Sensitivity Analysis Study of Engine Control Parameters on Sustainable Engine Performance

With the increasing global concern for environmental protection and sustainable resource utilization, sustainable engine performance has become the focus of research. This study conducts a sensitivity analysis of the key parameters affecting the performance of sustainable engines, aiming to provide a scientific basis for the optimal design and operation of engines to promote the sustainable development of the transportation industry. The performance of an engine is essentially determined by the combustion process, which in turn depends on the fuel characteristics and the work cycle mode suitability of the technical architecture of the engine itself (oil-engine synergy). Currently, there is a lack of theoretical support and means of reference for the sensitivity analysis of the core parameters of oil–engine synergy. Recognizing the problems of unclear methods of defining sensitivity parameters, unclear influence mechanisms, and imperfect model construction, this paper proposes an evaluation method system composed of oil–engine synergistic sensitivity factor determination and quantitative analysis of contribution. The system contains characteristic data acquisition, model construction and research, and sensitivity analysis and application. In this paper, a hierarchical SVM regression model is constructed, with fuel physicochemical characteristics and engine control parameters as input variables, combustion process parameters as an intermediate layer, and diesel engine performance as output parameters. After substituting the characteristic data into the model, the following results were obtained, R 2 > 0.9, MSE < 0.014, MAPE < 3.5%, indicating the model has high accuracy. On this basis, a sensitivity analysis was performed using the Sobol sensitivity analysis algorithm. It was concluded that the load parameters had the highest influence on the ID (ignition delay time), combustion duration (CD), and combustion temperature parameters of the combustion elements, reaching 0.24 and above. The influence weight of the main spray strategy was greater than that of the pre-injection strategy. For the sensitivity analysis of the premix ratio, the injection timing, EGR (exhaust gas recirculation) rate, and load have significant influence weights on the premix ratio, while the influence weights of the other parameters are not more than 0.10. In addition, the combustion temperature among the combustion elements has the highest influence weights on the NOx, PM (particulate matter) concentration, and mass, as well as on the BTE (brake thermal efficiency) and BSFC (brake specific fuel consumption). The ID has the highest influence weight on HC and CO at 0.35. Analysis of the influence weights of the index parameters shows that the influence weights of the fuel physicochemical parameters are much lower than those of the engine control parameters, and the influence weights of the fuel CN (cetane number) are about 5% greater than those of the volatility, which is about 3%. From the analysis of the proportion of index parameters, the engine control parameter influence weights are in the following order: load > EGR > injection timing > injection pressure > pre-injection timing> pre-injection ratio.