Multi-objective rapid optimization method for a variable-stroke valve train device

Abstract

Raising the maximum valve lift in two-stroke drive mode is the key to increasing the power output of the variable-stroke engine, so the limit design of the variable-stroke valve train device is critical. The multi-objective genetic optimization algorithm NSGA-2 was coupled with the single-freedom dynamics model of the variable-stroke valve train device. A multi-objective rapid optimization program for the valve train device was constructed in Matlab. Two optimization methods explored the maximum valve lift limit in two-stroke drive mode. The first was to optimize the rocker structure, and the second was to optimize the cam position and the rocker structure together. The results show that the single-freedom dynamics model has sufficient computational accuracy. The collaborative optimization results of the cam position-rocker arm structure are more optimal. Under the same maximum lift of the valve, compared to the results of individual optimization of the rocker arm structure, the maximum contact force is reduced by up to 285 N. The maximum contact stress is reduced by up to 63 MPa through collaborative optimization. Compared with the original case, the maximum increase rate of the maximum valve lift obtained through optimizing the rocker structure is 12.45%, while that obtained through collaborative optimization is 12.85%.