Modeling and Simulation of Energy-Regenerative Active Suspension Based on BP Neural Network PID Control

Abstract

In this paper, an electromagnetic energy-regenerative suspension system is proposed to achieve active control and vibration energy harvesting. For this system, a PID controller based on BP neural network algorithm is designed and vehicle dynamic performances are studied. Based on the traditional energy-regenerative efficiency calculation, a novel self-supply energy efficiency concept is proposed to evaluate the utilization effect of the recycled energy for this dual-functional suspension. Simulations are carried out, and the results show that the vehicle dynamic performances are effectively improved under different input conditions, including road surfaces and vehicle speeds. Furthermore, the energy-regenerative suspension can recover part of vibration energy, where the self-supply energy efficiency is about 55% and the energy-regenerative efficiency is about 16%. Meanwhile, the BP-PID algorithm also enables the suspension system’s self-adaptability and stability characteristics on its energy recovery capability.