This paper describes the development and use of a multi-body co-simulation approach for predicting the dynamic response of a vehicle containing magnetorheological (MR) semi-active dampers. The approach is used to investigate the effects of various local and global control strategies on the load histories of suspension components for the purpose of assessing their likely impact on fatigue life. The approach adopted aims to exploit the capability of a multi-body system (MBS) code and a mathematical simulation code, by integrating the MBS vehicle models with selected semi-active damper/controller models. Various MBS vehicle models are developed of increasing complexity using MSC.visualNastran, which are linked to three local, two-state switchable, control algorithms and also two global controllers, each developed in MATLAB/Simulink. The control strategies are implemented within the vehicle model using an MR damper model derived from experimental test data. Road inputs, including both bump/pothole and random road excitation, and the tyre model are also implemented within MATLAB/Simulink. Ultimately, the aim is to develop an approach which would allow concurrent structural optimization and controller optimization to enable lighter and more durable suspension components to be produced.
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.
In an effort to reduce cost involving repetitive prototype build-test cycles, it is inevitable that simulation on full vehicle will be carried out during the product development stage. Desired suspension kinematic profiles of a given vehicle parameter are often unknown at the initial design stage. This paper demonstrates a simple methodology to obtain optimized kinematic characteristics against quality of handling performance using this model as predictive model in earliest design stage. A full vehicle model that is inclusive of suspension kinematic profiles and nonlinear damper profiles has been derived to enable the engineer to study the characteristics of the nonlinear elements against the vehicle performance when only limited vehicle data are available in the initial stage. Results suggest that the handling characteristics of a vehicle are sensitive to the changes in suspension kinematic profile. Additionally, the proposed vehicle model is able to provide satisfactory handling objective when measured in transient handling and frequency response compared to other vehicle models. A robust prediction model of the vehicle responses in frequency domain is proposed. It is coupled with the vehicle model employed as predictive model to optimize front toe angle profile against vehicle quality of handling performance measured in frequency domain. Keywords 10-degree-of-freedom full vehicle model, suspension kinematic profiles, design of experiment, vehicle handling Date
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