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.

This study aims to investigate the impact of combustion chamber geometry and biodiesel on the performance of diesel engines under various load conditions. Simulations were conducted using AVL FIRE software, followed by experimental validation to compare the performance of the prototype Omega combustion chamber with the optimized TCD combustion chamber (T for turbocharger, C for charger air cooling, and D for diesel particle filter). This study utilized four types of fuels: D100, B10, B20, and B50, and was conducted under different load conditions at a rated speed of 1800 revolutions per minute (rpm). The results demonstrate that the TCD combustion chamber outperforms the Omega chamber in terms of indicated thermal efficiency (ITE), in-cylinder pressure, and temperature, and also exhibits a lower indicated specific fuel consumption (ISFC). Additionally, the TCD chamber shows lower soot and carbon monoxide (CO) emissions compared to the Omega chamber, with further reductions as the load increases and the biodiesel blend ratio is raised. The high oxygen content in biodiesel helps to reduce soot and CO formation, while its lower sulfur content and heating value contribute to a decrease in combustion temperature and a reduction in nitrogen oxide (NOx) production. However, the NOx emissions from the TCD chamber are still higher than those from the Omega chamber, possibly due to the increased in-cylinder temperature resulting from its combustion chamber structure. The findings provide valuable insights into diesel engine system design and the application of oxygenated fuels, promoting the development of clean combustion technologies.

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.