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

Abstract

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-Carsim®. 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.

Design of Active Disturbance Rejection Controller for Trajectory-Following of Autonomous Ground Electric Vehicles

“…Here assumptions are made using the bicycle model: the suspension is a robust construction, and the slip angle is usually small under normal driving conditions. The bicycle model is used as a tool in the examination of actual vehicle movements [5,7,25,26]. The essential parameters and symbols show in Appendix A.…”

“…Note that vehicle state, such as the side-slip angle of center can, be estimated using an online estimation method and observer [25][26][27]. When the AGEV is traveling at high speeds, the center of the side-slip angle can have a significant impact on the AGEV's stability.…”

Advanced Modeling, Analysis and Control for Electrified Vehicles

“…As an important part of future smart transportation systems, electrified vehicles have attracted increasing attention from academia, the industry and governments around the world due to their advantages and applications. Taking advantage of the advanced architectures supported by electrified vehicles, a large number of advanced models; estimation and control techniques for electrified vehicles such as parameter estimation, electronic control steering systems and roll stability control systems; torque distribution analyses; time analyses in in-vehicle controller-area networks; multi-objective topology optimizations and controllers designed for the active suspensions of in-wheel motors have been built or conducted to improve the active safety of vehicles [1][2][3][4][5][6].…”