Research On Control Strategy Of Electric Vehicle Driving And Braking Based On Road Recognition

The development of automobile industry provides great convenience for people to travel and plays a great role in promoting social development.However,with the popularization of fuel vehicles,their exhaust has seriously polluted the environment.In order to cope with environmental pollution and energy crisis,countries around the world begin to attach importance to the development of new energy vehicles.As one of the new energy vehicles,pure electric vehicles don't produce polluting gases during driving,which makes pure electric vehicles become the focus of development.Pure electric vehicles can recover braking energy during braking process,which effectively alleviates the shortcomings of pure electric vehicles with short driving mileage.In order to ensure the braking safety and driving stability of electric vehicles and have regenerative braking function,it is necessary to conduct in-depth research on the driving and braking control strategy of electric vehicles.In this paper,relying on the national key research and development project of new energy vehicles,the related work of pure electric vehicle driving and braking control strategy research is carried out.The specific contents are as follows:Firstly,the layout of power system of pure electric vehicle is selected,and the main components of power system are selected and matched according to the parameters and performance indexes of a pure electric vehicle.Then,the driving and braking control strategy of pure electric vehicle based on the optimal slip rate is adopted.According to Burckhardt tire model fitting several common standard pavement,a road surface recognition method based on RBF neural network is designed to identify the optimal slip rate and peak adhesion coefficient of the current road surface on-line.Then,the driving and braking control strategy of electric vehicle is designed.In the braking condition,the anti-lock braking control is carried out by using the fuzzy control method.The sliding error and error rate are two inputs,and the braking moment adjustment parameters are the output.At the same time,the braking moment distribution strategy considering regenerative braking is formulated.The rationality of the braking moment distribution strategy is verified by several typical conditions in Advisor simulation software.The power reaching law sliding mode control method is used to control the driving moment.The switching function is the sliding rate error,and the chattering of the system is reduced by replacing the symbolic function with a continuous function.Finally,based on Carsim and Simulink simulation platform,the joint simulation model of driving and braking control strategy of electric vehicle is established;the quantitative factor of fuzzy controller and power reaching law parameters of sliding mode controller are optimized globally by genetic optimization algorithm;finally,the driving and braking control strategy of electric vehicle based on optimal sliding rate is simulated and validated by setting different road conditions.The simulation results show that the road surface recognition method based on RBF neural network can identify the optimal sliding rate and peak adhesion coefficient of the road surface online;the driving and braking control effect of electric vehicle is good,which shows that the driving and braking control strategy of electric vehicle based on the optimal sliding rate is reasonable and effective;the control effect after parameter optimization is further improved,which indicates that the control effect is carried out by genetic algorithm.It is feasible to optimize parameters.