For diesel engines, accurate prediction of NO_x (Nitrogen Oxides) emission plays an essential role in virtual NO_x sensor development and engine design under situations of actual road driving. However, due to the randomness and uncertainty in the driving process of diesel vehicles, it is difficult to make predictions about NO_x emissions. In order to solve this problem, this paper proposes differential models for noise reductions of NO_x emissions in time series. First, according to the internal fluctuation of time series, use SSA (Singular Spectrum Analysis) to reduce the noises of the original time series; second, use ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to decompose the noise-reducing data into several relatively stable subsequences; third, use the sample entropy to calculate the complexity of each subsequence, and divide the sequences into high-frequency ones and low-frequency ones; finally, use GRU (Gated Recurrent Unit) to complete the prediction of high-frequency sequences and SVR (Support Vector Regression) for the prediction of low-frequency sequences. To obtain the final models, integrate the prediction results of the subsequences. Make comparisons with five single models, SSA single-processing models, and ICEEMDAN single-processing models. The experimental results show that the proposed model can predict the instantaneous NO_x emissions of diesel engines better than the single model and the model processed by SSA, and the differentiated model can effectively improve the execution speed of the model.

In order to deal with many influence factors of electric vehicles in driving under complex conditions, this paper establishes the system state equation based on the longitudinal dynamics equation of vehicle. Combined with the improved Sage–Husa adaptive Kalman filter algorithm, the road slope estimation model is established. After the driving speed and rough slope observation are input into the slope estimation model, the accurate road slope estimation at the current time can be obtained. The road slope estimation method is compared with the original Sage–Husa adaptive Kalman filter road slope estimation method through three groups of road tests in different slope ranges, and the accuracy and stability advantages of the proposed algorithm in road conditions with large slopes are verified.

The geometric alignment of highways directly affects the fuel consumption of motor vehicles. To analyze the impact of the road gradient on fuel consumption, actual road tests were conducted in plateau and mountainous areas. Geographic Information Systems (GISs) were used to calculate road gradients, and Vehicle Specific Power (VSP) distributions were obtained through testing, serving as inputs for the motor vehicle emission simulator (MOVES) model. Finally, the simulation results were verified against the experimental results. The findings indicate a strong positive correlation between road gradients ranging from −5% to +5%, the VSP, and fuel consumption. At a constant gradient, the fuel consumption rate increases with the vehicle speed; the fuel consumption factor is lowest at 60 km/h and highest at 40 km/h. Under both constant and actual driving speeds, when the absolute values of uphill and downhill gradients are the same, the average fuel consumption for both uphill and downhill driving shows that, at gradients of 1% to 3%, the fuel savings from downhill driving can offset the additional fuel consumption on uphill driving. At gradients of 4% to 6%, the increase in fuel consumption on uphill driving surpasses the savings from downhill driving. During uphill climbs, lower speeds within the mid-to-low range result in lower fuel consumption and greater reserve power. The MOVES model demonstrates good adaptability in plateau and mountainous areas.

This paper investigated the physical and chemical properties of gasoline direct injection (GDI) engine particulate matter (PM). The physical properties mainly included the particulate aggregate morphology, primary particle size, and internal nanostructure. High-resolution transmission electron microscopy (HRTEM) and scanning electron microscopy (SEM) were used to obtain particle morphology information and to conduct image processing and analysis. The chemical characterization tests included X-ray photoelectron spectroscopy (XPS), energy dispersive scanning (EDS), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and thermal gravimetric analysis (TGA). XPS can be used to observe the content of carbon and oxygen components and the surface carbon chemistry status, EDS can be used to obtain the elemental composition of particles, and TGA is used to analyze the oxidative kinetics of particles. Samples were collected from the exhaust emissions of a passenger vehicle compliant with China’s VI emission standards under China Light-Duty Vehicle Test Cycle (CLTC) test conditions. The study found that the particle morphology mainly comprised primary particles stacked on top of each other to form agglomerate structures, and the primary particles exhibited a core–shell structure. Analysis showed that carbon and oxygen were the predominant components of the particles, with other metallic elements also present. The XPS observations agreed with the FTIR results, indicating a small amount of oxygen was present on the particle surface and that the carbon components consisted mainly of sp2 hybridized graphite and sp3 hybridized organic carbon. The TGA results indicated high characteristic temperatures and low oxidation activity.

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle (PHEV) types: one Series Hybrid Electric Vehicle (S-HEV), one Parallel Hybrid Electric Vehicle (P-HEV), and one Series-Parallel Hybrid Electric Vehicle (SP-HEV), during real driving conditions. The findings show a correlation between acceleration and increased CO₂ emissions for P-HEV, while acceleration has a relatively minor impact on S-HEV and SP-HEV emissions. Under urban driving conditions, the SP-HEV displays the lowest average CO₂ emission rate. However, under suburban and highway conditions, the average CO₂ emission rates follow the order S-HEV > SP-HEV > P-HEV. An analysis of CO₂ emission factors across different road types and vehicle-specific power (VSP) ranges indicates that within low VSP intervals (VSP ≤ 0 for urban, VSP ≤ 5 for suburban, and VSP ≤ 15 for highway roads), the P-HEV exhibits the best CO₂ emission control. As VSP increases, the P-HEV’s emission factors rise under all three road conditions, with its emission control capability weakening when VSP exceeds 5 in urban, 15 in suburban, and 20 on highway roads. For the SP-HEV, CO₂ emission factors increase with VSP in urban and suburban areas but remain stable on highways. The S-HEV shows minimal changes in emission factors with varying VSP. This research provides valuable insights into the CO₂ emission patterns of PHEVs, aiding vehicle optimization and policy development.