Ride comfort parameter optimization of hydro-pneumatic suspension based on discretely constrained genetic algorithm

The genetic algorithm (GA) is capable of optimizing hydro-pneumatic suspension parameters based on multiple vehicle performance indices, thereby improving ride comfort. However, the conventional genetic algorithm can only perform continuous searches within a predefined numerical range, and the optimized parameters may not be consistent with practical constraints. To address this limitation, this study proposes a discrete constrained genetic algorithm (DCGA), which restricts optimization to a set of discrete candidate values. This approach ensures both the practicality of the optimized throttle valve orifice and the enhancement of ride comfort. In this work, the suspension system is simplified into a spring–damper model, and a one-fourth hydro-pneumatic suspension numerical model is developed in MATLAB–Simulink. The effects of suspension parameters on stiffness and damping characteristics are analyzed. Vertical acceleration signals of the unsprung mass under different road conditions are obtained using a tri-axial accelerometer. These signals are filtered with a bandwidth filter to suppress high-frequency noise and low-frequency drift, and subsequently integrated to derive vertical displacement and velocity, which are used as input for the unsprung mass. The proposed DCGA was employed to determine the optimal throttle valve orifice from a set of discrete candidate values, using the root mean square (RMS) values of vehicle body vertical displacement, vertical acceleration, and suspension dynamic deflection as performance evaluation metrics. Simulation results indicate that, under the discrete bump road condition, the optimized orifice reduces the RMS values of vehicle body vertical displacement, vertical acceleration, and suspension dynamic deflection by 41.6%, 65.3%, and 65.5%, respectively, compared with the initial throttle valve orifice. Under the random road condition, the corresponding reductions are 5.8%, 52.8%, and 82.2%, respectively.