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Hydrogen exhibits the notable attribute of lacking carbon dioxide emissions when used in internal combustion engines. Nevertheless, hydrogen has a very low energy density per unit volume, along with large emissions of nitrogen oxides and the potential for backfire. Thus, stratified charge combustion (SCC) is used to reduce nitrogen oxides and increase engine efficiency. Although SCC has the capacity to expand the lean limit, the stability of combustion is influenced by the mixture formation time (MFT), which determines the equivalence ratio. Therefore, quantifying the equivalence ratio under different MFT is critical since it determines combustion characteristics. This study investigates the viability of using a Laser Induced Breakdown Spectroscopy (LIBS) for measuring the jet equivalence ratio. Furthermore, study was conducted to analyze the effect of MFT and the double injection parameter, namely the dwell time and split ratio, on the equivalence ratio.

Electrification of off-road vehicle powertrains can increase mobility, improve energy efficiency, and enable new utility by providing high amounts of electrical power for auxiliary devices. These vehicles often operate in extreme temperature conditions at low ground speeds and high power levels while also having significant cooling airpath restrictions. The restrictions are a consequence of having grilles and/or louvers in the airpath to prevent damage from the operating environment. Moreover, the maximum operating temperatures for high voltage electrical components, like batteries, motors, and power-electronics, can be significantly lower than those of the internal combustion engine. Rejecting heat at a lower temperature gradient requires higher flow rates of air for effective heat exchange to the operating environment at extreme temperature conditions.

To meet the target of the CO2 regulations, it is mandatory to replace high-carbon fossil fuels with low-carbon fuels. Diesel/Natural Gas (NG) reactivity-controlled compression ignition (RCCI) can reduce CO2 emission, which stratifies two types of fuels with different reactivity. And also, RCCI produces less NOx and particulate matter emissions by reducing the in-cylinder temperature. However, RCCI must still be enhanced in terms of the thermal and combustion efficiencies at low and medium loads. In this work, a modified piston geometry was applied to improve the RCCI combustion. The piston geometry was designed to minimize heat loss and reduce flame quenching in an RCCI engine. Experiments were conducted using a single-cylinder engine with a displacement volume of 1,000 cc. Diesel was directly injected into the cylinder, and NG was fed through the intake port.

Drive cycles are a core piece of vehicle development testing methodology. The control and calibration of the vehicle is often tuned over drive cycles as they are the best representation of the real-world driving the vehicle will see during deployment. To obtain general performance numerous drive cycles must be generated to ensure final control and calibration avoids overfitting to the specifics of a single drive cycle. When real-world driving cycles are difficult to acquire methods can be used to create statistically similar synthetic drive cycles to avoid the overfitting problem. This subject has been well addressed within the passenger vehicle domain but must be expanded upon for utilization with tracked off-road vehicles. Development of hybrid tracked vehicles has increased this need further. This study shows that turning dynamics have significant influence on the vehicle power demand and on the power demand on each individual track.

In marine or stationary engines, consistent engine performance must be guaranteed for long-haul operations. A dual-fuel combustion strategy was used to reduce the emissions of particulates and nitrogen oxides in marine engines. However, in this case, the combustion stability was highly affected by environmental factors. To ensure consistent engine performance, the in-cylinder pressure measured by piezoelectric pressure sensors is generally measured to analyze combustion characteristics. However, the vulnerability to thermal drift and breakage of sensors leads to additional maintenance costs. Therefore, an indirect measurement via a reconstruction model of the in-cylinder pressure from engine block vibrations was developed. The in-cylinder pressure variation is directly related to the block vibration; however, numerous noise sources exist (such as, valve impact, piston slap, and air flowage).

Opposed-piston two-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a conventional four-stroke diesel engine. However, the uniflow scavenging process is difficult to control over a wider range of speed and loads due to its sensitivity to pressure dynamics, port timings, and port design. Specifically, the angle of the intake ports can be used to generate swirl which has implications for open and closed cycle effects. This study proposes an analysis of the effects of port angle on the in-cylinder flow distribution and combustion performance of an OP-2S using computational fluid dynamics engine. Large Eddy Simulation (LES) was used to model turbulence given its ability to predict in-cylinder mixing and cyclic variability. A three-cylinder model was validated to experimental data collected by Achates Power and the grid was verified using an LES quality approach from the literature.

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios.

The application of dual-fuel combustion in the freight transportation sectors has received considerable attention due to the capability of achieving higher fuel efficiency and less pollutant emissions than the conventional diesel engines. In this study, high-speed flame visualization was used to investigate the phenomena of natural gas/diesel dual-fuel combustion in a single-cylinder heavy-duty engine with optical access. To implement diverse fuel blending conditions, diesel injection timing and natural gas substitution ratio were varied under constant fuel energy input. A novel flame regime separation method was implemented based on color segmentation in HSV color space to characterize the spatial distributions of premixed and non-premixed flame regimes. Flame images for larger natural gas substitution showed a significant reduction in the non-premixed flame regime accompanied by flame propagation along the vaporized diesel sprays.

The use of hydrogen as a possible fuel for internal combustion (IC) engines can help build a society with a clean transportation framework. Diluting the in-cylinder mixture can improve the efficiency of the engines. To prove the validity of lean burn in hydrogen IC engines, three different combustion modes are investigated in this study. The engine experiments are conducted in a spray-guided direct-injection (DI) spark-ignition engine with 10 MPa of hydrogen DI. When lean burn is applied to a hydrogen IC engine, the characteristics of pumping and heat transfer loss improve. The improvement in heat transfer loss is more significant than the reduction in negative pumping work for the indicated thermal efficiency. Among the three combustion modes, stratified charge combustion (SCC) develops the maximum indicated mean effective pressure. However, this mode deteriorates the combustion stability slightly. The nitrogen oxide emission is reduced when the excess air ratio is increased.

Substantial fuel economy improvements for light-duty automotive engines demand novel combustion strategies. Low temperature combustion (LTC) demonstrates potential for significant fuel efficiency improvement; however, control complexity is an impediment for real-world transient operation. Spark-assisted compression ignition (SACI) is an LTC strategy that applies a deflagration flame to generate sufficient energy to trigger autoignition in the remaining charge. Operating a practical engine with SACI combustion is a key modeling and control challenge. Current models are not sufficient for control-oriented work such as calibration optimization, transient control strategy development, and real-time control. This work describes the process and results of developing a fast-running control-oriented model for the autoignition phase of SACI combustion. A data-driven model is selected, specifically artificial neural networks (ANNs).

This research proposes to leverage the fast response time of Permanent Magnet Synchronous Motors (PMSMs) to compensate for crank angle resolved engine torque variations caused by cycle-by-cycle combustion variations. This method reduces powertrain vibration and enables engine calibrations with high combustion variation that produces low fuel consumption. This research integrates a Field Oriented Control (FOC) strategy with an Explicit Model Predictive Control (EMPC) to trace previewed current references. The previewed current references are computed from the engine torque difference between predicted nominal operation and the measured torque output. This research reveals that the MPC can track a d-q current reference without overshoot, rendering current magnitude constraints unnecessary in the MPC formulation. A control rate penalty is used to tune the aggressiveness of transient voltage demand and meet with the DC voltage limit.

Spark-assisted compression ignition (SACI) leverages flame propagation to trigger autoignition in a controlled manner. The autoignition event is highly sensitive to several parameters, and thus, achieving SACI in production demands a high tolerance to variations in conditions. Limited research is available to quantify the combustion response of SACI to these variations. A simulation study is performed to establish trends, limits, and control implications for SACI combustion over a wide range of conditions. The operating space was evaluated with a detailed chemical kinetics model. Key findings were synthesized from these results and applied to a 1-D engine model. This model identified performance characteristics and potential actuator positions for a production-viable SACI engine. This study shows charge preparation is critical and can extend the low-load limit by strengthening flame propagation and the high-load limit by reducing ringing intensity.

Accurate estimation of engine state(s) is vital for engine control systems to achieve their designated objectives. The fusion of sensors can significantly improve the estimation results in terms of accuracy and precision. This paper investigates using an Extended Kalman Filter (EKF) to estimate engine state(s) for Spark Ignited (SI) engines with the external EGR system. The EKF combines air path sensors with cylinder pressure feedback through a control-oriented engine cycle domain model. The model integrates air path dynamics, torque generation, exhaust gas temperature, and residual gas mass. The EKF generates a cycle-based estimation of engine state(s) for model-based control algorithms, which is not the focus of this paper. The sensor and noise dynamics are analyzed and integrated into the EKF formulation. To account for ‘non-white’ disturbances including modeling errors and sensor/actuator offset, the EKF engine state(s) observer is augmented with disturbance state(s) estimation.

This paper seeks to identify key input parameters needed to achieve a production-viable control strategy for spark-assisted compression ignition (SACI) engines. SACI is a combustion strategy that uses a spark plug to initiate a deflagration flame that generates sufficient ignition energy to trigger autoignition in the remaining charge. The flame propagation phase limits the rate of cylinder pressure rise, while autoignition rapidly completes combustion. High dilution within the autoignited charge is generally required to maintain reaction rates feasible for production. However, this high dilution may not be reliably ignited by the spark plug. These competing constraints demand novel mixture preparation strategies for SACI to be feasible in production. SACI with charge stratification has demonstrated sufficiently stable flame propagation to reliably trigger autoignition across much of the engine operating map.

Lean stratified combustion is a mean to dilute the fuel-air mixture leaner than stoichiometric ratio, by using stratification of fuel gradient in a spark ignition engine. Under the lean stratified combustion, differed from the stoichiometric homogeneous charge combustion, flame could propagate through extremely rich air-fuel mixture, while the global air-fuel mixture is under lean condition. The rich mixture causes considerable amount of particulate matter, but, due to large effect of efficiency improvement, the attractive point is on fuel economy compare to homogeneous charge SI combustion. The easiest way to reduce particulate matter is changing fuel to gaseous hydrocarbon, to minimize evaporating and mixing period.

Modern turbocharged spark-ignition engines are being equipped with an increasing number of control actuators to meet fuel economy, emissions, and performance targets. The response time variations between engine control actuators tend to be significant during transients and necessitate highly complex actuator scheduling routines. Model Predictive Control (MPC) has the potential to significantly reduce control calibration effort as compared to the current methodologies that are based on decentralized feedback control strategies. MPC strategies simultaneously generate all actuator responses by using a combination of current engine conditions and optimization of a control-oriented plant model. To achieve real-time control, the engine model and optimization processes must be computationally efficient without sacrificing effectiveness. Most MPC systems intended for real-time control utilize a linearized model that can be quickly evaluated using a sub-optimal optimization methodology.

Non-linear model predictive engine control (nMPC) systems have the ability to reduce calibration effort while improving transient engine response. The main drawback of nMPC for engine control is the computational power required to realize real-time operation. Most of this computational power is spent linearizing the non-linear plant model at each time step. Additionally, the effectiveness of the nMPC system relies heavily on the accuracy of the model(s) used to predict the future system behavior, which can be difficult to model physically. This paper introduces a hybrid modeling approach for internal combustion engines that combines physics-based and machine learning techniques to generate accurate models that can be linearized with low computational power. This approach preserves the generalization and robustness of physics-based models, while maintaining high accuracy of data-driven models. Advantages of applying the proposed model with nMPC are discussed.

Lean stratified combustion shows high potential to reduce fuel consumption because it operates without the intervention of a throttle valve. Despite its high fuel economy potential, it emits large amounts of particulate matter (PM) because the locally rich mixture is formed at the periphery of a spark plug. Furthermore, the combustion phasing angle is not realized at MBT ignition timing, which can bring high work conversion efficiency. Since PM emission and work conversion efficiency are in a trade-off relation, this research focused on reducing PM emission through achieving high work conversion efficiency. Two intake air control strategies were examined in this research; throttle operation and late intake valve closing (LIVC). The experiment was conducted in a single cylinder spray-guided direct injection spark ignition (SG-DISI) engine with liquefied petroleum gas (LPG). The injected fuel amount was fixed so as to investigate the effect of each strategy.

Knocking combustion limits the downsized gasoline engines’ potential for improvement with regard to fuel economy. The high in-cylinder pressure and temperature caused by the adaptation of a turbocharger aggravates the tendency of the end-gas to autoignite. Thus, the knocking combustion does not allow for further advancing of the combustion phase. In this research, the effects of the ignition and valve timings on knocking combustion were investigated under steady-state conditions. Moreover, the optimal ignition and valve timings for the transient operations were derived with the aim of a greater fuel economy improvement, based on the steady-state analysis. A 2.0 liter turbocharged gasoline direct injection engine with continuously variable valve timing (CVVT), was utilized for this experiment. 2, 10, and 18 bar brake mean effective pressure (BMEP) load conditions were used to represent the low, medium, and high load operations, respectively.

The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only.