Study On State Observation And Torque Distribution Strategy Of Hub-motor-in-wheels Driving Electric Vehicle

With the energy shortage and ecological environment deterioration recently, electric vehicle has become the developing direction of the future traffic because of its zero emission or extremely low emission. As a kind of electric vehicles, hub-motor-in-wheels driving EV (electric vehicle) has become an important development direction of electric vehicle because of advantages which are dispersion driven, flexible vehicle space layout, energy conservation and environmental protection. Compared with traditional vehicle, the handling stability control, driving state parameters observation and other technologies of hub-motor-in-wheels driving EV are the key problems which can effect performance of hub-motor-in-wheels driving EV. This paper takes hub-motor-in-wheels driving EV as the research object, and researches vehicle driving state parameters observation, vehicle control target parameters algorithm, torque distribution strategy based on handling stability, etc. This paper has high academic and engineering application value of improving performance and dynamics theory of hub-motor-in-wheels driving EV.This paper adopts modular design method to establish the simulation platform of 7 degrees of freedom (DOF) of hub-motor-in-wheels driving EV. The simulation platform includes vehicle dynamics model and vehicle controller model. The vehicle dynamics model mainly includes vehicle body model, hub-motor model, wheel model, tire model, steering system model, hydraulic brake system model, etc. The vehicle controller model mainly includes driver intention model and Ackerman steering differential model. In this paper, hub-motor energy efficiency based on test data and driver accelerated intention model and smooth intention model are established.This paper designs driving state parameters observation of hub-motor-in-wheels driving EV based on MCMC (Markov Chain Monte Carlo) particle filter algorithm and researches observation method of longitudinal vehicle velocity, lateral vehicle velocity, and yawing angular velocity based on MCMC particle filter algorithm. The vehicle control target parameters algorithm is researched by considering the change of the tire cornering stiffness which is caused by wheel vertical load transfer when vehicle is steering, the researched algorithm establishes the internal relation of longitudinal and lateral acceleration-wheel vertical load-tire cornering stiffness-vehicle control target parameters. On basis of above research, this paper researches the torque distribution strategy based on handling stability of hub-motor-in-wheels driving EV. The researched strategy adopts PID control strategy based on BP neural network and takes the torque distribution strategy based on handling stability when vehicle is driving on the low adhesion road, and analyses mutual target parameters coupling control problem selectively, and researches the improving method of handling stability of hub-motor-in-wheels driving EV.This paper researches and designs test vehicle of hub-motor-in-wheels driving EV. By analyzing characteristics of vehicle driving state parameters observation, vehicle driving state parameters observation algorithm, control target algorithm and torque distribution strategy based on handling stability are verified. The verification results show that the proposed algorithms and torque distribution strategy of hub-motor-in-wheels driving EV has certain effectiveness.