Combined Estimation of Vehicle Dynamic State and Inertial Parameter for Electric Vehicles Based on Dual Central Difference Kalman Filter Method  

作　　者：Xianjian Jin Junpeng Yang Liwei Xu Chongfeng Wei Zhaoran Wang Guodong Yin 

机构地区：[1]School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200072,China [2]State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130025,China [3]School of Mechanical Engineering,Southeast University,Nanjing 211189,China [4]Mechanical and Construction Engineering,Northumbria University,Newcastle NE18ST,UK

出　　处：《Chinese Journal of Mechanical Engineering》2023年第4期339-354,共16页中国机械工程学报（英文版）

基　　金：Supported by National Natural Science Foundation of China(Grant Nos.51905329,51975118);Foundation of State Key Laboratory of Automotive Simulation and Control of China(Grant No.20181112).

摘　　要：Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically estimated.This paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of gravity.The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs.The four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory.To address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and compared.Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-CarsimR.The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions.This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.

关 键 词：Distributed drive Electric vehicle State observation Inertial parameter Dual central difference Kalman filter 

分 类 号：U469.7[机械工程—车辆工程]