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Based on the similarity theory and conservation equations, some of the important dimensionless numbers in diesel combustion are deduced and discussed. Existence of similarity is theoretically proved in diffusion (or mixing-controlled) combustion and premixed combustion as well as in spray mixture formation processes in different size diesel engines. With the prerequisite of geometric similarity, scaling rules for some parameters including engine speed, injection pressure and injection duration are established to realize the similarity between large-bore and small-bore diesel engines. To verify the similarity theories, the computational fluid dynamics (CFD) simulation are conducted, and three scaling rules, which keep the engine speed, injection pressure and lift-off length constant, respectively, are compared under the conditions of the light load (0.3 MPa IMEP) and high load (1.55 MPa IMEP) operations.

Owing to the stochastic nature of engine knock, determination of the knock limited spark angle (KLSA) is difficult in engine cycle simulation. Therefore, the state-of-the-art knock modeling is mostly limited to either merely predicting knock onset (i.e. auto-ignition of end gas) or combining a simple unburned mass fraction (UMF) model representative of knock intensity (KI). In this study, a newly developed KLSA model, which takes both predictions of knock onset and intensity into account, is firstly introduced. Multiple variables including the excess air ratio, EGR ratio, cylinder pressure and the end gas temperature are included in the knock onset model. Based on the auto-ignition theory of hot spots in end gas, both the energy density and heat release rate in hot spots are taken into consideration in the KI model.

Excellent energy consumption performance of a plug-in hybrid electric vehicle (PHEV) is usually attributed to its hybrid drive mode. However, the factors including vehicle performance, driver behavior and traffic status have been shown to cause unsatisfactory performance. This phenomenon leads to a necessity of study on energy consumption control strategies under real-world driving conditions. This paper proposes a new approach for energy management optimization of plug-in hybrid electric vehicles based on real-world driving data for two purposes. One is for improving the energy consumption of PHEV under real-world driving conditions and the other is for reducing the computational complexity of optimization methods in simulation models. In this process, the paper collected real-world driving record data from 180 drivers within 6 months. Then the principal component analysis (PCA) was employed to extract and define the hidden factors from the initial real-world driving data.