A reasonable inflating strategy must be developed for filling an onboard hydrogen storage tank with hydrogen gas. The inflow hydrogen temperature has always been a constant value in filling cases. However, in our opinion, the optimal inflow hydrogen temperature is not supposed to be a fixed value but a value that constantly changes and adjusts with filling time, i.e., the inflow hydrogen temperature is a function of the filling time. How to determine this functional relationship is a critical problem to be addressed. Herein, an approach is introduced. A dual-zone model is presented to research the thermal effect during the process of charging hydrogen storage tanks. Based on the numerical results of the dual-zone model, the charging process was divided into three stages, allowing us to obtain data for 1331 filling cases. Then, a back propagation (BP) neural network model was built to analyze the data, and the implicit relationship between the inflow hydrogen temperatures and maximum hydrogen temperature pressure could be deduced. With this implicit relationship, the critical values of the inflow hydrogen temperatures can be obtained from the critical situation. Suppose the inflow hydrogen temperatures in a practical case are higher than the critical values. In that case, the highest hydrogen temperature in the tank will exceed the limited safety value of 358 K. In contrast, if the inflow hydrogen temperatures are lower than the critical values, then more energy will be needed to precool the inlet hydrogen temperature. Thus, theoretically, the critical inflow hydrogen temperatures should be at their optimal values.

In this study, a bench test was conducted employing the Worldwide Harmonized Light-duty Vehicles Test Cycle (WLTC) to investigate the emission rates of hydrocarbons (HCs), carbon monoxide (CO), and carbon dioxide (CO₂) with two different gasolines and five gasoline vehicles. The results indicated that compared with X gasoline, X+ gasoline led to a reduction in the emission rates of HC, CO, and CO₂, by 38%, 11%, and 7%, respectively, attributed to its lower aromatic hydrocarbon content, olefin content, and 90% evaporation temperature (T90), and higher oxygen content. X+ gasoline exhibited more emission reductions under both acceleration and deceleration conditions. The two gasolines showed consistent patterns: for X+ gasoline, the emission rates under acceleration conditions were significantly higher than those under deceleration conditions, by a factor of 14.9, 2.1, and 1.6 for HC, CO, and CO₂, respectively. Stronger Spearman correlations between vehicle specific power (VSP) and the emission rates were observed at higher speed (>80 km/h) of X, than those at medium speed (40–80 km/h) and lower speed (≤40 km/h), for both gasolines. Overall, the grey relation analysis revealed obvious heterogeneity between each of the seven fuel properties (RON, T10, T50, T90, Oxygen content, Aromatics content, Olefin content) and each of the three emission rates. However, slightly higher relational degrees were observed between HC emissions and aromatics or olefin contents, highlighting the need for lowering aromatics and olefin contents, thus reducing HC emissions.

To promote the green and low-carbon transition and achieve sustainable development in the transportation sector, virtual calibration technology was employed for the efficient and precise control of emissions from heavy-duty diesel engines and aftertreatment systems. A data-driven, semi-empirical and semi-physical simulation modeling method was proposed. By constructing core modules based on physical mechanisms and refining empirical parameters using experimental data, the method improves computational efficiency while maintaining the prediction accuracy of key parameters. Additionally, a collaborative architecture combining physical actuators and virtual sensor signals was introduced, laying the foundation for the validity of virtual calibration. By innovatively introducing a closed-loop system with real actuators and virtual sensors, the dynamic response characteristics of the control system are faithfully reproduced, providing a reliable environment for validating the results of virtual calibration. Under steady-state conditions, the results demonstrated an average relative error of 1.7% for brake-specific fuel consumption (BSFC) and 6.1% for NOx emissions. An open-loop system for the virtual calibration testing platform was constructed for steady-state calibration. Using the main injection timing and common rail pressure as independent variables, a D-optimal design was utilized to generate 43 sets of experimental points, from which a polynomial regression model was established (R² ≥ 98%). Under the constraints of NOx and pre-turbine temperature, fuel consumption in the low-load range is reduced by 0.5–3 g/kW·h, aftertreatment NOx emissions are reduced by 0.5–3 g/kW·h, and exhaust temperature is increased by 10 °C. This study establishes a complete development workflow consisting of “operating condition design-virtual optimization-bench validation,” significantly enhancing calibration efficiency and engineering applicability. This method shortens the calibration cycle and reduces the number of physical bench tests, providing the industry with a comprehensive calibration methodology tailored to engine operating conditions that is both reproducible and scalable.