Estimation of State and Road Adhesion Coefficient for Distributed Drive Electric Vehicles

Abstract

Purposes Accurate acquisition of vehicle driving state parameters and road adhesion coefficient is very important to maintain the safety and stability of distributed drive electric vehicles. Therefore, it is necessary to get an algorithm that can accurately estimate the parameters, which can not only reduce the cost of mass production vehicles, but also achieve the purpose of vehicle safety and stability. Methods In this paper, a joint estimation algorithm is investigated. First, a 3-degree-of-freedom vehicle model is established, and a parameter estimator is built by applying the volumetric Kalman filtering algorithm based on the singular value decomposition method. Then, the Carsim/Simulink simulation experiment platform is built, and high and low attachments and buttressing road double shift line conditions are used for simulation verification. Findings The simulation experiment results show that the estimator can estimate the real values of parameters more accurately. On the basis of the good estimation effect of the simulation, a real vehicle experimental platform is built, and further verification is carried out to conclude that the real-vehicle experimental results are consistent with the trend of the simulation results, and the algorithm has good estimation accuracy and timeliness.

Keywords

• distributed drive electric vehicles
• volumetric kalman filtering
• singular value decomposition
• driving state parameters
• road surface adhesion coefficient