重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (12): 58-66.

• 汽车工程 • 上一篇    下一篇

基于最大相关熵SCKF的分布式电动汽车状态估计

高伟, 杨涛, 邓召文, 王保华, 吴华伟, 朱远志   

  1. 湖北汽车工业学院汽车工程学院; 南京航空航天大学能源与动力学院; 湖北隆中实验室; 湖北文理学院汽车与交通工程学院; 北方工业大学机械与材料工程学院
  • 出版日期:2024-02-04 发布日期:2024-02-04
  • 作者简介:高伟,女,博士研究生,副教授,主要从事汽车动力学仿真与控制研究,E-mail:gaowei978112@163.com;通信作者 邓召文,男,博士,教授,主要从事汽车动力学仿真与控制研究,E-mail:dengzhaowen1@163.com

Distributed state estimation for electric vehicles based on MCSKF

  • Online:2024-02-04 Published:2024-02-04

摘要: 车辆状态的精确估计,对车辆横、纵向稳定性控制至关重要。在车辆状态估计中,容积卡尔曼滤波(cubature Kalman fifilter,CKF)和平方根容积卡尔曼滤波(SCKF,square-root cubature Kalman fifilter)易受重尾非高斯噪声的影响,估计精度差。为了解决该问题,提出了一种基于最大相关熵准则的新型滤波算法,即最大相关熵平方根容积卡尔曼滤波器MCSCKF(maximum correntropy square-root cubature Kalman fifilter),通过近似状态预测值和测量值重新构造测量噪声协方差矩阵。建立了非线性7DOF车辆模型、Dugoff轮胎模型和Carsim分布式电驱动车辆模型,在正弦工况和双移线工况下,对车辆的纵向速度、侧向速度和横摆角速度3个状态变量进行估计。通过Carsim和Matlab/Simulink联合仿真验证,结果表明:MCSCKF算法可以适应复杂工况,对车辆状态估计的准确性优于CKF和SCKF算法

关键词: 分布式电驱动汽车, 状态估计, 平方根容积卡尔曼滤波, 最大相关熵

Abstract: The accurate estimation of vehicle state is crucial for the control of its lateral and longitudinal stability. In vehicle state estimation, the Cubature Kalman Filter (CKF) and Square-root Cubature Kalman Filter (SCKF) are susceptible to heavy-tailed non-Gaussian noise, leading to decreased estimation accuracy. To address the problem, this paper proposes a novel filtering algorithm based on the Maximum Correntropy Square-root Cubature Kalman Filter (MCSCKF) that utilizes the maximum correntropy criterion. The algorithm reconstructs the measurement noise covariance matrix by approximating the state prediction and measurement values. Nonlinear 7-degree-of-freedom (DOF) vehicle model, Dugoff tire model, and Carsim distributed electric drive vehicle model are built to estimate three state variables of the vehicle, namely longitudinal velocity, lateral velocity, and yaw angular velocity, under sinusoidal and double lane-change conditions. The algorithm is verified by the joint simulation of Carsim and Matlab/Simulink. The results show the MCSCKF algorithm adapts to complex working conditions and improves the the accuracy of vehicle state estimation compared with CKF and SCKF algorithms.

中图分类号: 

  • U461.1