分布式驱动电动汽车行驶状态估计综述  

Review on driving state estimation of distributed drive electric vehicles

作　　者：樊东升 李刚[1] Fan Dongsheng;Li Gang(Automobile&Transportation Engineering College,Liaoning University of Technology,Liaoning Jinzhou 121001)

机构地区：[1]辽宁工业大学汽车与交通工程学院

出　　处：《汽车实用技术》2019年第16期10-12,共3页Automobile Applied Technology

基　　金：国家自然科学基金面上项目(51675257);辽宁省高等学校创新人才项目(LR2016 054)

摘　　要：针对分布式驱动电动汽车行驶状态估计的问题,论文对汽车行驶状态估计算法的研究现状进行了综述,列举了在车辆行驶状态估计中常用的估计算法,分别介绍了扩展卡尔曼滤波算法、容积卡尔曼滤波算法和联邦卡尔曼滤波算法在车辆行驶状态估计中的优缺点,结合各算法在实际应用中需要考虑的因素,对比分析不同的估计算法对车辆行驶状态参数估计效果的影响,指出基于联邦卡尔曼(FKF)滤波算法的多信息融合估计车辆行驶状态的方法是提高估计精度的有效手段。For the problem of driving state estimation of distributed driving electric vehicles,the paper reviewed research status of the vehicle driving state estimation algorithm.The estimation algorithms commonly used in vehicle driving state estimation were listed.The advantages and disadvantages of vehicle driving state estimation for extended Kalman filter algorithm,Cubature Kalman filter algorithm and federated Kalman filter algorithm were introduced.Combined with the factors that each algorithm needs to consider in practical application,the paper compared and analyzed the influence of different estimation algorithms on vehicle driving state estimation,and pointed out the multi-information fusion based on federated Kalman filtering(FKF)algorithm.The method of vehicle driving state estimation is an effective means to improve the estimation accuracy.

关 键 词：分布式驱动电动汽车 行驶状态估计 容积卡尔曼滤波 联邦卡尔曼滤波 

分 类 号：U469.72[机械工程—车辆工程]