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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.

The study and prevention of unstable vibration is a challenging task for vehicle industry. Improving predicting accuracy of braking squeal model is of great concern. Closed-loop coupling disc brake model is widely used in complex eigenvalue analysis and further analysis. The coupling stiffness of disc rotor and pads is one of the most important parameters in the model. But in most studies the stiffness is calculated by simple static force-deformation simulation. In this paper, a closed-loop coupling disc brake model is built. Initial values of coupling stiffness are estimated from static calculation. Experiment modal analysis of stationary disc brake system with brake line pressure and brake torques applied is conducted. Then an optimization process is initiated to minimize the differences between modal frequencies predicted by the stationary model and those from test. Thus model parameters more close to reality are found.

Due to the rapid development of road infrastructure and vehicle population in China, the fuel consumption and emission of on-road vehicles tested in China World Transient Vehicle Cycle (C-WTVC) cannot indicate the real driving results. But the test results in China Heavy-duty Commercial Vehicle Test Cycle-Coach (CHTC-C) based on the road driving conditions in China are closer to the actual driving data. In this paper, the model for predicting the performance of heavy-duty vehicles is established and validated. The fuel consumption and NOx emission of a Euro VI heavy-duty coach under C-WTVC and CHTC-C tests are calculated by employing the developed model. Furthermore, the fuel consumption of the test coach is optimized and its sensitivity to the driving factors is analyzed.

A computational fluid dynamics (CFD) guided design optimization was conducted for the piston bowl geometry for a heavy-duty diesel engine. The optimization goal was to minimize engine-out NOx emissions without sacrificing engine peak power and thermal efficiency. The CFD model was validated with experiments and the combustion system optimization was conducted under three selected operating conditions representing low speed, maximum torque, and rated power. A hundred piston bowl shapes were generated, of which 32 shapes with 3 spray angles for each shape were numerically analyzed and one optimized design of piston bowl geometry with spray angle was selected. On average, the optimized combustion system decreased nitrogen oxide (NOx) emissions by 17% and soot emissions by 41% without compromising maximum engine power and fuel economy.

Transport vehicles consume a large amount of fuel with low efficiency, which is significantly affected by drivers' behaviors. An assessment system of eco-driving pattern for buses could identify the deficiencies of driver operation as well as assist transportation enterprises in driver management. This paper proposes an assessment method regarding drivers' economic efficiency, considering driving conditions. To this end, assessment indexes are extracted from driving economy theories and ranked according to their effect on fuel consumption, derived from a database of 135 buses using multiple regression. A layered structure of assessment indexes is developed with application of AHP, and the weight of each index is estimated. The driving pattern score could be calculated with these weights.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

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).

The combustion characteristics and soot emissions of three types of fuels were studied in a high pressure and temperature vessel. In order to achieve better volatility, proper cetane number and high oxygen content, the newly designed WDEP fuel was proposed and investigated. It is composed of wide distillation fuel (WD), PODE3-6 mixture (PODEn) and ethanol. For comparison, the test on WD and the mixture of PODEn-ethanol (EP) are also conducted. OH* chemiluminescence during the combustion was measured and instantaneous PLII was also applied to reveal the soot distribution. Abel transformation was adopted to calculate the total soot of axisymmetric flame. The results show that WDEP has similar ignition delays and flame lift-off lengths to those of WD at 870-920 K. But the initial ignition locations of WDEP flame in different cycles were more concentrated, particularly under the condition of low oxygen atmosphere.

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).

Studying drifting dynamics and control could extend the usable state-space beyond handling limits and maximize the potential safety benefits of autonomous vehicles. Distributed electric vehicles provide more possibilities for drifting control with better grip and larger maximum drift angle. Under the state of drifting, the distributed electric vehicle is a typical nonlinear over-actuated system with actuator redundancy, and the coupling of input vectors impedes the direct use of control algorithm of upper. This paper proposes a novel automated drifting controller for the distributed electric vehicle. First, the nonlinear over-actuated system, comprised of driving system, braking system and steering system, is formulated and transformed to a square system through proposed integrative recombination method of control channel, making general nonlinear control algorithms suitable for this system.

Combustion visualizations were carried out in a constant volume vessel to study the partially premixed combustion of a gasoline-like fuel using high speed imaging. The test fuel (G80H20) is composed by volume 80% commercial gasoline and 20% n-heptane. The effects of ambient gas composition, ambient temperature and injection pressure on G80H20 combustion characteristics were analyzed. Meanwhile, a comparison of the EGR effect on combustion process between G80H20 and diesel was made. Four ambient gas conditions that represent the in-cylinder gas compositions of a heavy-duty diesel engine with EGR ratios of 0%, 20%, 40% and 60% were used to simulate EGR conditions. Variables also include two ambient temperature (910K and 870K) and two injection pressure (20 MPa and 50 MPa) conditions.

Transient emissions of a turbocharged three-litre V6 diesel engine fuelled by hydrogenated vegetable oil (HVO) blends were experimentally investigated and compared with transient emissions of diesel as reference. The transient emissions measurements were made by highly-dynamic emissions instrumentations including Cambustion HFR500, CLD500 and DMS500 particulate analyzer. The HVO blends used in this study were 30% and 60% of HVO in diesel by volume. The transient conditions were simulated by load increases over 5 s, 10 s and 20 s durations at a constant engine speed. The particulate, NO, HC concentrations were measured to investigate the mechanism of emission formation under such transient schedules. The results showed that as the load increased, NO concentrations initially had a small drop before dramatically increasing for all the fuels investigated which can be associated with the turbocharger lag during the load transient.

Polyoxymethylene Dimethyl Ether (PODEn) is a promising green additive to diesel fuel, owing to the unique chemical structure (CH3O[CH2O]nCH3, n≥2) and high cetane number. Together with the general wide-distillation fuel (WDF), which has an attractive potential to reduce the cost of production of vehicle fuel, the oxygenated WDF with PODEn can help achieve a high efficiency and low emissions of soot, NOx, HC, and CO simultaneously. In this paper, the first detailed reaction mechanism (225 species, 1082 reactions) which can describe the ignition characteristics of PODE1 and PODE3 at low temperature was developed.

The first several cycles determine the quality of an engine start. Low temperatures and air/fuel ratio cause incomplete combustion of the fuel. This can lead to dramatic increases in HC and PM emissions. In order to meet Euro V legislation requirements which have stricter cold start emission levels, it is critical to study the characteristics of cold and warm starting of engines in order to develop an optimized operation. The NO and THC emissions were measured by fast CLD and Fast FID gas analyzers respectively and PM in both nucleation and accumulation modes were measured by DMS500. The coolant temperature was controlled in order to guarantee the experiment repeatability. The results show that at cold start using RME60 produced higher NO and lower THC than the other tested fuels while combustion of HVO60 produced a similar level of NO but lower THC compared with mineral diesel. Meanwhile, the nucleation mode of mineral diesel was similar to RME60 but higher than HVO60.

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.

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.