Joint Estimation of Driving State and Road Adhesion Coefficient for Distributed Drive Electric Vehicle

Abstract

Obtaining accurate vehicle driving state and road adhesion coefficient information is of great significance to many aspects of the vehicle. This paper takes distributed drive electric vehicles as the research object and designs a joint estimation method of vehicle driving state and road adhesion coefficient based on the theory of federated-cubature Kalman filter. The corresponding nonlinear three-degree-of-freedom vehicle dynamics model is established and the state space equation is obtained. Multi-source fusion of low-cost sensor signals is carried out by using information fusion technology, and an algorithm estimator is built by using vehicle dynamics theory. Select typical experimental conditions and apply Simulink to build an algorithm model and co-simulate with CarSim for verification. The experimental results show that the proposed estimation method can improve the accuracy and stability of state estimation.

Keywords

• Distributed drive electric vehicle
• driving state estimation
• federal-cubature Kalman filter
• information fusion
• road adhesion coefficient
• simulation verification