Buzz, squeak, rattle noise that are referred as BSR have become important part in vehicle's quality because vehicle NVH(noise vibration harshness) reduction technology of main parts like engine and drive-line has made great progress except BSR noise. BSR test is progressed by composing many road excitation power spectrum density(PSD) profiles into a composite PSD profile. Shakers which are input by composite PSD profile make time histories(time-acceleration data) by aggregate of a large number of harmonics related with composite PSD profile in general. But when only composite PSD profile is input to shaker, the time histories from shakers exhibits Gaussian distribution characteristics and can't reflect all road excitation PSD profiles. In this study, we search other studies that try to solve the problems occurring when a shaker is input only PSD profile and analyze time histories resulted from BSR test to check ways of shaker's operation.

Because of environmental pollution and lack of resources, necessity of energy efficiency improvement and reduction of exhaust gas emission and CO2 have grown in importance. Therefore a lot of studies are conducted for HEV(hybrid electric vehicle) and PHEV(plug-in hybrid electric vehicle). In addition, automobile companies are researching and manufacturing HEV and PHEV. Due to cost and time problem, simulation is preferred than experimental test to find better component size for efficiency improvement. In this research, backward simulation program is developed base on Dynamic Programming. Using this simulation program, fuel economy sensitivities for each parameter are analyzed and compared. Fuel economy is measured for a combined cycle that is calculated from FTP-75 and HWFET cycle. The target parameters are front/rear power train efficiency, drag coefficient, vehicle mass, rolling resistance coefficient, tire radius, center of gravity. The most sensitive parameter is front power train efficiency and second is drag coefficient. Rear power train efficiency, vehicle mass, rolling resistance coefficient are third, forth and fifth. By comparing sensitivities, we can choose a better way to improve fuel economy of HEV.