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

Abstract

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.

Optimal Neural Network PID Approach for Building Thermal Management

“…Many studies have opted for optimal solutions based on artificial intelligence techniques facilitated by the development of computer technology, such as genetic algorithms [10], Fuzzy Logic [11,12], Neural Networks [13,14], decoupling control, and others. They are used in several areas such as controlling electrical DC motors [15,16], automatic and robot manipulation systems [17], controlling temperature performance [18], and controlling systems in agriculture [19].…”