Combining GA-SVM and NSGA-III multi-objective optimization to reduce the emission and fuel consumption of high-pressure common-rail diesel engine

This research proposed a multi-objective optimization approach that combines Non-dominated Sorting Genetic Algorithms (NSGA) III and support vector machine (SVM) to reduce diesel engine emissions while enhancing economic performance and calibration efficiency. In order to obtain accurate experimental data on diesel en-gines, a space-filling design method was proposed based on the prediction modeling of diesel engine perfor-mance. The SVM prediction model for diesel engine performance was established. A genetic algorithm (GA) was introduced to optimize the SVM model's penalty factor and radial basis parameters, thereby improving its prediction accuracy. The multi-objective optimization approach optimized the braking specific fuel consumption (BSFC), NOx , and CO. The results show that: the GA-SVM diesel engine performance prediction model has excellent prediction performance and generalization ability for BSFC, NOx , and CO, with R2 values of 0.981, 0.979, and 0.968, respectively. GA-SVM was used to evaluate the fitness of the NSGA-III optimal set. This not only ensures optimization accuracy but also improves working efficiency. After optimization, the BSFC of the diesel engine was reduced by 1.67%, NOx emission was reduced by 27.01%, CO emission was reduced by 19.15%, and noticeable optimization results were obtained. This work has important reference value for the automatic calibration of diesel engine control parameters, improving the economy and emission of diesel engines.