重庆理工大学学报2025，Vol.39Issue(21)：12-20,9.DOI:10.3969/j.issn.1674-8425(z).2025.11.002
基于CKMC-SCKF的三轴分布式电驱动重型车辆状态估计
State estimation of three-axis distributed drive heavy-duty vehicles based on CKMC-SCKF
摘要
Abstract
To accurately estimate the state vibrations of multi-axis distributed electric drive heavy-duty vehicles operating in non-Gaussian noise environments,this paper proposes a Cauchy-kernel maxmium correlation entropy square-rootcabature Kalman filter(CKMC-SCKF)algorithm.It adopts the Cauchy kernel maximum correlation entropy criterion as the optimization objective for vehicle state estimation.By integrating a kernel adaptive filter based on logarithmic similarity and employing fixed-point iteration to update the target estimation state,the algorithm dynamically adjusts the error covariance matrix,and effectively increases the proportion of effective data in state estimation to improve the robustness of the filter.A nine-degree-of-freedom three-axle distributed electric drive heavy-duty vehicle dynamic model is constructed,and a co-simulation platform is built using Trucksim and Matlab to estimate the lateral angular velocity,sideslip angle at the center of mass,and longitudinal velocity,verifying the accuracy and reliability of the proposed CKMC-SCKF under various working conditions.Simulation results demonstrate the proposed method achieves markedly higher estimation accuracy and robustness under non-Gaussian noise conditions compared with Gaussian kernel maximum correlation entropy root mean square volume Kalman filtering and traditional volume Kalman filtering algorithms..关键词
柯西核函数/最大相关熵/均方根容积卡尔曼滤波/车辆状态估计Key words
Cauchy-kernel function/maximum correlation entropy/Square-root Cubature Kalman Filter/vehicle state estimation分类
交通工程引用本文复制引用
GENG Guoqing,ZHUANG Shengru,WANG Bo,XU Xing..基于CKMC-SCKF的三轴分布式电驱动重型车辆状态估计[J].重庆理工大学学报,2025,39(21):12-20,9.基金项目
江苏省重点研发计划竞争项目(BE2019010) （BE2019010）
