Research On Electric Power Steering System Of Electric Logistics Vehicle Based On Mass Estimation

The weight of electric logistics vehicles varies greatly with different loads.If traditional Electric Power Steering(EPS)design methods are still used,it can’t guarantee the driver always has a clear road feel and may impact driving experience.At the same time,electric logistics cars mainly operate under city conditions at low speeds and frequent turning,which raises higher requirements for the agility of steer assist system.Focusing on the EPS system of electric logistics vehicles in this paper,it establishes a full-weight power-assisted characteristic suitable for electric logistics vehicles,solves the problem of steering loss and poor portability performance caused by weight changes,and research on the control strategies of the EPS system of electric logistics vehicles.The main research contents of this paper are as follows:(1)A quality estimation model for electric logistics vehicles was established,decoupling the slope,speed,and quality in the longitudinal vehicle dynamics model.The acceleration and driving force data were preprocessed using a tracking differentiator,and then using the Kalman filter method to estimate the overall vehicle quality.Subsequently,a curve-type assist characteristic covering the entire vehicle quality range was established,allowing the vehicle to obtain ideal assist torque at any quality,speed,and steering wheel torque.Establishing a BP neural network to solve the assist torque to replace the table lookup method in traditional automobile ECU and reduce ECU storage space usage.(2)Research on the control strategy of the assist motor.Aiming at the problems of traditional PID control such as high sensitivity to motor current,easy amplification of high-frequency noise,and difficulty in parameter adjustment,this paper uses an incomplete differential PID controller based on BP neural network to control the motor,and introduces RBF neural network as a The system identifier solves the Jacobian information of the system to improve the learning efficiency of the BP neural network.In order to make the control system more suitable for the actual needs of the industry,the BP neural network has been improved,adding single-connected linear units and output layer bias,so that the incomplete differential PID control parameters output by the neural network have ideal initial values and adjustable ranges,to further improve the control performance.(3)Co-simulation using CARSIM and MATLAB/SIMULINK.The effectiveness of the mass estimation model is verified through simulations on both fixed and variable slope roads.Lemniscate conditions and steering wheel angle step simulations are performed on the vehicle to demonstrate that the established full-quality assisted characteristic EPS system meets the requirements for lightness and improves the steering feel and vehicle handling stability.By comparing the control effects of PI control and incomplete differential PID control based on RBF identification and neural network,as well as improved incomplete differential PID control based on RBF identification and neural network,the effectiveness of the control strategy in this paper was verified.The improvement of the neural network was proved to improve the control performance,and the overshoot was reduced from 2.02% to 0.36% under step target signal.It had a small tracking error under sinusoidal target current.(4)An experimental platform was built using the EPS bench system and single-chip microcomputer,and the experimental program was designed.The control performance of PI control and improved PID control based on RBF identification and incomplete differential neural network was compared under step target signal and slope target signal.The experimental results show that the control strategy used in this paper reduces the regulation time by 0.26 seconds compared to PI control,lowers overshoot by 0.5%,and exhibits superior tracking performance.This paper studies the EPS system of electric logistics vehicles and designs a mass estimation model and a full-mass power-assisted characteristic EPS system.The control strategy of the EPS system is also studied.Simulation and experimental results prove the feasibility of the mass estimation method and full-mass power-assisted characteristic EPS system designed in this paper,as well as the good control performance of the control strategy.