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Many design problems are discovered often late in the development process, when design flexibility is limited. It is the art of the refinement engineers to find a solution to any unpredicted issues at this stage. The refinement process contains many hours of testing and requires many prototypes. Having an accurate experimental model of the system in this phase could reduce refinement time significantly. One of the areas that usually require refinement and tuning late in the design process is engine and body mounting systems. In this paper, we introduce a technique to optimize the mounting system of a vehicle for a given objective function using experimental/numerical analysis. To obtain an accurate model of the vehicle, we introduce an experimental procedure based upon the substructuring method. The method eliminates the need for any accurate finite element method of the vehicle. Experimental results of the implementation of this approach to a real vehicle are presented.

Ride performance characteristics of a road vehicle involving different suspension system layouts are investigated. The suspension layouts consist of conventional rectangular 4-wheel, novel diamond-shaped 4-wheel, triangular 3-wheel and inverse-triangular 3-wheel. A generalized full-vehicle model integrating different suspension system layouts is formulated. The fundamental suspension properties are compared in terms of bounce-, roll- and pitch-mode. The ride dynamic responses and power consumption characteristics are explored under two measured road roughness excitations and a range of vehicle speeds. The results demonstrate that the novel diamond-shaped suspension system layout could yield significantly enhanced vehicle ride performance in an energy-saving manner.

This paper seeks to identify the refrigeration load of a hybrid electric truck in order to find the demand power required by the energy management system. To meet this objective, in addition to the power consumption of the refrigerator, the vehicle mass needs to be estimated. The Recursive Least Squares (RLS) method with forgetting factors is applied for this estimation. As an example of the application of this parameter identification, the estimated parameters are fed to the energy control strategy of a parallel hybrid truck. The control system calculates the demand power at each instant based on estimated parameters. Then, it decides how much power should be provided by available energy sources to minimize the total energy consumption. The simulation results show that the parameter identification can estimate the vehicle mass and refrigeration load very well which is led to have fairly accurate power demand prediction.