Firefly Algorithm-Based PID Optimization for Active Suspension Systems in Electric Vehicles

This paper presents the optimization of a PID controller for an active suspension (AS) system in the electric vehicle (EV) using the Firefly Algorithm (FA). The objective is to enhance ride comfort and vehicle stability by minimizing body acceleration (BA), suspension dynamic deflection (SDD), and wheel dynamic load (WDL). The proposed AS system is based on a quarter-car EV model. Random road excitation and harmonic disturbances are selected as input conditions to evaluate system performance. The FA is employed to determine the optimal PID parameters, improving the system’s overall efficiency. The AS system and PID controller are developed in the Matlab/Simulink environment. The results demonstrate that the optimized PID-controlled active suspension (AS-OPID) achieves significant performance improvements, reducing the root mean square (RMS) values of BA, SDD, and WDL by 23.05%, 19.78%, and 13.31%, respectively, compared to a passive suspension (PS) system under random road conditions at a vehicle speed of 70 km/h. These improvements highlight the effectiveness of FA in optimizing control parameters, leading to better ride quality and vehicle stability. The findings confirm that FA-based PID optimization is a promising approach for enhancing AS performance in EVs.