TY - GEN
T1 - Multi-objective optimization of suspension and seat for vehicle ride dynamics behavior using NSGA-II method
AU - Song, Kang
AU - Chen, Xiao Kai
AU - Lin, Yi
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84903444732&partnerID=8YFLogxK
U2 - 10.1115/IMECE2013-63093
DO - 10.1115/IMECE2013-63093
M3 - Conference contribution
AN - SCOPUS:84903444732
SN - 9780791856420
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - Transportation Systems
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2013 International Mechanical Engineering Congress and Exposition, IMECE 2013
Y2 - 15 November 2013 through 21 November 2013
ER -