基于改进EKF的无人机SOC估算分析  

SOC estimation method of UAV based on improved EKF

作　　者：张宇[1,2] 王利杰 吴铁洲[1,2] 张敏[1,2] ZHANG Yu;WANG Li-jie;WU Tie-zhou;ZHANG Min(School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan Hubei 430068, China;Key Laboratory of High Efficiency Utilization and Energy Storage Operation Control of Hubei Province, Hubei University of Technology, Wuhan Hubei 430068, China)

机构地区：[1]湖北工业大学电气与电子工程学院,湖北武汉430068 [2]湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室,湖北武汉430068

出　　处：《电源技术》2019年第2期320-323,共4页Chinese Journal of Power Sources

基　　金：湖北省科技厅技术创新重大专项(2018AAA056)

摘　　要：多旋翼无人机的工作情况要求采用精度很高的电池剩余电量(SOC)估算方法,而常用的安时法因其特性在该领域中无法做到精确估算,针对多旋翼无人机的工作特性,在戴维南电池模型的基础上,利用扩展卡尔曼滤波法(EKF)估算多旋翼无人机电池剩余电量,并为提高EKF算法的跟踪效果,加入实时调整因子,改进EKF算法。实验结果表明采用扩展卡尔曼滤波算法估算多旋翼无人机的SOC值有较好效果,改进后的算法相比改进前的算法精度又提高了25%,可以有效解决多旋翼无人机电池SOC值无法精准估算的问题。The operation of a multi-rotor UAV requires a highly accurate battery residual capacity (SOC) estimation method, and the commonly used current integration method cannot be accurately estimated in this field due to its characteristics. In order to find a method for accurately estimating the SOC value of a multi-rotor UAV battery, based on the full analysis of the working environment of the multi-rotor UAV, the extended Kalman filter method was applied to estimate the SOC value of the UAV battery, and here based on the introduction of dynamic change gain, the tracking effect estimated by the extended Kalman filter method was improved. The experimental results show that the extended Kalman filter algorithm is effective in estimating the SOC value of the multi-rotor UAV. The improved algorithm improves the accuracy of the algorithm before the improvement by 25%, which can effectively solve the SOC value of the multi-rotor UAV battery and the problems that cannot be accurately estimated.

关 键 词：多旋翼无人机 剩余电量 安时法 扩展卡尔曼滤波法 跟踪效果 

分 类 号：TM912[电气工程—电力电子与电力传动]