Multi-Objective Parameter Optimization of Electro-Hydraulic Energy-Regenerative Suspension Systems for Urban Buses

Abstract

To enhance energy efficiency and reduce emissions in public transportation systems, this study proposes a novel electro-hydraulic energy-regenerative suspension system for urban buses. A comprehensive co-simulation framework was established to evaluate system performance. Targeting ride comfort and energy regeneration performance as dual optimization objectives, we conducted systematic parameter analysis through design-of-experiments methodology to identify critical structural parameters. To streamline multi-objective optimization processes, a particle swarm optimization–back propagation (PSO-BP) neural network surrogate model was developed to approximate the complex co-simulation system. Subsequent non-dominated sorting genetic algorithm II (NSGA-II) implementation enabled effective multi-objective optimization of key suspension parameters. Comparative simulations revealed that the optimized configuration achieves the following: (1) maintains ride comfort within human perception thresholds despite slight performance reduction, (2) enhances energy recovery efficiency, and (3) improves roll stability characteristics. These findings demonstrate the proposed system’s capability to balance passenger comfort with energy conservation and safety requirements.