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Detailed chemical kinetics is essential for accurate prediction of combustion performance as well as emissions in practical combustion engines. However, implementation of that is challenging. In this work, dynamic adaptive chemistry (DAC) is integrated into large eddy simulations (LES) of an n-heptane spray flame in a constant volume chamber (CVC) with realistic application conditions. DAC accelerates the time integration of the governing ordinary differential equations (ODEs) for chemical kinetics through the use of locally (spatially and temporally) valid skeletal mechanisms. Instantaneous flame structures and global combustion characteristics such as ignition delay time, flame lift-off length (LOL) and emissions are investigated to assess the effect of DAC on LES-DAC results. The study reveals that in LES-DAC simulations, the auto-ignition time and LOL obtain a well agreement with experiment data under different oxygen concentrations.

Homogeneous Charge Induced Ignition (HCII) combustion utilizes a port injection of high-volatile fuel to form a homogeneous charge and a direct injection of high ignitable fuel near the Top Dead Center (TDC) to trigger combustion. Compared to Conventional Diesel Combustion (CDC) with high injection pressures, HCII has the potential to achieve diesel-like thermal efficiency with significant reductions in NOx and PM emissions with relatively low-pressure injections, which would benefit the engine cost saving remarkably. In the first part of current investigation, experiments were conducted at medium load with single diesel injection strategy. HCII exhibited great potential of using low injection pressures to achieve low soot emissions. But the engine load for HCII was limited by high heat release rate. Thus, in the second and third part, experiments were performed at high and low load with double diesel injection strategy.

Traffic congestions are increasingly severe in urban areas, especially at the merging areas of the ramps and the arterial roads. Because of the complex conflict relationship of the vehicles in ramps and arterial roads in terms of time-spatial constraints, it is challenging to coordinate the motion of these vehicles, which may easily cause congestions at the merging areas. The connected and automated vehicles (CAVs) provides potential opportunities to solve this problem. A centralized merging control method for CAVs is proposed in this paper, which can organize the traffic movements in merging areas efficiently and safely. In this method, the merging control model is built to formulate the vehicle coordination problem in merging areas, which is then transformed to the discrete nonlinear optimization form. A simulation model is built to verify the proposed method.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

Plug-in electric vehicle (PHEV) has a promising prospect to reduce greenhouse gas (GHG) emission and optimize engine operating in high-efficiency region. According to the maximum electric power and all-electric range, PHEVs are divided into two categories, including “all-electric PHEV” and “blended PHEV” and the latter provides a potential for more rational energy distribution because engine participates in vehicle driving during aggressive acceleration not just by motor. However, the frequent use of engine may result in severe emissions especially in low state of charge (SOC) and ahead of catalyst light-off. This study quantitatively investigates the impact of oil viscosity and driving mode (hybrid/conventional) on oil dilution and emissions including particle number (PN).

The effects of different biodiesel blends on engine-out emissions under various transient conditions were investigated in this study using fast response diagnostic equipment. The experimental work was conducted on a modern 3.0 L, V6 high pressure common rail diesel engine fuelled with mineral diesel (B0) and three different blends of rapeseed methyl esters (RME) (B30, B60, B100 by volume) without any modifications of engine parameters. DMS500, Fast FID and Fast CLD were used to measure particulate matter (PM), total hydrocarbon (THC) and nitrogen monoxide (NO) respectively. The tests were conducted during a 12 seconds period with two tests in which load and speed were changed simultaneously and one test with only load changing. The results show that as biodiesel blend ratio increased, total particle number (PN) and THC were decreased whereas NO was increased for all the three transient conditions.

An energy management strategy is needed to optimally allocate the driver's power demands to different power sources in the fuel cell hybrid vehicles. The driver's power demand is modelled as a Markov process in which the transition probabilities are estimated on the basis of the observed sample paths. The Markov Decision Process (MDP) theory is applied to design a stochastic energy management strategy for fuel cell hybrid vehicles. This obtained control strategy was then tested on a real time simulation platform of the fuel cell hybrid vehicles. In comparison to the other 3 strategies, the constant bus voltage strategy, the static optimization strategy and the dynamic programming strategy, simulations in the Beijing bus driving cycle demonstrate that the obtained stochastic energy management strategy can achieve better performance in fuel economy in the same demand of dynamic.

In this paper, a hybrid combustion mode in four-stroke gasoline direct injection engines was studied. Switching cam profiles and injection strategies simultaneously was adopted to obtain a rapid and smooth switch between SI mode and HCCI mode. Based on the continuous pressure traces and corresponding emissions, HCCI steady operation, HCCI transient process (combustion phase adjustment, SI-HCCI, HCCI-SI, HCCI cold start) were studied. In HCCI mode, HCCI combustion phase can be adjusted rapidly by changing the split injection ratio. The HCCI control strategies had been demonstrated in a Chery GDI2.0 engine. The HCCI engine simulation results show that, oxygen and active radicals are stored due to negative valve overlap and split fuel injection under learn burn condition. This reduces the HCCI sensitivity on inlet boundary conditions, such as intake charge and intake temperature. The engine can be run from 1500rpm to 4000rpm in HCCI mode without spark ignition.

Due to the high cost of torque sensors, a calculation model of transient torque is required for real-time coordinating control purpose, especially in hybrid electric powertrains. This paper presents a feedforward calculation method based on mean value model of turbocharged non-EGR diesel engines. A fitting variable called fuel coefficient is defined in an affine relation between brake torque and fuel mass. The fitting of fuel coefficient is simplified to depend only on three variables (engine speed, boost pressure, injected fuel mass). And a two-layer feedforward neural network is utilized to fit the experimental data. The model is validated by load response test and ETC (European Transient Cycle) transient test. The RMSE (root mean square error) of the brake torque is less than 3%.

Clarifying accident causations can provide a strong foundation to prevent traffic accidents and reduce severities. This paper uses Chinese government census data from 1996-2003[1∼8] and models a relationship between various kinds of traffic accident causations and the severities of the traffic accidents based on neural network combined (NNC). The paper adapts multi-folder cross validation concept to enhance the properties of NNC. It then conducts sensitivity analysis on the trained NNC to identify the prioritized importance of traffic accident causations as they are to the severities of traffic accident. Lastly, the results are validated and compared by the findings of previous researches.

Advanced compression ignition combustion system which reduces simultaneously both nitride oxides (NOx) and particulate matter (PM) is a promising approach to meet future emission regulations. In order to achieve advanced compression ignition, flexible fuel injection is required for ultra-early and post-TDC injections, which conventional injector fails to accomplish due to wall-wetting effect. In this work, special injectors with the spray angle of 60 degree are applied on a 4 cylinder mass-production diesel engine without modification of the engine configuration. For application-oriented study, sweep experiments of injection timings and durations, fuel injection pressure and the boost pressure are carried out to investigate the relationships between the control parameters and the engine performance. Model based calibration and real application tests validate the maximum applicable operation range of maximum speed of 2200 RPM and IMEP of 8.0 bar.

The large amount of controllable fuel injection parameters of Diesel engine equipped with high pressure common-rail fuel injection system makes the control of combustion more flexible, and also makes the workload of calibration and optimization much heavier. For higher efficiency, model-based approaches are presented and researched. This contribution presents a new method for modeling which is constituted by Neural Network and Adaptive Network-based Fussy Inference System (ANFIS). The experiment is carried out on a 6-cylinder common rail diesel engine. The analysis and experiment show that effective modeling can be achieved using this method.

The traditional vacuum booster is gradually replaced by Brake-by-Wire system (BBW) in modern passenger car, especially Electric Vehicle (EV). Some mechanical and hydraulic components are replaced by electronic components in Brake-by-Wire system. Using BBW system in modern passenger vehicles can not only improve the automotive safety performance, reliability and stability, but also promote vehicle maneuverability, comfort, fuel economy and environmental protection. Although vehicle's braking performance is greatly improved by using BBW, the system will inevitably consume some energy of the vehicle power supply, thus introducing unexpected drawback in comparison with the traditional vacuum assist braking system, since it doesn't need any electric power. Therefore, the analysis of energy consumption on typical main cylinder booster based BBW system under typical driving cycles will contribute to advanced design of current advanced braking system.

Highway traffic safety has been the most serious problem in current society, statistics show that about 70% to 90% of accidents are caused by driver operational errors. The autonomous emergency braking (AEB) is one of important vehicle intelligent safety technologies to avoid or mitigate collision. The AEB system applies the vehicle brakes when a collision is eminent in spite of any reaction by the driver. In some technologies, the system forewarns the driver with an acoustic signal when a collision is still avoidable, but subsequently applies the brakes automatically if the driver fails to respond. This paper presents the development and implementation of a rear-end collision avoidance system based on hierarchical control framework which consists of threat assessment layer, wheel slip ratio control layer and integrated-electro-hydraulic brake (IEHB) actuator control layer.

Gasoline direct injection (GDI) engines have been developed rapidly in recent years, driven by stringent legislative requirements on vehicle fuel efficiency and emissions. However, one challenge facing GDI is the formation of particulate emissions, particularly with the presence of injector tip deposits. The Chinese market features some gasoline fuels that contain no detergent additives and are prone to deposit formation, which can affect engine performance and emissions. The use of detergent additives to mitigate the formation of injector deposits in a GDI engine was investigated in this study by testing a 1.5L turbocharged GDI engine available in the Chinese market. The engine was operated both on base gasoline and on gasoline dosed with detergent additives to evaluate the effect on injector deposit formation and engine performance and emissions.

Low temperature combustion (LTC) is an advanced combustion mode, which can achieve low emissions of NOx and PM simultaneously, and keep relatively high thermal efficiency at the same time. However, one of the major challenges for LTC is the cold condition. In cold conditions, stable compression ignition is hard to realize, while thermal efficiency and emissions deteriorate, especially for gasoline or fuel with high octane number. This study presents using pressure sensor glow plugs (PSG) to realize Glow plug assisted compression ignition (GA-CI) at cold conditions. Further, a glow plug control unit (GPCU) is developed, a closed-loop power feedback control algorithm is introduced based on GPCU. In the experiment, engine coolant temperature is swept. Experimental results show that GA-CI has earlier combustion phases, larger combustion duration and higher in-cylinder pressure. And misfire is avoided, cycle-to-cycle variations are greatly reduced.

Advanced exhaust after-treatment technology is required for heavy-duty diesel vehicles to achieve stringent Euro VI emission standards. Diesel particulate filter (DPF) is the most efficient system that is used to trap the particulate matter (PM), and particulate number (PN) emissions form diesel engines. The after-treatment system used in this study is catalyzed DPF (CDPF) downstream of diesel oxidation catalyst (DOC) with secondary fuel injection. Additional fuel is injected upstream of DOC to enhance exothermal heat which is needed to raise the CDPF temperature during the active regeneration process. The objective of this research is to numerically investigate soot loading and active regeneration of a CDPF on a heavy-duty diesel engine. In order to improve the active regeneration performance of CDPF, several factors are investigated in the study such as the effect of catalytic in filter wall, soot distribution form along filter wall, and soot loads.

Urea selective catalytic reduction (SCR) is a key technology for heavy-duty diesel engines to meet the increasingly stringent nitric oxides (NOx) emission limits of regulations. The urea water solution injection control is critical for urea SCR systems to achieve high NOx conversion efficiency while keeping the ammonia (NH3) slip at a required level. In general, an open loop control strategy is sufficient for SCR systems to satisfy Euro IV and Euro V NOx emission limits. However, for Euro VI emission regulation, advanced control strategy is essential for SCR systems due to its more tightened NOx emission limit and more severe test procedure compared to Euro IV and Euro V. This work proposed an approach to achieve model based closed loop control for SCR systems to meet the Euro VI NOx emission limits. A chemical kinetic model of the SCR catalyst was established and validated to estimate the ammonia storage in the SCR catalyst.

In this paper, the impacts of Aromatic and Olefin on the formation of poly-aromatic hydrocarbons (PAHs) in the gasoline direct injection (GDI) engine were experimentally and numerically investigated. The objective of this study is to describe the formation process of the soot precursors including one ring to four ring aromatics (A1-A4). In order to better understand the effects of the fuel properties on the formations of PAHs. Three types of fuels, namely base gasoline, gasoline with higher aromatics content, and gasoline with higher olefin content were experimentally studied. At the same time, these aspects were also numerically investigated in the CHEMKIN code by using premixed laminar flame model and surrogated fuels. The results show that higher aromatics content in gasoline will lead to much higher PAHs formation. Similar trend was also found in the gasoline with higher olefin content.

High boost and direct injection are effective ways for energy saving in gasoline engines. However, the occurrence of super-knock at high load has become a main obstacle for further improving power density and fuel economy. It has been known that super-knock can be induced by pre-ignition, and oil droplet auto-ignition is found to be one of the possible mechanisms. In this study, experiments were conducted in a single-cylinder thermal research engine (TRE), in which different types of oil and surrogates were directly injected into the cylinder and then led to pre-ignition and super-knock. The effect of oil injection timing, oil injection quantity, different gasoline and different oil were tested. All the oil in this work could induce pre-ignition, even though their combustion phasing was much later than that in the case of n-hexadecane.