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Alternative propulsion systems such as renewable fuels and electric powertrains are expensive; thus, efficient internal combustion engines (ICE) with hybrid powertrains still play significant roles in the transportation fleet in the coming decades. Modern engine technologies have been adopted to meet stringent emissions and fuel economy standards. As a result, engine control systems are becoming more complex. Furthermore, as ICE control parameters increase exponentially, engine calibration and design become bottlenecks in the development process. While a map-based feed-forward control method is a current de facto standard in combustion control, online closed-loop feedback control can improve engine performance and robustness. However, adding physical sensors to measure the various data for the online feedback control and calibration increase the vehicle cost.

For a series hybrid electric vehicle (SHEV), the electric motor is responsible for driving the wheels, while the engine drives the only generator to provide electricity. SHEVs set a control strategy to make the engine run near the fixed operating point with high thermal efficiency, thereby effectively reducing fuel consumption. The powertrain system of HEV is more complex than that of a conventional drive system using only an internal combustion engine, and it is time-consuming to obtain the optimal components specification values and control parameters. Therefore, automatic optimization methods are required nowadays. We used Genetic Algorithm (GA) as the optimization method and optimize powertrain specifications and control parameter values to reduce fuel consumption. The results show that it is an effective optimization method.

The purpose of this paper is to build up a model for predicting turbulent burning velocity which can be used for One-Dimensional (1D) engine simulation. This paper presents the relationship between turbulent burning velocity, the Karlovitz number, and the Markstein number for building up the prediction model. The turbulent burning velocity was measured using a single-cylinder gasoline engine, which has an external Exhaust Gas Recirculation (EGR) system. In the experiment, various engine operating parameters, e.g. engine loads and EGR rates, and various engine specifications, i.e. different types of intake ports were tested. The Karlovitz number was calculated using Three-Dimensional Computational Fluid Dynamics (3D-CFD) and detailed chemical kinetics simulation with a premixed laminar flame model. The Markstein number was also calculated using detailed chemical kinetics simulation with the Extinction of Opposed-flow Flame model.

The purpose of the present study is to find a way to extend a combustion stability limit for diluted combustion in a spark-ignition (SI) gasoline engine which has a high compression ratio. This paper focuses on partial oxidation in an unburned mixture which is observed in the high compression engine and clarifies the effect of partial oxidation in an unburned mixture on the behavior of a flame stretch and the extinction limit. The behavior of the flame stretch was simulated using the detailed chemical kinetics simulation with the opposed-flow flame reactor model. In the simulation, the reactants which have various reaction progress variables were examined to simulate the flame stretch and extinction under the partial oxidation conditions. The mixtures were also diluted by complete combustion products which represent exhaust gas recirculation (EGR).

This paper describes the development of an integrated simulation model for evaluating the effects of electrically heating the three-way catalyst (TWC) in a series hybrid electric vehicle (s-HEV) on fuel economy and exhaust gas purification performance. Engine and TWC models were developed in GT-Power to predict exhaust emissions during transient operation. These models were validated against data from vehicle tests using a chassis dynamometer and integrated into an s-HEV model built in MATLAB/Simulink. The s-HEV model accurately reproduced the performance characteristics of the vehicle’s engine, motor, generator, and battery during WLTC mode operation. It can thus be used to predict the fuel consumption, emissions, and performance of individual powertrain components. The engine combustion characteristics were reproduced with reasonable accuracy for the first 50 combustion cycles, representing the cold-start condition of the driving mode.

Performance predictions of advanced turbocharged engines are becoming difficult because conventional engine models are built using performance map data of turbochargers with a proportional integral derivative (PID) controller. Improving prediction capabilities under transient test cycles or real driving conditions is a challenging task. This study applies a machine learning technique to predict turbocharger performances with high accuracy under steady-state and transient conditions. The manipulated signals of engine speed and torque created based on Compressed High-Intensity Radiated Pulse (Chirp signal) and Amplitude-modulated Pseudo-Random Binary Signal (APRBS) are used as inputs to the engine testbed. Data from the engine experiments are used as training data for the AI-based turbocharger model. High prediction accuracy of the AI turbocharger model is achieved with the co-efficient of determination in the model, and cross-validation results are higher than 0.8.

Emission regulations are becoming tighter, and Real Driving Emissions (RDE) is proposed as a testing cycle for evaluating modern engine emissions under a wide operation range. For this reason, engine manufacturers have been developing a method to effectively assess engine performances and emissions under a wide range of transient conditions. Transient engine performances can be evaluated efficiently by applying time-series data created by chirp signals. However, when the time-series data produced by the chirp signal are used directly, the engine hardware may damage, and emission performances deteriorate drastically. It is therefore essential to develop a method to avoid these undesirable operating conditions. This work aims to develop an algorithm to avoid such unrealistic operation conditions for engine performance evaluation. A virtual diesel engine (VDE) model is developed based on a four-cylinder engine using GT-POWER software.

Natural gas has been used in spark-ignition (SI) engines of natural gas vehicles (NGVs) due to its resource availability and stable price compared to gasoline. It has the potential to reduce carbon monoxide emissions from the SI engines due to its high hydrogen-to-carbon ratio. However, short running distance is an issue of the NGVs. In this work, methodologies to improve the fuel economy of a heavy-duty commercial truck under the Japanese Heavy-Duty Driving Cycle (JE05) is proposed by numerical 1D-CFD modeling. The main objective is a comparative analysis to find an optimal fuel economy under three variable mechanisms, variable valve timing (VVT), variable valve actuation (VVA), and variable compression ratio (VCR). Experimental data are taken from a six-cylinder turbocharged SI engine fueled by city gas 13A. The 9.83 L production engine is a CR11 type with a multi-point injection system operated under a stoichiometric mixture.

Battery models are being developed as a component of the powertrain systems of hybrid electric vehicles (HEVs) to predict the state of charge (SOC) accurately. Electrically heated catalysts (EHCs) can be employed in the powertrains of HEVs to reach the catalyst light off temperature in advance. However, EHCs draw power from the battery pack and hence sufficient energy needs to be stored to power auxiliary components. In series HEVs, the engine is primarily used to charge the battery pack. Therefore, it is important to develop a control strategy that triggers engine start/stop conditions and reduces the frequency of engine operation to minimize the equivalent fuel consumption. In this study, a battery pack model was constructed in MATLAB-Simulink to investigate the SOC variation of a high-power lithium ion battery during extreme engine cold start conditions (-7°C) with/without application of an EHC.

Pre-chamber spark ignition technology can stabilize combustion and improve thermal efficiency of lean burn natural gas engines. During compression stroke, a homogeneous lean mixture is introduced into pre-chamber, which separates spark plug electrodes from turbulent flow field. After the pre-chamber mixture is ignited, the burnt jet gas is discharged through multi-hole nozzles which promotes combustion of the lean mixture in the main chamber due to turbulence caused by high speed jet and multi-points ignition. However, details mechanism in the process has not been elucidated. To design the pre-chamber geometry and to achieve stable combustion under the lean condition for such engines, it is important to understand the fundamental aspects of the combustion process. In this study, a high-speed video camera with a 306 nm band-pass filer and an image intensifier is used to visualize OH* self-luminosity in rapid compression-expansion machine experiment.

In order to improve thermal efficiency of spark ignition (SI) engines, an improved technology to avoid irregular combustion under high load conditions of high compression ratio SI engines is required. In this study, the authors focused on high pressure gasoline direct injection in a high compression ratio SI engine, which its rapid air-fuel mixture formation, turbulence, and flame speed, are enhanced by high-speed fuel spray jet. Effects of fuel injection pressure, injection and spark ignition timing on combustion characteristics were experimentally and numerically investigated. It was found that the heat release rate was drastically increased by raising the fuel injection pressure. The numerical simulation results show that the high pressure gasoline direct injection enhanced small-scale turbulent intensity and fuel evaporation, simultaneously.

This paper focuses on an assessment of predictive combustion model using a 0D/1D simulation tool under high load, different excess air ratio λ , and different combustion stabilities (based on coefficient of variation of indicated mean effective pressure COVimep). To consider that, crank angle resolved data of experimental pressure of 500 cycles are recorded under engine speed 1000 RPM and 2000 RPM, wide-open throttle, and λ=1.0, 1.42, 1.7, and 2.0. Firstly, model calibration is conducted using 18 cases at 2000 RPM using 500 cycle-averaged in-cylinder pressure to find optimized model constants. Then, the model constants are unchanged for other cases. Next, different cycle-averaged pressure data are used as inputs in the simulation based on the COVimep for studying sensitivity of the turbulent model constants. The simulation is conducted using 1D simulation software GT-Power.

Experimental and numerical studies on the combustion of the particulate matter in the diesel particulate filter with the particulate matter loaded under different particulate matter loading condition were carried out. It was observed that the pressure losses through diesel particulate filter loaded with particulate matter having different mean aggregate particle diameters during both particulate matter loading and combustion periods. Diesel particulate filter regeneration mode was controlled with introducing a hot gas created in Diesel Oxidation Catalyst that oxidized hydrocarbon injected by a fuel injector placed on an exhaust gas pipe. The combustion amount was calculated with using a total diesel particulate filter weight measured by the weight meter both before and after the particulate matter regeneration event.

With the development of downsized spark ignition (SI) engines, low-speed pre-ignition (LSPI) has been observed more frequently as an abnormal combustion phenomenon, and there is a critical need to solve this issue. It has been acknowledged that LSPI is not directly triggered by autoignition of the fuel, but by some other material with a short ignition delay time. It was previously reported that LSPI can be caused by droplets of lubricant oil intermixed with the fuel. In this work, the ignition behavior of lubricant component containing fuel droplets was experimentally investigated by using a constant volume chamber (CVC) and a rapid compression and expansion machine (RCEM), which enable visualization of the combustion process in the cylinder. Various combinations of fuel compositions for the ambient fuel-air mixture and fractions of base oil/metallic additives/fuel for droplets were tested.

In the conventional approval test method of fuel consumption for heavy-duty diesel vehicles currently in use in Japan, the fuel consumption under the transient test cycle is calculated by integrating the instantaneous fuel consumption rate referred from a look-up table of fuel consumptions measured under the steady state conditions of the engine. Therefore, the transient engine performance is not considered in this conventional method. In this study, a highly accurate test method for fuel consumption in which the map-based fuel consumption rate is corrected using the transient characteristics of individual engines was developed. The method and its applicability for a heavy-duty diesel engine that complied with the Japanese 2009 emission regulation were validated.

Primary work is to investigate premixed laminar flame propagation in a constant volume chamber of iso-octane/air combustion. Experimental and numerical results are investigated by comparing flame front displacements under lean to rich conditions. As the laminar flame depends on equivalence ratio, temperature, and pressure conditions, it is a main property for chemical reaction mechanism validation. Firstly, one-dimensional laminar flame burning velocities are predicted in order to validate a reduced chemical reaction mechanism. A set of laminar burning velocities with pressure, temperature, and mixture equivalence ratio dependences are combined into a 3D-CFD calculation to compare the predicted flame front displacements with that of experiments. It is found that the reaction mechanism is well validated under the coupled 1D-3D combustion calculations. Next, lean experiments are operated in a SI engine by boosting intake pressure to maintain high efficiency without output power penalty.

A mixed time-scale subgrid large eddy simulation was used to simulate mixture formation, combustion and soot formation under the influence of turbulence during diesel engine combustion. To account for the effects of engine wall heat transfer on combustion, the KIVA code's standard wall model was replaced to accommodate more realistic boundary conditions. This were carried out by implementing the non-isothermal wall model of Angelberger et al. with modifications and incorporating the log law from Pope's method to account for the wall surface roughness. Soot and NOx emissions predicted with the new model are compared to experimental data acquired under various EGR conditions.

Experimental and numerical studies were conducted on diesel particulate filters (DPFs) under different soot loading conditions and DPF configurations. Pressure drops across DPFs with various mean pore diameters loaded with soots having different mean particle diameters were measured by introducing exhaust gases from a 2.2 liter inline four-cylinder, TCI diesel engine designed for use in passenger cars. A mechanistic hypothesis was then proposed to explain the observed trends, accounting for the effects of the soot loading regime in the wall and the soot cake layer on the pressure drop. This hypothesis was used to guide the development and validation of a numerical model for predicting the pressure drop in the DPF. The relationship between the permeability and the porosity of the wall and soot cake layer was modeled under various soot loading conditions.

Natural gas is a promising alternative fuel for internal combustion engines because of its clean combustion characteristics and abundant reserves. However, it has several disadvantages due to its low energy density and low thermal efficiency at low loads. Thus, to assist efforts to improve the thermal efficiency of spark-ignited (SI) engines operating on natural gas and to minimize test procedures, a numerical simulation model was developed to predict and optimize the performance of a turbocharged test engine, considering flame propagation, occurrence of knock and ignition timing. The numerical results correlate well with empirical data, and show that increasing compression ratios and retarding the intake valve closing (IVC) timing relative to selected baseline conditions could effectively improve thermal efficiency. In addition, employing moderate EGR ratios is also effective for avoiding knock.

Urea SCR (Selective Catalytic Reduction) exhaust after-treatment systems are one of the most promising measures to reduce NOx emissions from diesel engines. Both Cu-zeolite (Cu-SCR) and Fe-zeolite (Fe-SCR) urea SCR systems have been studied extensively but not many detailed studies have been conducted on the combination of both systems. Thus, we carried out studies on such Combined-SCR systems and their capability to reduce NOx under various engine operating conditions. We also conducted transient engine tests using different catalyst systems to compare their performance. The results show that combined-SCR systems can reduce NOx more effectively than Fe-SCR or Cu-SCR alone. The best NOx reduction performance was achieved at a Cu ratio of 0.667 (i.e. Fe: Cu =1: 2). Combined-SCR thus apparently benefits from the characteristics of both Cu-SCR and Fe-SCR, allowing it to reduce NOx over a wide range of operating conditions.