摘要
路面附着系数是影响汽车行驶状态估计的重要因素,单一路面附着系数下的汽车行驶状态估计无法适应各种路面工况。针对分布式电动汽车行驶状态与路面附着系数估计问题,研究了一种基于双容积卡尔曼滤波理论的联合估计算法。利用分布式电动汽车多信息源优势,建立3自由度车辆估计模型,将多传感器信号作为估计模型的输入,侧向力通过Dugoff轮胎模型计算获得,设计行驶状态和路面附着双容积联合估计算法。通过典型工况对接路面双移线进行仿真实验,结果表明算法能够实现实时准确估计。
Road friction coefficient is an important factor affecting vehicle driving state estimation.Vehicle driving state under single road friction coefficient is not suitable for various road conditions.Aiming at the problem of distributed electric vehicle driving state and road friction coefficient estimation,this paper studied a joint estimation algorithm based on dual-cubature Kalman filter theory.Based on the advantage of distributed electric vehicle with multiple information sources,a three-degree-of-freedom vehicle estimation model was established.Multi-sensor signals were used as input of estimation model.Lateral force was calculated by Dugoff tire model,and driving state and road friction coefficient dual-cubature joint estimation algorithm were designed.The simulation experiment of double-shift line on the pavement with typical experiment condition proved the algorithm could achieve accurate estimation in real time.
作者
樊东升
李刚
王野
FAN Dongsheng;LI Gang;WANG Ye(Automobile&Transportation Engineering College,Liaoning University of Technology,Jinzhou 121001,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2020年第6期69-76,共8页
Journal of Chongqing University of Technology：Natural Science
基金
国家自科学基金项目(51675257)
辽宁省创新人才项目(LR2016054)。
关键词
分布式电动汽车
行驶状态
估计模型
路面附着系数
容积卡尔曼滤波
distributed electric vehicle
driving state
estimation model
road friction coefficient
cubature kalman filter
