A seven degree-of-freedoms human body-seat-suspension model was built for multi-objective optimization of vehicle ride dynamics behavior. Biomechanical models of human body and elastic model of seat cushion were integrated with classical 1/4 car model. The root mean square values of head acceleration of human body, together with suspension work space and dynamic tire load, were selected as objective functions of optimization. Non-dimension method was introduced into the formulation of objective functions so that optimization could be independent of different running conditions. Parameter sensitivity analysis was utilized to explore the relation between objective functions and parameters of suspension and seat cushion. Based on the results of analysis, design variables were determined. Non-dominated Sorting Genetic Algorithm - II was used in this multi-objective optimization problem to compute Pareto optimal set and Pareto frontier. Results indicate that Pareto frontier includes two parts. These two parts have the nearly same range of dynamic tire load and share partial range of suspension work space in objective function space. In design variable space, two parts respectively correspond to two different distribution areas of Pareto optimal solution set. So, for the same expected objective, parameters of suspension and seat cushion usually have at least one available combination, which improves the flexibility of optimal design.

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