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Roundabouts are one of the most complex traffic environments in urban roads, and a key challenge for intelligent driving decision-making. Deep reinforcement learning, as an emerging solution for intelligent driving decisions, has the advantage of avoiding complex algorithm design and sustainable iteration. For the decision difficulty in roundabout scenarios, this paper proposes an artificial potential field based Soft Actor-Critic (APF-SAC) algorithm. Firstly, based on the Carla simulator and Gym framework, a reinforcement learning simulation system for roundabout driving is built. Secondly, to reduce reinforcement learning exploration difficulty, global path planning and path smoothing algorithms are designed to generate and optimize the path to guide the agent.

Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.

The push for environmental protection and sustainability has led to strict emission regulations for automotive manufacturers as evident in EURO VII and 2026 EPA requirements. The challenge lies in maintaining fuel efficiency and simultaneously reducing the carbon footprint while meeting future emission regulations. Alcohol (primarily methanol, ethanol, and butanol) and ether (dimethyl ether) fuels, owing to their comparable energy density to existing fuels, the comparative ease of handling, renewable production, and suitable emission characteristics may present an attractive drop-in replacement, fully or in part as an additive, to the gasoline/diesel fuels, without extensive modifications to the engine geometry. Additionally, lean and diluted combustion are well-researched pathways for efficiency improvement and reduction of engine-out emissions of modern engines.

The majority of transportation systems have continued to be powered by the internal combustion engine and fossil fuels. Heavy-duty applications especially are reliant on diesel engines for their high brake efficiency, power density, and robustness. Although engineering developments have advanced engines towards significantly fewer emissions and higher efficiency, the use of fossil-derived diesel as fuel sets a fundamental threshold in the achievable total net carbon reduction. Dimethyl ether can be produced from various renewable feedstocks and has a high chemical reactivity making it suitable for heavy-duty applications, namely compression ignition direct injection engines. Literature shows the successful use of DME fuels in diesel engines without significant hardware modifications.

Substantial effort has been devoted to utilizing homogeneous charge compression ignition (HCCI) to improve thermal efficiency and reduce emission pollutants in internal combustion engines. However, the uncertainty of ignition timing and limited operational range restrict further adoption for the industry. Using the spark-assisted compression ignition (SACI) technique has the advantage of using a spark event to control the combustion process. This study employs a rapid compression machine to characterize the ignition and combustion process of Dimethyl ether (DME) under engine-like background temperature and pressures and combustion regimes, including HCCI, SACI, and knocking onsite. The spark ignition timing was swept to ignite the mixture under various thermodynamic conditions. This investigation demonstrates the presence of four distinct combustion regimes, including detonation, strong end-gas autoignition, mild end-gas autoignition, and HCCI.

In order to improve the fuel economy for future high-efficiency spark ignition engines, the applications of advanced combustion strategies are considered to be beneficial with an overall lean and/or exhaust gas recirculation diluted cylinder charge. Stronger and more reliable ignition sources become more favorable under extreme lean/EGR conditions. Existing research indicates that the frequency of plasma restrikes increases with increased flow velocity and decreased discharge current level, and a higher discharge current can reduce the gap resistance and maintain the stretched plasma for a longer duration under flow conditions. An in-house developed current boost control system provides flexible control of the discharge current level and discharge duration. The current boost ignition system is based on a multi-coil system with a discharge current level of 180mA.

Ammonia has attracted the attention of a growing number of researchers in recent years. However, some properties of ammonia (e.g., low laminar burning velocity, high ignition energy, etc.) inhibit its direct application in engines. Several routes have been proposed to overcome these problems, such as oxygen enrichment, partial fuel cracking strategy and co-combustion with more reactive fuels. Improving the reactivity of ammonia from the oxidizer side is also practical. Ozone is a highly reactive oxidizer which can be easily and rapidly generated through electrical plasma and is an effective promoter applicable for a variety of fuels. The dissociation reaction of ozone increases the concentration of reactive radicals and promotes chain-propagating reactions. Thus, obtaining accurate rate constants of reactions related to ozone is necessary, especially at elevated to high pressure range which is closer to engine-relevant conditions.

To achieve higher efficiencies and lower emissions, dual-fuel strategies have arisen as advanced engine technologies. In order to fully utilize engine fuels, understanding the combustion chemistry is urgently required. However, due to computation limitations, detailed kinetic models cannot be used in numerical engine simulations. As an alternative, approaches for developing reduced reaction mechanisms have been proposed. Nevertheless, existing simplified methods neglecting the real engine combustion processes, which is the ultimate goal of reduced mechanism. In this study, we propose a novel simplified approach based on fuel reactivity. The high-reactivity fuel undergoes pyrolysis first, followed by the pyrolysis and oxidation of the low-reactivity fuel. Therefore, the simplified mechanism consists of highly lumped reactions of high-reactivity fuel, radical reactions of low-reactivity fuel and C0-C2 core mechanisms.

Compression ignition engines used in heavy-duty applications are typically powered by diesel fuel. The high energy density and feedstock abundance provide a continuing source for the immense energy demand. However, the heavy-duty transportation sector is challenged with lowering greenhouse gas and combustion by-product emissions, including carbon dioxide, nitrogen oxides, and particulate matter. The continuing development of engine management and combustion strategies has proven the ability to meet current regulations, particularly with higher fuel injection pressure. Nonetheless, a transition from diesel to a renewable alternative fuel source will play a significant role in reducing greenhouse gases while maintaining the convenience and energy density inherent in liquid fuels. Dimethyl ether is a versatile fuel that possesses combustion properties suitable for compression ignition engines and physical properties helpful for clean combustion.

Compression ignition internal combustion engines provide unmatched power density levels, making them suitable for numerous applications including heavy-duty freight trucks, marine shipping, and off-road construction vehicles. Fossil-derived diesel fuel has dominated the energy source for CI engines over the last century. To mitigate the dependency on fossil fuels and lessen anthropogenic carbon released into the atmosphere within the transportation sector, it is critical to establish a fuel source which is produced from renewable energy sources, all the while matching the high-power density demands of various applications. Dimethyl ether (DME) has been used in non-combustion applications for several decades and is an attractive fuel for CI engines because of its high reactivity, superior volatility to diesel, and low soot tendency. A range of feedstock sources can produce DME via the catalysis of syngas.

Considering the change of vehicle future power demand in the process of energy distribution can improve the fuel saving effect of hybrid system. However, current studies are mostly based on historical information to predict the future power demand, where it is difficult to guarantee the accuracy of prediction. To tackle this problem, this paper combines hybrid energy management with predictive cruise control, proposing a hierarchical control strategy of predictive energy management (PEM) that includes two layers of algorithms for speed planning and energy distribution. In the interest of decreasing the energy consumed by power components and ensuring transportation timeliness, the upper-level introduces a predictive cruise control algorithm while considering vehicle weight and road slope, planning the future vehicle speed during long-distance driving.

Driving assistance system is regarded as an effective method to improve driving safety and comfort and is widely used in automobiles. However, due to the different driving styles of different drivers, their acceptance and comfort of driving assistance systems are also different, which greatly affects the driving experience. The key to solving the problem is to let the system understand the driving style and achieve humanization or personalization. This paper focuses on clustering and identification of different driving styles. In this paper, based on the driver's real vehicle experiment, a driving data acquisition platform was built, meanwhile driving conditions were set and drivers were recruited to collect driving information. In order to facilitate the identification of driving style, the correlation analysis of driving features is conducted and the principal component analysis method is used to reduce the dimension of driving features.

With the aim of addressing the problem of inconsistency of the traditional proportion integration (PI) driver model with the actual driving behavior, a longitudinal driver model based on the elastic net regression (ENR) and adaptive network fuzzy inference system (ANFIS) method is proposed. First, longitudinal driving behavior data are collected through bench tests to extract the characteristic parameters that affect driving behavior. A quadratic regression model is established after considering the nonlinear characteristics of the driver behavior. The multi-collinear problem of high-dimensional variables in the regression model is solved by the ENR method, and the parameters with significant influence on driving behavior selected. A longitudinal driver model of ANFIS was established with the selected characteristic parameters as input. Finally, the validity of the model is verified by comparing it with the PI and ENR driver models.

Oxygenated, low energy-density fuels have the potential to decouple the NOx-soot emissions trade-off in compression-ignition engines. Additionally, synthetic fuels can provide a pathway to reach carbon-neutral utilization of hydrocarbon-based fuels in IC engines. Oxymethylene Dimethyl Ether (OME) is one such synthetic, low energy-density fuel, derived from sustainable sources that in combination with conventional fossil fuels with higher energy content, has the potential to reduce CO2 emissions below the US and EU VI legislative limits, while maintaining ultra-low soot emissions. The objective of this work is to investigate and compare the performance, emissions and efficiency of a modern multi-cylinder diesel engine under conventional high temperature combustion (HTC) with two different fuels; 1) OME310 - a blend of 10% OME3 by volume, with conventional Ultra-Low Sulphur Diesel (ULSD), and 2) D100 - conventional ULSD in North America.

The RON(Research Octane Number) is the most important indicator of motor petrol, and the petrol refining process is one of the important links in petrol production. However, RON is often lost during petrol refining and RON Loss means the value of RON lost during petrol refining. The prediction of the RON loss of petrol during the refining process is helpful to the improvement of petrol refining process and the processing of petrol. The traditional RON prediction method relied on physical and chemical properties, and did not fully consider the high nonlinearity and strong coupling relationship of the petrol refining process. There is a lack of data-driven RON loss models. This paper studies the construction of the RON loss model in the petrol refining process.

Increasingly strict emission regulations and unfavorable economic climate bring severe challenges to the energy conservation of marine low-speed engine. Besides traditional methods, the energy and exergy analysis could acknowledge the losses of fuel from a global perspective to further improve the engine efficiency. Therefore, the energy and exergy analysis is conducted for a marine low-speed engine based on the experimental data. Energy analysis shows the exhaust gas occupies the largest proportion of all fuel energy waste, and it rises with the increment of engine load. The heat transfer consumes the second largest proportion, while it is negatively correlated to engine load. The energy analysis indicates that the most effective way to improve the engine efficiency is to reduce the energy wasted by exhaust gas and heat transfer. However, the latter exergy analysis demonstrates that there are other effective approaches to improve the engine efficiency.

Advanced combustion engines, as power sources, dominate all aspects of the transportation sector. Stringent emission and fuel efficiency standards have promoted the research interest in advanced combustion strategies and alternative fuels. Owing to the comparable energy density to the existing fossil fuels and renewable production, alcohol and ether fuels may be a suitable replacement, or an additive to the gasoline/diesel fuels to meet the future emission standards with minimal modification to current engine geometry. Furthermore, lean and diluted combustion are well-researched pathways for efficiency improvement and reduction of engine-out emissions of modern engines. However, lean-burn or EGR dilution can introduce combustion inefficiencies in the form of excessive hydrocarbon, carbonyl species and carbon monoxide emissions.

The heterogeneous nature of direct injection (DI) combustion yields high combustion efficiencies but harmful emissions through the formation of high nitrogen oxide (NOx) and smoke emissions. In response, extensive empirical and computational research has focused on balancing the NOx-smoke trade-off to limit diesel DI combustion emissions. Dimethyl ether (DME) fuel is applicable in DI compression ignition engines and its high fuel oxygen produces near-smoke-free emissions. Moreover, the addition of a premixed fuel can improve mixture homogeneity and minimize the DI fuel energy demands lessening injection durations. For this technique, a low reactivity fuel such as ethanol is essential to avoid early autoignition in high compression ratio engines. In this work, empirical experiments of dual fuel operation have been conducted using premixed ethanol with high-pressure direct injection DME.

Gluing is an essential fastening step in the field of aircraft assembly except for riveting and bolting. Generally, the robotic programs of gluing are generated in CAM environment. Due to the positioning errors and deformation of the workpiece to be glued in the fixture, the nominal pose and the actual pose of the workpiece are no longer consistent with each other. The Robot trajectory of dispensing glue is adjusted manually according to the actual pose of the workpiece by robot teaching. In this paper, an on-line gluing path correction method is developed by 2D laser profile measurement. A pose calibration method for 2D laser profiler integrated into a gluing robot by measuring a fixed center point of a standard ball is proposed to identify the position and orientation of the laser sensor, which enables the accurate transforming coordinates between the robot frame and the sensor frame.

To quickly and efficiently match the planetary gearset characteristic parameter of power-spilt hybrid vehicles so that their oil-saving potential can be maximized, this study proposes a parameter matching method that comprehensively considers energy management strategy and driving cycle based on an analysis of vehicle instantaneous efficiency. The method is used to match the planetary characteristic parameter of a power-split hybrid light truck. The relevant conclusions are compared with the influence of various planetary characteristic parameters on fuel consumption obtained through simulation under typical operating conditions. The simulation results show that the influence laws of the various planetary characteristic parameters on vehicle average efficiency are similar to those on fuel consumption. The proposed parameter-matching method based on vehicle efficiency analysis can effectively match the planetary characteristic parameter for power-split hybrid powertrains.