Balancing Ride Comfort: Optimizing Semi-Active Suspension Systems Through Multi-Objective Parameter Tuning

Optimizing semi-active suspension systems for ride comfort presents significant challenges due to the inherently conflicting requirements of different ride conditions. Enhancing primary ride associated with low-frequency body motions—may compromise the system’s ability to handle secondary ride, which involves mid-frequency road irregularities. Conversely, tuning for impact loadcases like potholes or road discontinuities often introduces trade-offs that can worsen comfort or suspension performance under regular driving conditions. These conflicts necessitate a systematic approach to identify balanced suspension behavior across diverse frequency bands. This study focuses on the parameter tuning of a smart damper within a semi-active suspension system to optimize overall ride performance. The investigation is carried out using a high-fidelity vehicle model in ADAMS Car, while mode-FRONTIER is employed to perform multi-objective optimization. This approach targets specific tunable blocks within the smart damper's control logic—such as Skyhook block and bump-hole management block. A set of performance metrics including driver and passenger acceleration, chassis bounce and pitch and suspension travels, are evaluated under a range of primary, secondary, and impact ride scenarios. Using mode-FRONTIER’s design exploration capabilities, Pareto-optimal solutions are identified to balance comfort across all ride conditions without favoring one case excessively over another. The outcomes of this study are expected to offer practical insights into the behavior of smart damping systems under conflicting ride requirements and provide a methodology for tuning such systems in virtual development phases. The framework demonstrates a scalable approach for enhancing ride quality through intelligent suspension control, supporting more adaptive and passenger-centric vehicle design.