As a critical component in automotive engines, the valve seat ring directly correlates with the engine service life.However, limitations such as insufficient wear resistance and susceptibility to oxidation during operation restrict the durability of valve seat rings.Surface modification during the manufacturing process has emerged as an effective solution to address these challenges.Current research on surface modification techniques for valve seat rings lacks systematic summarization.To bridge this gap, an overview of surface modification technologies and their advancements in this field was conducted. Firstly, the working environment of valve seat rings and research progress in commonly used materials were comprehensively analyzed. Subsequently, it focused on the effects of surface modification techniques（including metal nitride films, laser cladding coatings, and heat treatment processes） on the wear resistance and high-temperature oxidation resistance of valve seat rings. It further explored the relationship between these techniques and the performance optimization of different categories of seat ring materials. Finally, it summarized current achievements and proposed future directions for surface modification of valve seat rings.

To address the challenges of slow combustion speed and high NO_x emissions associated with ammonia as a zero-carbon fuel in engines, simulation research was conducted on the combustion and emission characteristics of  ammonia-hydrogen and ammonia-methane dual-fuel engines. One-dimensional simulation model （the core index error for model validation of less than 7%) was established using GT-POWER software based on the structural parameters of a 12.8 L natural gas engine. The effects of compression ratio and blending gas volume fraction on in-cylinder peak pressure (p_max), exhaust temperature, NO_x emissions, thermal efficiency were systematically investigated under different engine speeds. The results indicate that increasing both compression ratio and blending ratio can effectively enhance p_max and combustion speed, but would lead to lower exhaust temperature and higher NO_x emissions. Increasing the proportion of hydrogen or methane can significantly reduce fuel consumption rate and improve fuel economy.

To meet the requirements of China Ⅵ emission regulations, the large displacement diesel engines for current domestic trucks generally use the technical route of electronically controlled high pressure common rail+pressurized intercooling+efficient SCR+DOC+DPF+intake thermal management control (IAT) or exhaust thermal management control (ETV). The purpose of IAT and ETV control is to accurately control the exhaust temperature, and therefore balance emissions and fuel economy. Different thermal management control strategies will have different impacts on engine and vehicle performance. Taking a vehicle with ETV technology route and abnormal noise as the research object, the effects of different ETV thermal management schemes on engine emissions, vehicle emissions and vehicle noise were studied. And the improvement of vehicle noise by ETV control combined with vehicle intake system optimization was studied, so as to find a scheme to reduce vehicle noise and improve vehicle comfort.

A heavy-duty natural gas engine meeting China Ⅵ emission standard was taken as the research object. According to the test conditions of WHTC cycle in GB 17691, the effects of exhaust temperature, excess air coefficient and exhaust components on NH₃ and N₂O emissions were investigated at Kunming area. The results show that NH₃ and N₂O mainly generate when the excess air coefficient is small. NH₃ genertes in large quantities at the engine exhaust temperature from 109 ℃ to 326 ℃, while N₂O generates especially at the temperature less than 350 ℃, especially in the cold start stage. The generation of NH₃ and N₂O is greatly affected by the exhaust components of natural gas engine. Under the appropriate exhaust temperature, NH₃ and N₂O emissions increase with the increase of CO, THC and NO emissions. N₂O emission increases and NH₃ emission decreases when SO₂ emission increases. In the WHTC cycle test, NH₃ and N₂O generate in large quantities after 60 s of the start of WHTC cycle test, the generation amount fluctuates greatly in the urban condition and quickly decreases in the suburban condition.

To study the impact of road conditions and gradients on fuel consumption and pollutant emission characteristics of light-duty gasoline vehicles in plateau environment, a China Ⅵ light-duty gasoline vehicle was selected as the subject. Based on the custom RS 803 test cycle that was independently developed by coupling "speed and gradient" by using real-world road operation data, fuel consumption and pollutant emission tests were conducted under normal temperature conditions at the Light-Duty Vehicle Environmental Simulation Emission Laboratory of the National Plateau Motor Vehicle Quality Inspection and Testing Center (altitude 1 914 m). By comparing test conditions with and without gradients, the influences of vehicle operating conditions and road gradients on fuel consumption and pollutant emission characteristics of light-duty gasoline vehicles were analyzed. The results indicate that the relative deviations in CO₂ emissions and fuel consumption between the RS803 cycle with and without gradients are 1% and 2% respectively, suggesting that road gradients have a minor impact on vehicle fuel consumption. However, gradients have a significant effect on pollutant emissions. Under gradient conditions, THC, CO, N₂O and NO_x emissions increase by 27%, 58%, 50% and 3% respectively compared to conditions without gradients. The fuel consumption and CO₂ emissions show a strong positive correlation with speed, acceleration and vehiclespecific power (VSP), with correlation coefficients of 0.55 and 0.53 for VSP respectively. CO emissions exhibit an extremely strong correlation with speed, and higher speeds will lead to more CO emissions. N₂O and NO_x emissions show lower correlations with these factors. Optimizing combustion strategies under highspeed conditions can reduce CO emissions, but this requires balancing the local increasing trends of NO_x and N₂O.

Three-dimensional simulation technology is widely employed in the development of high-power density diesel engines due to its efficiency and cost-effectiveness. However, its application is often constrained by the lack of effective methods for evaluating the accuracy of simulation models. Taking a constant-volume diesel spray simulation model as the subject, the sensitivity coefficient method was used to determine the influence trend and degree of 13 key parameters such as initial spray conditions, breakup models and turbulence models on both liquid and vapor phase spray penetration distances. Based on the analysis, the simulation model was calibrated through optimal parameter configuration, and its accuracy was evaluated using statistical methods. The results indicate that the simulation accuracy for liquid phase penetration reaches 93.11%, while that for vapor phase penetration reaches 95.95%. The proposed model calibration and accuracy evaluation methodology can be extended to the simulation of diesel engine working processes, providing theoretical support and technical reference for the practical application of three-dimensional simulation technology in the development of high-power density diesel engines.

In order to accurately reflect the actual kinematic characteristics of vehicles and open up new directions for driving cycle acquisition, generation and recognition, a new energy vehicle driving cycle generation and recognition method was proposed based on unit driving conditions. Kernel principal component analysis and Gaussian mixture model clustering were used to classify the kinematic fragments of pretreated driving cycle. Based on the Markov chain, the fragment state division and characteristic condition construction were carried out for various unit condition groups, and the customized unit driving condition was constructed by the operating condition generation model according to various characteristic parameters. Using the aforementioned method as the training sample source, a driving condition recognition model was established based on a backpropagation neural network. The results show that the constructed driving conditions exhibit an average error of only 2.69% compared to the feature parameters of original sample data. Customized unit conditions show an average error of merely 1.85% relative to the corresponding category sample data feature parameters. The neural network driving condition recognition model achieves an overall accuracy of 93.8%.

The state of charge (SOC) estimation for lithium-ion batteries is highly sensitive to temperature. Traditional methods based on equivalent circuit models require offline open circuit voltage (OCV) testing at different temperatures to obtain OCV-SOC curves, and the process is extremely time-consuming. Meanwhile, large data-based methods necessitate collecting large amounts of feature data at various temperatures for model training. Both approaches are difficult to apply for online estimation. A new online SOC estimation method across different temperatures was propesed. An incremental support vector machine (ISVM) models the terminal voltage, while online collection of mini-batch data enables dynamic model parameter updates. And an adaptive unscented Kalman filter (AUKF) ensures closed-loop estimation. Tests under federal urban driving schedule (FUDS) at 0 ℃, 25 ℃, and 45 ℃ show that the method achieves SOC estimation errors within 0.02%.Compared to conventional methods, the mean absolute error and root mean square error reduce by 1.09 percentage points and 1.10 percentage points, respectively. The method also exhibits strong robustness to initial SOC values.

Dual-planetary hybrid electric vehicles excel in fuel efficiency and emissions due to the complete decoupling of engine from vehicle speed. However, the complex structures pose challenges in coordinating power sources during multi-mode operation. A dual-planetary gear hybrid system was designed based on the equivalent tree graph method and a co-simulation model was established by combining AVL CRUISE and Simulink. A dynamic chaos sine-cosine multi-objective particle swarm optimization (DCSC-MOPSO) algorithm was proposed to perform multi-objective optimization targeting power, fuel economy, and smoothness. The simulation results demonstrate that the DCSC-MOPSO algorithm exhibits significant advantages in Pareto solution set search and objective balancing. Compared to the initial solution, the power, fuel economy, and smoothness improve by an average of 39.79%, 17.77%, and 25.24%, respectively, indicating a substantial enhancement in the overall system performance.

Taking the connecting rod small end bearing of non-road high pressure common rail diesel engine as the research object, the eddy current method-based axial trajectory testing system was built, and the data acquisition software was developed. The axial trajectory testing and analysis of connecting rod small end bearing were carried out under idle condition. The results show that the axial trajectory testing based on eddy current method realizes the synchronous acquisition of eddy current and crankshaft angle signal. Under the idle condition, the oil film thickness of bearing fluctuates periodically in the range of 0-20 μm, the peak value appears in the intake and exhaust stages, and the minimum oil film thickness is 0.88 μm and appears at 21° after the in-cylinder maximum combustion pressure point. The motion of bearing axial trajectory is primarily characterized by vertical oscillation, and the main load-bearing zones of bearing distribute in the circumferential intervals of 60°-90° and 270°-300°.

Traditional fault diagnosis methods relying on single-modality feature extraction suffer from issues such as loss of fault information, limited representation of fault states and low fault recognition rates. Fault diagnosis method of engine gear transmission was hence proposed based on an optimized bidirectional gated recurrent unit (BiGRU) and Swin Transform. The Gramian Angular Difference Field (GADF) was first employed to transform the acquired one-dimensional vibration signal into a two-dimensional image with time-frequency features. Then a dual-channel feature extraction network was designed, Channel 1 employed a Swin Transformer network to extract time-frequency image features from the signal, while Channel 2 utilized an improved BiGRU-ATT network with an attention mechanism to capture deep temporal features of the signal. Subsequently, the feature fusion between time-frequency image features and temporal features was conducted. Softmax classifier was finally applied to the fused features for fault classification. To validate the proposed method, experiments were conducted using a dataset from a comprehensive powertrain fault diagnosis test bench. Comparative experiments with other intelligent diagnostic methods demonstrated that the proposed approach achieved the highest fault recognition rate with an average diagnostic accuracy of 99.68%. This confirmed its feasibility and provided valuable insights for the intelligent diagnosis and practical application of engine gear transmission mechanisms.