Study On Integrated Control For Steer-by-wire Electric Vehicle With Four In-wheel Motors

With the continuous development of automobile electronic technology, internet technology, new energy technology, various forms of electric vehicles is becoming a hot spot in research, and steer-by-wire electric vehicle with four in-wheel motors is one of the more promising one. Various forms of electric vehicles is becoming a hot spot in research and development of automobile industries in the world, steer-by-wire electric vehicle with four in-wheel motors is one of the more promising one. This electric vehicle and steering system transmission ratio arbitrary can be adjusted, that was good carrier of automobile intelligent control method. thesis bases on youth fund of national natural science fund project "intelligent four-wheel independent drive electric vehicle adaptive steering wheel motor research". It studies on the driver’s preferences steering gear ratio integrated control research with steer-by-wire electric vehicle by four-wheel motors. The main contents are as follows:(1) The modeling of steer-by-wire electric vehicle with four in-wheel motors. The electric vehicle model by steer-by-wire system and wheel hub motor system were analysed. Steer-by-wire system and wheel hub motor system were built in the Matlab / Simulink, it was embedded into vehicle Car Sim model and established steer-by-wire electric vehicle by four-wheel motors.(2) The study of driving and steering integrated control algorithm. Thesis raises the active front steering and yaw moment control integration principle. It was studied on fuzzy control theory of active steering and direct horizontal pendulum torque coordinated control algorithm based on optimal control theory of active steering and direct cross yaw moment control algorithm, the simulation test of control algorithm were verified experimentally.(3) The study of driver’s preferences steering gear ratio’s classification and identification. Basing on the driving simulator experiment platform to finish experiment design. To classify driver’s preferences of steering gear ratio in using experimental data. Application of BP neural network was built the driver’s preferences steering ratio identification model.(4) The experiment of the driving simulator hardware in loop. This experiment uses driving simulator hardware in loop test bench and chooses typical working conditions. The driver’s preferences steering gear ratio identification model and steer-by-wire electric vehicle with four in-wheel motors integrated control.