Based on an active pre-chamber methanol jet ignition engine, a simulation platform was developed using CONVERGE software.Building upon the original pre-chamber geometry, the cross-sectional area of throat  was modified along the axial direction to create three distinct configurations: a gradually constricting throat (P1), a conventional uniform throat (P2) and a gradually expanding throat (P3). The flame jet dynamics and engine combustion characteristics were systematically studied across these different throat geometries. The results show that the gradually expanding throat promotes more uniform reactant distribution within the pre-chambe due to its lower velocity gradient, thereby enchancing mixture homogeneity-although all three configurations exhibit a pronounced concentration gradient at the throat region. In contrast, the gradually constricting throat design imposes stronger combustion confinement and produces a more focused jet with a higher momentum discharge, resulting from the largest pressure difference between the pre-chamber and the main combustion chamber. Consequently, this configuration acheieves faster flame propagation and greater jet kinetic energy, which further enhances turbulent mixing and combustion efficiently in the main combustion chamber. As a result, the peak cylinder pressure increases by 0.129 MPa and 0.912 MPa compared to the P2 and P3 designs respectively, while the peak heat release rate significantly increases and the combustion phase advances.

Adding nanoparticles to diesel can improve the evaporation and oxidation rate of diesel fuel, and the effective thermal efficiency of diesel also increase accordingly. The effects of nano aluminum powder (Al NPs) and carbon nanotubes (CNT) on diesel evaporation characteristics were studied at a concentration of 0.5%. Experiments demonstrated that both Al NPs and CNT accelerated diesel evaporation. At temperatures below 573 K, the impacts of Al NPs and CNT on diesel were similar, reducing the evaporation time of diesel to 60%-70% of its original value. However, at temperatures exceeding 573 K, the influence of Al NPs remained unchanged, but CNT shortened the evaporation time of diesel to 10%-20% of pure diesel. In addition, Al NPs and CNT had different evaporation forms for diesel. Al NPs accelerated evaporation by accelerating smooth evaporation, CNT accelerated evaporation by inducing fluctuating evaporation. Notably, fluctuating evaporation in Al NPs-diesel occured primarily under high-temperature and large-volume conditions, manifesting as jet-like jection. In contrast, fluctuating evaporation in CNT-diesel was observed even at low temperatures and small volumes, predominantly through micro-explosions.

To improve the injection performance of a certain highflow common rail system integrating flow limiting valve and injector filter, structural optimization was carried out for both components. Firstly, a one-dimensional hydraulic simulation model of the common rail system was established using AMESim. The simulation model was used to explore the influence of changes in the parameters of flow limiting valve, filter and high-pressure fuel pipe on the fuel injection characteristics. Then the valve core diameter, throttle aperture, valve body inner cavity diameter, valve core clearance, filter hole diameter, and high-pressure fuel pipe inner diameter were used as design variables, and the cyclic fuel injection quantity and average fuel injection rate were used as response variables, and a multiple quadratic regression equation of effect values and design factors was constructed and the weights and coupling effects of design variables were analyzed by applying Design-Expert software to form a design matrix based on the three-level and second-order test method. Finally，based on the response surface regression model, the multi-objective MOGA optimization algorithm was used to optimize the structure of the flow limiting valve and injector filter. The results showed that both the cyclic fuel injection volume and average injection rate were simultaneously optimized when the valve core diameter was 5.567 mm, the throttle aperture was 0.954 mm, the valve body inner cavity diameter was 16.112 mm, the valve core clearance was 0.130 mm, the filter hole diameter was 0.583 mm, and the high-pressure fuel pipe inner diameter was 3.990 mm, The cyclic fuel injection quantity and average fuel injection rate after optimization increased by 5.86% and 6.24%, respectively. 

The multi-scheme design and optimization of blade trailing edge width for centrifugal compressor was carried out by numerical simulation method. The influencing law of blade trailing edge width on compressor performance and the internal flow characteristics of impeller are analyzed in detail, and the results before and after optimization were compared. The results show that the trailing edge blade width has different effects on the compressor performance at different compressor speeds. Under the design condition, the modified trailing edge blade width can increase the pressure ratio by 2.19% and the efficiency by 0.59%, the performance gap between the two width blades changes with the change of speed. The optimized trailing edge width can also increase the flow in the blocking area of each speed, broadening the range of compressor working flow. After optimization, the maximum pressure ratios increase by 1.5%-3.21% and the maximum efficiency increases by 0.75%-1.75% with a more pronounced performance improvement in the medium and high speed region.

Based on an engine test bench with intake and exhaust altitude simulation function, the world harmonized transient cycle（WHTC） emission tests were carried out on a China Ⅵ diesel engine at altitudes ranging from 0 m to 4 000 m. The WHTC cycle was further divided into typical operating conditions corresponding to the actual vehicle operation scenarios, namely urban condition, suburban condition and highway condition. The pollutant emission characteristics of diesel engine were investigated under different altitudes and operating conditions, as well as the both combination. The results showed that the emissions of CO, HC and NO_x at 4 000 m increased by 11.5 times, 25.3 times and 6.4 times respectively compared with those at 0 m, while the exhaust particle number（PN） specific emission was almost unaffected by altitude. The specific emissions of CO, HC and NO_x under urban condition were 7.7 times, 5.5 times and 6 times of those under highway condition respectively; and the PN specific emission under suburban condition was 7.5 times and 3.4 times of that under urban condition and highway condition respectively. The exhaust gaseous pollutant emissions of diesel engine were strongly correlated with the engine output power and altitude. The correlation coefficients between the specific emissions of CO, HC, NO_x and the ratio of altitude coefficient to power coefficient were 0.91, 0.78 and 0.79 respectively, whereas the PN specific emission exhibited low correlation coefficients with both engine output power and altitude.

The venturi tube in the automotive desorption system is a device that utilizes fluid dynamics principles to achieve gas mixing and transmission. Optimizing the structural parameters is particularly crucial for improving vehicle emission performance. By employing the advanced numerical simulation technology, the fluid dynamic characteristics within the vehicle venturi tube was analyzed and the influence mechanisms of key structural parameters on flow characteristics and operational efficiency were explored. These parameters included mixing tube length, the ratio of drive nozzle diameter to mixing tube diameter (d_mr/d_td), thrust gap width, diffuser diameter and diffusion angle. The results indicate that all parameters significantly influence the suction performance and operational efficiency of the venturi tube. Detailed influence patterns for each parameter across different driving pressure ranges are elaborated, along with proposed optimization strategies.

In practice, factors such as the assembly process of compressor shaft end connection structures, surface quality of connecting components, and operational loads exhibit significant uncertainties, which can lead to degradation of the shaft end fastening force. To address this issue, the life cycle of the shaft end fastening force was divided into three sequential stages: pre-tensioning, static holding and service operation. A prediction model of the fastening force during the whole life course of shaft end connecting structure was developed and validated using experimental and simulation data. Based on this, a reliability model for compressor shaft end fastening was established to enable quantitative reliability assessment. The results indicate that the proposed fastening force prediction model achieves high accuracy, with errors within ±10%. Under 95% reliability conditions, the critical  fastening force required to prevent loosening, as evaluated by the reliability model, is 36.69 kN, satisfying the specified reliability requirements.

Traditional oil sump designs usually adopt metallic materials. To give full play to the performance of oil sump, a lightweight design of oil sump was proposed based on NSGA-Ⅱ algorithm. A finite element model of oil sump was first established, and a free modal analysis was carried out. Modal tests were conducted on the original steel oil sump, and the first six modal frequencies and mode shapes were compared and analyzed to verify the accuracy of finite element model. Based on the equal stiffness approximation theory, seven typical wall thicknesses of the aluminum alloy oil sump were selected as design variables, and the first-order natural frequency and mass of the oil sump were set as the optimization objectives. The optimal Latin hypercube experimental design method was applied to analyze the contribution of design variables. Finally, based on the Isight parameter optimization software, RBF and RSM approximation models between the optimization objectives and influencing factors were constructed respectively. After analysis and comparison, RSM approximation model was selected for subsequent optimization. A global optimization was performed within the surrogate model based on NSGA-Ⅱ algorithm, and a set of predicted values for the optimal combination of first-order modal frequency and mass of the oil sump were obtained. The research results show that the predicted values of RSM approximation model are basically consistent with the simulation test results. After optimization, the first-order modal frequency increases by 22.9%, and the mass reduces by 37.6%.

Taking a certain in-line six-cylinder diesel engine as the research object, the design of experiments (DOE) for bench testing was conducted. Based on the experimental data, second-order and third-order polynomial regression emission models for NO_x and soot from the original engine were constructed and compared. The results indicated that the second-order polynomial NO_x emission model achieved a coefficient of determination (R²) of 0.999 3 and a root mean square error (RMSE) of 13.27×10^－6 on the training set, while the soot model achieved R² of 0.949 1 and RMSE of 0.343 2  mg/m³ on the training set, which demonstrated a good model fitting. The third-order polynomial model exhibited a higher R² value on the training set, but it had poorer prediction accuracy for operating points outside the training set. Based on the results, the second-order polynomial models were applied to predict the emissions of world harmonized transient cycle (WHTC) and plateau conditions. The results showed that the prediction errors for NO_x and soot specific emissions of hot WHTC were －0.51% and －1.13% respectively. At an altitude of 2 200 m, the proportion of operating points for the relative error of NO_x specific emissions within ±10% is 86.3%. The accuracy of second-order polynomial regression model met the needs of engineering predictions and had generalization capability for emission predictions at high-altitude conditions.

To address the issues of slow convergence, low accuracy and poor robustness in the estimation of state of charge (SOC) for automotive power batteries using the extended Kalman filter (EKF), a dual-symmetric adaptive extended Kalman filter method optimized by firefly algorithm (FA-DSAEKF) was proposed. Based on the EKF algorithm, the initial parameters were intelligently optimized, the symmetry and stability of the algorithm were enhanced, and the noise covariance matrix was adaptively adjusted using dual parameters, significantly improving the SOC estimation performance. The experimental results show that under different operating conditions, temperatures and initial states, the algorithm can converge quickly and stably with maximum absolute error, root mean square error, and mean absolute error all below 0.28%, and convergence time within 200 seconds. Compared to the traditional EKF algorithm, the estimation error is reduced by about 80%, and compared to the DSAEKF algorithm, the convergence speed is increased by over 83%, demonstrating excellent accuracy, adaptability, and robustness.

With the rapid development of electric vehicles, the accurate estimation for the state of charge (SOC) of power battery is crucial for vehicle energy management, range prediction and prevention of overcharge/overdischarge. In order to improve the estimation accuracy of lithium battery SOC, an equivalent circuit model was constructed based on the fractional order theory, and the model parameters were identified by using the adaptive forgetting factor recursive least square (AFFRLS) algorithm. In order to solve the problem of low data utilization and insufficient noise immunity caused by the single unscented Kalman filter (UKF) under dynamic conditions, a fractional order multiple-information unscented Kalman filter (FOMIUKF) algorithm was proposed by combining with the requirements of the long memory characteristics of fractional order model. The simulation model was built by MATLAB and compared with the extended Kalman filter (EKF) algorithm and UKF algorithm. The results showed that the average error of SOC estimation based on FOMIUKF algorithm was 0.78%, which was 0.42% higher than that of EKF algorithm and 0.25% higher than that of UKF algorithm.

Due to the typical nonlinear and non-stationary characteristics of diesel engine vibration signals, there are many challenges in the precise identification of diesel engine faults based on vibration signals. An end-to-end deep learning network framework (ResNet-ConvLSTM-Self-Attention) was proposed based on vibration signals. This framework combined the advantages of multiple models in extracting robust features without manual design. By encoding the vibration data stream images into the model, the framework can effectively identify three typical faults of diesel engines. The framework model underwent hyperparameter optimization, fault identification evaluation, and comparison with and without attention mechanisms, and an identification accuracy of 98.38% was achieved. The research results verified the effectiveness of the diesel engine fault identification model.