针对车用汽油机标定成本高、开发周期长等问题，提出一种车用汽油机的标定优化方法，以提高标定效率。建立发动机稳态工况的高斯过程回归模型，通过少量的试验数据预测燃油消耗率(BSFC)、氮氧化合物(NO_x)等发动机性能和排放参数，并引入决定系数(R²)评估模型质量。在非支配排序中加入了约束违反并引入混合交叉算子对NSGA-Ⅱ进行改进。将预测模型作为改进NSGA-Ⅱ的目标函数，进行发动机标定优化。采用一种平滑校准图自动生成的方法，根据校准图性能和平滑度，以自动方式选择最终校准图。结果表明：对于标准测试函数，改进NSGA-Ⅱ算法的分布性最高提升21.41%，应用于发动机标定优化，为决策者提供了更多的优选方案。将改进的NSGA-Ⅱ算法与平滑校准图自动生成方法结合，在WLTC循环工况下，标定结果相较于人工标定校准图，燃油消耗率降低了1%，NO_x排放降低了5%，为减少发动机标定工作量提供了参考。

In order to solve the problems of high calibration cost and long development cycle of vehicle gasoline engine, a calibration optimization method for vehicle gasoline engine was proposed to improve the calibration efficiency. Under steady state conditions of engine, Gaussian process regression model was applied to predict engine performance and emission parameters such as brake specific fuel consumption(BSFC) and nitrogen oxides(NO_x） through a small amount of test data, and a determination coefficient(R²) was introduced to evaluate the quality of model. Constraint violation was added to nondominated sorting and hybrid crossover operator was used to improve NSGA-Ⅱ. Taking the prediction model as the objective function of improved NSGA-Ⅱ, the calibration optimization of engine was carried out. An automatic generation method of smooth calibration map was employed, and the final calibration map was automatically selected according to the performance and smoothness of calibration map. The results show that the distribution of the improved NSGA-Ⅱ algorithm improves by 21.41% for the standard test function, and more optimal solutions of engine calibration are provided for decision makers after application. Combining the improved NSGA-Ⅱ algorithm with the automatic generation method of smooth calibration map, the calibration results show that the fuel consumption decreases by 1% and the nitrogen oxides decrease by 5% under the WLTC cycle condition compared with the manually-calibrated calibration map. It provides a reference for reducing the workload of engine calibration.