A new multi-objective optimization method for full-vehicle suspension systems

The conventional approach in vehicle suspension optimization based on the ride comfort and the handling performance requires decomposition of the multi-performance targets, followed by lengthy iteration processes. Suspension tuning is a time-consuming process, which often requires the benchmarking of competitors’ vehicles to define the performance targets of the desired vehicle by experimental techniques. Optimum targets are difficult to derive from benchmark vehicles as each vehicle has its own unique vehicle set-up. A new method is proposed to simplify this process and to reduce significantly the development process. These design objectives are formulated into a multi-objective optimization problem together with the suspension packaging dimensions as the design constraints. This is in order to produce a Pareto front of an optimized vehicle at the early stages of design. These objectives are minimized using a multi-objective optimization workflow, which involves a sampling technique, and a regularity-model-based multi-objective estimation of the distribution algorithm to solve greater than 100-dimensional spaces of the design parameters by the software-in-the-loop optimization process. The methodology showed promising results in optimizing a full-vehicle suspension design based on the ride comfort and the handling performance, in comparison with the conventional approach.

[1]  P. E. Uys,et al.  Investigation of the applicability of the dynamic-Q optimisation algorithm to vehicle suspension design , 2003 .

[2]  Qingfu Zhang,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 RM-MEDA: A Regularity Model-Based Multiobjective Estimation of , 2022 .

[3]  Pinhas Barak,et al.  Magic numbers in design of suspensions for passenger cars , 1991 .

[4]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[5]  S. G. Joshi,et al.  OPTIMUM DESIGN OF A PASSIVE SUSPENSION SYSTEM OF A VEHICLE SUBJECTED TO ACTUAL RANDOM ROAD EXCITATIONS , 1999 .

[6]  Wanzhong Zhao,et al.  Research on the multi-disciplinary design method for an integrated automotive steering and suspension system , 2015 .

[7]  L.F.P. Etman,et al.  Design of a Stroke Dependent Damper for the Front Axle Suspension of a Truck Using Multibody System Dynamics and Numerical Optimization , 2002 .

[8]  P. E. Uys,et al.  The ride comfort vs. handling compromise for off-road vehicles , 2007 .

[9]  Saltelli Andrea,et al.  Sensitivity Analysis for Nonlinear Mathematical Models. Numerical ExperienceSensitivity Analysis for Nonlinear Mathematical Models. Numerical Experience , 1995 .

[10]  Tey Jing Yuen,et al.  Comparision Of Compuational Efficiency Of MOEA\D and NSGA-II For Passive Vehicle Suspension Optimization , 2010, ECMS.

[11]  D.-O. Kang,et al.  Robust design optimization of the McPherson suspension system with consideration of a bush compliance uncertainty , 2010 .

[12]  Kim,et al.  Kinematic Design of a Double Wishbone Type Front Suspension Mechanism Using Multi-objective Optimisation , 2007 .

[13]  Howard Alan Simon Ash.  Correlation of subjective and objective handling of vehicle behaviour , 2002 .

[14]  Masato Abe,et al.  Vehicle Handling Dynamics: Theory and Application , 2009 .

[15]  G Virzì Mariotti,et al.  Optimization of the characteristic angles of both front and rear McPherson suspensions on a circular track using multi-body numerical simulation , 2009 .

[16]  P. E. Uys,et al.  Criteria for handling measurement , 2006 .

[17]  P. E. Uys,et al.  Efficient optimisation of a vehicle suspension system, using a gradient-based approximation method, Part 2: Optimisation results , 2009, Math. Comput. Model..

[18]  T C Sun,et al.  Dynamic response and optimization for suspension systems with non-linear viscous damping , 2000 .

[19]  Tey Jing Yuen,et al.  Optimized suspension kinematic profiles for handling performance using 10-degree-of-freedom vehicle model , 2014 .

[20]  Hee G. Lee,et al.  Design Sensitivity Analysis and Optimization of McPherson Suspension Systems , 2009 .

[21]  Siang Yew Chong,et al.  Identification of vehicle suspension parameters by design optimization , 2014 .

[22]  P. E. Uys,et al.  Efficient optimisation of a vehicle suspension system, using a gradient-based approximation method, Part 1: Mathematical modelling , 2009, Math. Comput. Model..

[23]  W. Sun,et al.  Ride-Comfort-Oriented Suspension Optimization Using the Pseudo-Excitation Method , 2010 .

[24]  Mohamad S. Qatu,et al.  Road Vehicle Dynamics , 2008 .

[25]  Mehdi Soleymani,et al.  Genetic optimization of a fuzzy active suspension system based on human sensitivity to the transmitted vibrations , 2008 .