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A hybrid search optimization is presented in order to optimize hybrid laminated fibrous composite E-springs for vehicle suspension systems. This optimization is conducted with both of the geometrical configuration and laminate structure of the E-spring. A genetic algorithm along with a hill-climbing random-walk approach are used through a developed NURBS-based technique in order to conduct this optimization. A mathematical-modeling-based mid-ware technology is introduced in order to fully automate the optimization process through linking the run engines of mathematical modeling and finite element analysis from within the mathematical modeling engine. A hybrid approach of the inter-laminar shear stress and Tsai-Wu criteria is first implemented in order to identify failure indices of the resulting optimum shape and laminate structure.

It is well known that the excessive levels of vibration in heavy vehicles negatively affect driver comfortability, cargo safety and road condition. The current challenge in the field of suspension design for heavy vehicles is to optimize the suspension dynamic parameters to improve such requirements. Almost all of the previous work in this field is based on applying the mathematical optimization considering active or passive suspension systems to obtain the optimal dynamic parameters. In this work a new passive suspension systems for heavy trucks is suggested and compared with the conventional passive suspension systems. The new systems rely on transferring the vertical motion, (vibration), into horizontal motion through a bell-crank mechanism to be taken by a horizontal passive suspension system. The system dynamic parameters like body acceleration, suspension travel and dynamic tire load are calculated assuming random excitation due to road irregularities.