In order to improve the starting performance of a 3-cylinder gasoline engine and meet the requirements of peak speed

according to the optimal Latin hypercube design method and the ellipsoid-based neural network model(EBFNN)

the relationship among starting time

peak speed and starting control parameters

such as starting fuel pressure threshold and starting fuel injection enrichment factor was established. The accuracy of the ECFNN model was verified by 20 sets of sample data

and the non-dominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ）was used to optimize starting time and peak speed. Optimization results show that the starting time is reduced by 20.29%

and the starting peak speed is increased by 1.93%

which is 300~500 r/min larger than the target idle speed. After optimization

the increase of THC

CO and PN is less than 10%

while NOx is reduced by 3.2% coupled to the reduction of fuel consumption of 16.60%

which can well meet the development requirements of the prototype.