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Computational fluid dynamics (CFD) modeling has many potentials for the design and calibration of modern and future engine concepts, including facilitating the exploration of operation conditions and casting light on the involved physical and chemical phenomena. As more attention is paid to the matching of different fuel types and combustion strategies, the use of detailed chemistry in characterizing auto-ignition, flame stabilization processes and the formation of pollutant emissions is becoming critical, yet computationally intensive. Therefore, there is much interest in using tabulated approaches to account for detailed chemistry with an affordable computational cost. In the present work, the tabulated flamelet progress variable approach (TFPV), based on flamelet assumptions, was investigated and validated by simulating constant-volume Diesel combustion with primary reference fuels - binary mixtures of n-heptane and iso-octane.

Multiple injection strategies are commonly used in conventional Diesel engines due to the flexibility for optimizing heat-release timing with a consequent improvement in fuel economy and engine-out emissions. This is also desirable in low-temperature combustion (LTC) engines since it offers the potential to reduce unburned hydrocarbon and CO emissions. To better utilize these benefits and find optimal calibrations of split injection strategies, it is imperative that the fundamental processes of multiple injection combustion are understood and computational fluid dynamics models accurately describe the flow dynamics and combustion characteristics between different injection events. To this end, this work is dedicated to the identification of suitable methodologies to predict the multiple injection combustion process.

In partially automated and conditionally automated vehicles, a part of the work of human drivers is replaced by the system, and the main source of safety risks is no longer system failures, but non-failure risks caused by insufficient system function design. The absence of unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or by reasonably foreseeable misuse by persons, is referred to as the Safety Of The Intended Functionality. Drivers have the responsibility to supervise the automated driving system. When they don't agree with the operation behavior of the system, they will interfere with the instructions. However, this may lead to potential risks.

A novel magnetorheological fluid dual clutch (MRFDC) for electric vehicle transmission is proposed in this article. The structure was based on the MR fluid clutch and traditional dual clutch equipped on internal combustion engine vehicle. Therefore the MRFDC combines the advantages of MR fluid clutch and dual clutch transmission (DCT) to achieve high control accuracy and fast response. The structure of MRFDC was designed by Unigraphics (UG) three-dimensional (3D) modeling software. Then, finite element analysis (FEA) for magnetic field was conducted by ANSYS under different applied currents from 0.1A to 1A with 0.1A space to obtain the relation between the applied current and magnetic field. In this article, Herschel-Bulkley model is used to predict the MR fluid behavior because of the high shear rate of MR fluid.

An EV prototype, with all the wheels respectively driven by 4 inwheel motors, is developed, and undergoes a series of practical measurements and road tests. Based on the obtained vehicle parameters, a multi-body dynamics model is built by using SolidWorks and Adams/Car, and then validated by track test data. The virtual prototype is served as the control plant in simulation. An adaptive fractional order PID (A-FO-PID) controller is designed to enhance the handling and stability performance of the EV. Considering the model uncertainties, e.g. the variation in body mass distribution and the consequent change in yaw moment of inertial, a Parameter Self-Adjusting Differential Evolution (PSA-DE) algorithm is adopted for tuning the controller parameters, i.e. KP, KI, KD, λ and μ. As a modification of traditional DE algorithm, the so-called Variance of Population’s Fitness is utilized to evaluate the diversity of the population.

High fuel injection pressure has been regarded as a key controlling factor for internal combustion engines to achieve good combustion performance with reduced emissions and improved fuel efficiency. For common-rail injection system (CRS) used in advanced diesel engines, fuel injection pressure can often be raised to beyond 200 MPa. Although characteristics of diesel spray has been thoroughly studied, little work has been done at ultra-high injection pressures. In this work, the characteristics of CRS diesel spray under ultra-high injection pressure up to 250 MPa was investigated. The experiments were conducted in an optically accessible high-pressure and high-temperature constant volume chamber. The injection pressure varied from 50 MPa to up to 250 MPa. Both non-evaporating condition and evaporating condition were studied. A single-hole injector was specially designed for this investigation.

Ignition quality tester (IQT) is a standard experimental device to determine ignition delay time of liquid fuels in a controlled environment in the absence of gas exchange. The process involves fuel injection, spray breakup, evaporation and mixing, which is followed by auto-ignition. In this study, three-dimensional computational fluid dynamics (CFD) is used for prediction of auto-ignition characteristics of diethyl ether (DEE) and ethanol. In particular, the sensitivity of the ignition behavior to different injection rate profiles is investigated. Fluctuant rate profile derived from needle lift data from experiments performs better than square rate profile in ignition delay predictions. DEE, when used with fluctuant injection rate profile resulted in faster ignition, while for ethanol the situation was reversed. The contrasting results are attributed to the difference in local mixing.

Excellent energy consumption performance of a plug-in hybrid electric vehicle (PHEV) is usually attributed to its hybrid drive mode. However, the factors including vehicle performance, driver behavior and traffic status have been shown to cause unsatisfactory performance. This phenomenon leads to a necessity of study on energy consumption control strategies under real-world driving conditions. This paper proposes a new approach for energy management optimization of plug-in hybrid electric vehicles based on real-world driving data for two purposes. One is for improving the energy consumption of PHEV under real-world driving conditions and the other is for reducing the computational complexity of optimization methods in simulation models. In this process, the paper collected real-world driving record data from 180 drivers within 6 months. Then the principal component analysis (PCA) was employed to extract and define the hidden factors from the initial real-world driving data.

In the current work, unidirectional (UD) carbon fiber composite hatsection component with two different layups are studied under dynamic three-point bending loading. The experiments are performed at various impact velocities, and the effects of impactor velocity and layup on acceleration histories are compared. A macro model is established with LS-DYNA for a more detailed study. The simulation results show that the delamination plays an important role during dynamic three-point bending test. Based on the analysis with a high-speed camera, the sidewall of hatsection shows significant buckling rather than failure. Without considering the delamination, the current material model cannot capture the post-failure phenomenon correctly. The sidewall delamination is modeled by assumption of larger failure strain together with slim parameters, and the simulation results of different impact velocities and layups match the experimental results reasonably well.

Using highly efficient powertrain is one of the most important and effective approaches to increase the driving distance of electric vehicles (EVs). In this paper, a novel two-speed dual-clutch transmission (DCT) is proposed. The transmission is comprised of two traditional friction clutches and two-stage planetary gear sets. One clutch connects the input sun gear and the other connects the input carrier. The Simulink models including an electric motor and two-speed DCT are established. Gearshift schedule based on fuzzy logic which reflects the driver’s intensions is adopted to improve the dynamic and economic performance of the novel transmission. The simulation model is built using MATLAB/Simulink® to validate the effectiveness of the proposed gearshift schedule compared with the conventional two-parameter gearshift schedule. Simulation results show that both the dynamic and economic performance of the novel DCT for EVs are improved with the proposed fuzzy logic gearshift schedule.

The interaction of fuel sprays and in-cylinder flow in direct-injection engines is expected to alter kinetic energy and integral length scales at least during some portions of the engine cycle. High-speed particle image velocimetry was implemented in an optical four-valve, pent-roof spark-ignition direct-injection single-cylinder engine to quantify this effect. Non-firing motored engine tests were performed at 1300 RPM with and without fuel injection. Two fuel injection timings were investigated: injection in early intake stroke represents quasi-homogenous engine condition; and injection in mid compression stroke mimics the stratified combustion strategy. Two-dimensional crank angle resolved velocity fields were measured to examine the kinetic energy and integral length scale through critical portions of the engine cycle. Reynolds decomposition was applied on the obtained engine flow fields to extract the fluctuations as an indicator for the turbulent flow.

Stringent particulate emission regulations are applied to spark-ignition direct-injection (SIDI) engines, calling for a significant in-cylinder reduction of soot particles. To enhance fundamental knowledge of the soot formation and oxidation process inside the cylinder of the engine, a new in-flame particle sampling system has been developed and implemented in a working optical SIDI engine with a side-mounted, wall-guided injection system. Using the sampling probes installed on the piston top, the soot particles are directly sampled from the petrol flame for detailed analysis of particle size distribution, structure, and shape. At the probe tip, a transmission electron microscope (TEM) grid is stored for the soot collection via thermophoresis, which is imaged and post-processed for statistical analysis. Simultaneously, the flame development was recorded using two high-speed cameras to evidence the direct exposure of the sampling grids to the soot-laden diffusion flames and pool fires.

It is well known that engine downsizing is still the main energy-saving technology for spark-ignition direct-injection (SIDI) engine. However, with the continuous increase of the boosting ratio, the gasoline engine is often accompanied by the occurrence of knocking, which has the drawback to run the engine at retarded combustion phasing. Besides, in order to protect the turbine blades from being sintered by high exhaust temperature, the strategies of fuel enrichment are often taken to reduce the combustion temperature, which ultimately leads to a high level of particulate number emission. Therefore, to address the issues discussed above, the port water injection (PWI) techniques on a 1.2-L turbocharged, three-cylinder, SIDI engine were investigated. Measurements indicate that the optimization of spark timing has a significant impact on its performance.

With the rapid development of series-parallel hybrid electric vehicles (SPHEVs), mode transition from pure electrical drive to hybrid drive has attracted considerable attention. The presence of time delay due to response capacity of actuators and signal transmission of communication may cause decrease of speed tracking accuracy, even instable dynamics. Consequently, drivability of the SPHEV is unacceptable, and durability of the components is reduced. So far, plenty of control strategies have been proposed for mode transition, however, no previous research has been reported to deal with the time delay during mode transition. In this paper, a dynamic model with time delay of hybrid electric system is established. Next, a mode transition time-delay controller is proposed based on a two degree of freedom internal model controller (2-DOF-IMC).

Vehicle lateral states such as lateral distance at a preview point and heading angle are indispensable for lane keeping control systems, and such states are normally estimated by fusing signals from an onboard vision system and inertial sensors. However, the sampling rates and measurement delays are different between the two kinds of sensing devices. Most of the conventional methods simply neglect measurement delay and reduce sampling rate of the estimator to adapt to the slow sensors/devices. However, the estimation accuracy is deteriorated, especially considering the delay of visual signals may not be constant. In case of electric vehicles, the actuators for steering and traction are motors that have high control frequency. Therefore, the frequency of vehicle state feedback may not match the control frequency if the estimator is infrequently updated. In this paper, a multi-rate estimation algorithm based on Kalman filter is proposed to provide lateral states with high frequency.

The internal combustion engine (ICE) is a typical complex multidisciplinary system which requires the support of precision design and manufacturing. To achieve a better performance of ICEs, tolerance assignment, or tolerance design, plays an important role. A novel multi-disciplinary tolerance design optimization problem considering two important disciplines of ICEs, the compression ratio and friction loss, is proposed and solved in this work, which provides a systematic procedure for the optimal determination of tolerances and overcomes the disadvantages of the traditional experience-based tolerance design. A bi-disciplinary analysis model is developed in this work to assist the problem solving, within which a model between the friction loss and tolerance is built based on the Gaussian Process using the corresponding simulation and experimental data.

Manufacturing of the internal combustion engines (ICEs) has very critical requirements on the precision and tolerance of engine parts in order to guarantee the engine performance. As a typical complex nonlinear system, small changes in dimensions of ICE components may have great impact on the performance and cost of the manufacturing of ICES. In this regard, it is still necessary to discuss the optimization of the tolerance and manufacturing precision of the critical components of ICEs even though the tolerance optimization in general has been reported in the literature. A systematic process for determining optimal tolerances will overcome the disadvantages of the traditional experience-based tolerance design and therefore improve the system performance.

Because of rising complexity during the automotive product development process, the number of disciplines to be concerned has been significantly increased. Multidisciplinary design optimization (MDO) methodology, which provides an opportunity to integrate each discipline and conduct compromise searching process, is investigated and introduced to achieve the best compromise solution for the automotive industry. To make a better application of MDO, the suitable coupling strategy of different disciplines and efficient optimization techniques for automotive design are studied in this article. Firstly, considering the characteristics of automotive load cases which include many shared variables but rare coupling variables, a multilevel MDO coupling strategy based on enhanced collaborative optimization (ECO) is studied to improve the computational efficiency of MDO problems.

Port water injection is considered as a promising strategy to further improve the combustion performance of internal combustion engines for its benefit in knock resistance by reducing the cylinder temperature. A thorough investigation of the port water injection technique is required to fully understand its effects on the engine combustion process. This study explores the potential of the port water injection technique in improving the performance of a turbo charged Gasoline Direct Injection engine. A 3D computational fluid dynamics model is applied to simulate the in-cylinder mixing and combustion for this engine both with and without water injection. Different water injection timings are investigated and it is found that the injection timing greatly effects the mass of water which enters the combustion chamber, both in liquid and vapor form.

The numerical simulation of ice melting process on an iced helicopter rotor blade is presented. The ice melting model uses an enthalpy-porosity formulation, and treats the liquid-solid mushy zone as a porous zone with porosity equal to the liquid fraction. The ice shape on the blade section is obtained by the icing code with a dynamic mesh module. Both of the temperature change and the ice-melting process on the rotor blade section surface are analyzed. The phenomenon of ice melting is analyzed through the change of temperature and liquid fraction on the abrasion/ice interface. The liquid fraction change as with time on the abrasion/ice surface is observed, which describes the ice-melting process well. The numerical results show that the ice melting process can be simulated effectively by the melting model. The de-icing process can be monitored by observing the change of the liquid fraction of the area around the abrasion/ice interface.