As a device for cooling charged air before it enters the cylinder, the intercooler is an indispensable part of the regular operation of a booster diesel engine. To solve the problem of the insufficient cooling performance of an intercooler for a high-power supercharged diesel engine, in this study, the flow field in the intercooler is simulated using the computational fluid dynamics (CFD) model of porous media, and the performance data measured using the steady flow test bench are used to provide boundary conditions for the calculation. The effects of the charged air mass flow rate and the tube bundle’s transverse spacing on the heat dissipation performance of the intercooler are analyzed and compared. The calculation results show that, under the condition of satisfying the regular operation of the diesel engine, the heat transfer coefficient of the intercooler heat dissipation belt increases with the increase in air mass flow and the spacing of cooling pipes, and the heat transfer coefficient can be increased by up to 57%. Still, excessive spacing of the cooling water pipes increases pressure loss in the charged air. Finally, the transverse spacing of the tube bundle is set to 17 mm, ensuring the pressure drop in the charged air, and the heat dissipation performance of the intercooler is increased by 6.04%. This paper provides a feasible solution for further optimizing the heat dissipation performance of intercoolers. Finally, grey correlation theory is used to study the correlation between air mass flow, cooling water pipe spacing, and intercooler heat dissipation performance. The correlation values are 0.8464 and 0.8497, respectively, indicating a significant relationship between air mass flow, cooling water pipe spacing, and intercooler heat dissipation performance.

The high-pressure common rail (HPCR) injection system, a key technology for enhancing diesel engine performance, plays a decisive role in ensuring fuel injection precision and combustion efficiency through rail pressure stability. This study establishes a coupled simulation model of an electronically controlled HPCR injection system and a diesel engine, using GT-Suite to systematically investigate the effects of fuel supply pressure, camshaft speed, high-pressure pump plunger parameters, and inlet and outlet valve characteristics on rail pressure fluctuations. Gray relational analysis quantifies the correlation between these factors and rail pressure variations. The results demonstrate that increasing camshaft speed, injection pulse width, plunger mass, plunger length, plunger spring preload, inlet valve spring preload, and outlet valve body mass reduces rail pressure fluctuations, while variations in fuel supply pressure, plunger spring stiffness, and valve spring stiffness have minimal impact. Notably, the influence of outlet valve spring preload, inlet valve spring stiffness, and inlet valve body mass on rail pressure is nonlinear, with optimal values observed. Gray relational analysis further identifies inlet valve spring preload as having the highest correlation with rail pressure fluctuations (0.815), followed by inlet valve spring stiffness (0.625), with outlet valve spring preload (0.551) and stiffness (0.527) showing relatively lower correlations. This study provides valuable insights for optimizing the HPCR injection system design and contributes to advancements in diesel engine technology.

In this paper, the negative impact of the charging load generated by the disorderly charging scheme of large-scale pure electric vehicles on the operation performance of the power grid system and the problem of reducing its charging energy efficiency are studied and analyzed. First, based on Matlab 2022a simulation software and the Monte Carlo random sampling method, the probability density model of the factors affecting the charging load is constructed, and the total charging load of different quantities is simulated. Second, the IEEE33-node distribution network model is introduced to simulate the influence of charging load on the grid under different permeability schemes. Finally, the multi-objective genetic algorithm is used to optimize the charging cost and battery life. Taking the 20% permeability scheme as an example, the research results show that, compared with the disorderly charging scheme, the multi-objective optimization scheme reduces the peaking valley difference rate by 24.34%, the charging load power generation cost by 29.5%, and the charging cost by 23.9%. The power grid profit increased by 45.8%, and the research conclusion has practical significance for the energy efficiency optimization of pure electric vehicles.