of the PNGV model was better than that of the PNGV model;the adaptive extended kalman filter algorithm had faster convergence speed and higher estimation accuracy when compared with the EKF algorithm.The improved model and algorithm had achieved better results.

关 键 词：磷酸铁锂电池 改进PNGV模型 SOC估计 自适应扩展卡尔曼滤波算法 

分 类 号：TM912[电气工程—电力电子与电力传动]