Swing Steadiness Regulation of Electric Vehicles with Improved Neural Network PID Algorithm

• Published in: Processes Vol. 10; no. 10; p. 2106
• Main Authors: Song, Dongfang, Ji, Hong, Li, Kang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Algorithms; Angular velocity; Automobile safety; Automobiles, Electric; Back propagation networks; Control algorithms; Electric vehicles; Energy; Energy efficiency; Environmental impact; Experiments; Industrial development; Neural networks; Optimization; Regulation; Safety; Safety regulations; Simulation; Snakes; Torque; United Kingdom
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr10102106

With the intensification of global environmental pollution and the energy crisis, the new energy EV industry is developing rapidly, and FWID-EV is a popular direction for future vehicle development. For the sake of improving the swing regulate steadiness and safety of EV, the study uses a particle swarm algorithm to optimize and improve the BP neural network PID, and designs an EV steering regulator to regulate the transverse swing torque and slip rate of EV to improve the safety and steadiness of EV steering. The research results display that the maximum value of the transverse swing angular velocity of the regulation algorithm is 0.156 rad/s, that the car slip rate is controlled within 0.046, and the steadiness is high, and that the maximum values of the car torque under the double shift line and snake conditions are 100 N-m and 179.4 N-m, respectively, which can effectively avoid the danger caused by steering. This demonstrates that the improved neural network PID regulator can effectively distribute the steering torque of the EV and improve the steering steadiness and safety of the EV while maintaining the driving dynamics. The use of the improved neural network PID algorithm to achieve the steering steadiness regulation of EV is of great significance to improve the safety of new energy EV, and helps to promote the widespread use of new energy EV.