Performance Analysis of Nonlinear Stiffness Suspension Based on Multi-Objective Optimization

This study optimizes vehicle suspension dynamics by introducing a controllable degree of nonlinearity, characterized by a parameter ε, into the spring element of Inerter-Spring-Damper (ISD) systems. Quarter-vehicle models for parallel and series ISD configurations are established, and a multi-objective genetic algorithm optimizes the parameters under random road excitation to minimize body acceleration (BA), suspension working space (SWS), and dynamic tire load (DTL). Results demonstrate that optimizing ε brings advantages: compared to a conventional passive suspension, the optimized parallel ISD suspension reduces BA, SWS, and DTL by 7.98%, 8.57%, and 1.69%, respectively, with the BA reduction notably improving from 5.94% (achieved by the linear ISD with ε = 0) to 7.98%. Similarly, the optimized series ISD achieves reductions of 2.53%, 7.62%, and 6.42% in BA, SWS, and DTL, showing a more balanced enhancement over its linear counterpart. The analysis reveals how ε distinctly influences the performance trade-offs, validating that strategically tuning the spring nonlinearity degree, in synergy with the inerter and damper, provides an effective method for superior suspension performance customization.