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A numerical modeling technique is proposed for computer simulations of high speed valve train dynamics. The dynamic terms in the valve spring reaction forces are calculated using linear vibration theory for given kinematic valve motions. Because the spring dynamics are analyzed before the time stepping integration, spring surge phenomena can be included without using additional computer time. Consequently, valve train dynamics can be simulated very quickly without noticeable errors in accuracy. The experimental results prove the computer model developed here is accurate and also computationally efficient.

This paper describes the development of small rotary internal combustion engines developed to operate on the High Efficiency Hybrid Cycle (HEHC). The cycle, which combines high compression ratio (CR), constant-volume (isochoric) combustion, and overexpansion, has a theoretical efficiency of 75% using air-standard assumptions and first-law analysis. This innovative rotary engine architecture shows a potential indicated efficiency of 60% and brake efficiency of >50%. As this engine does not have poppet valves and the gas is fully expanded before the exhaust stroke starts, the engine has potential to be quiet. Similar to the Wankel rotary engine, the ‘X’ engine has only two primary moving parts - a shaft and rotor, resulting in compact size and offering low-vibration operation. Unlike the Wankel, however, the X engine is uniquely configured to adopt the HEHC cycle and its associated efficiency and low-noise benefits.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

Gasoline compression ignition (GCI) technology shows the potential to obtain high thermal efficiencies while maintaining low soot and NOx emissions in light-duty engine applications. Recent experimental studies and numerical simulations have indicated that high reactivity gasoline-like fuels can further enable the benefits of GCI combustion. However, there is limited empirical data in the literature studying the gasoline compression ignition process at relevant in-cylinder conditions, which are required for further optimizing combustion system designs. This study investigates the temporal and spatial evolution of the compression ignition process of various high reactivity gasoline fuels with research octane numbers (RON) of 71, 74 and 82, as well as a conventional RON 97 E10 gasoline fuel. A ten-hole prototype gasoline injector specifically designed for GCI applications capable of injection pressures up to 450 bar was used.

Hydrogen internal combustion engines (H2ICE) offer a cost-effective solution to decarbonize transport by combining a lower carbon intensity fuel with mature and established internal combustion engine technology. While vehicles running with hydrogen have been demonstrated over the years, this fuel's physical and chemical properties require modifications and upgrades on the vehicle from an engine and system-level perspective. In addition, market-specific regulatory and economic factors can also constrain the realization of optimal hydrogen powertrain architectures. Therefore, this paper reviews the impact of hydrogen use on combustion, injection, air management, and after-treatment systems, indicating the different strategies used to enable effective H2ICE strategies from an efficiency, cost, and safety standpoint.

This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping.

As vehicles are getting electrified and more intelligent, the energy consumption of the auxiliary system increases rapidly. The auxiliary battery acts as the backbone of the system to support the proper operation of the vehicle. It is important to ensure the auxiliary battery has enough energy to meet the basic loads regardless the vehicle is in park or running. However, the existing methods only focus on auxiliary energy management when the vehicle is in a dynamic event. To fulfill the gap, we propose an intelligent strategy that detects the low state of charge (SOC) condition, temporarily turns down the auxiliary loads based on their priorities and charges the auxiliary battery at the maximum efficiency of the auxiliary power unit. In addition, the proposed strategy allows the vehicle to get the park duration update and make intelligent decisions on charging the auxiliary battery.

This paper introduces an intelligent energy distribution scheme for series plug-in hybrid electric vehicles (PHEVs) which incorporates the complexity of human driving behavior. Driving styles can have a significant impact on fuel consumption, but it is often unclear how one should drive to get the optimal fuel efficiency. Hybrid electric vehicles have been shown to improve fuel economy, reduce vehicle emissions and maintain drivability by incorporating electric motors into the drivetrain. Due to the highly complex system design and vehicle architecture, sophisticated energy management strategies (EMS) are required to optimize the vehicle performance. Currently, the power management system is based on static thresholds optimized on a fixed drive cycle for a given vehicle. This paper introduces an adaptive control method for EMS utilizing the complexity of human driving patterns for energy distribution in a series PHEV.

Reynolds-averaged Navier-Stokes (RANS) turbulence model has been used extensively for diesel engine simulations due to its computational efficiency and is expected to remain the workhorse computational fluid dynamics (CFD) tool for industry in the near future. Alternatively, large eddy simulations (LES) can potentially deal with complex flows and cover a large disparity of turbulence length scales, which makes this technique more and more attractive in the engine community. An n-dodecane spray flame (Spray A from Engine Combustion Network) was simulated using a dynamic structure LES model to understand the transient behavior of this turbulent flame. The liquid spray was treated with a traditional Lagrangian method and the gas-phase reaction was closed using a delta probability density function (PDF) combustion model. A 103-species skeletal mechanism was used for n-dodecane chemical kinetic model.

This research evaluates the capability of data-science models to classify the combustion events in Cooperative Fuel Research Engine (CFR) operated under Homogeneous Charge Compression Ignition (HCCI) conditions. A total of 10,395 experimental data from the CFR engine at the University of Michigan (UM), operated under different input conditions for 15 different fuel blends, were utilized for the study. The combustion events happening under HCCI conditions in the CFR engine are classified into four different modes depending on the combustion phasing and cyclic variability (COVimep). The classes are; no ignition/high COVimep, operable combustion, high MPRR, and early CA50. Two machine learning (ML) models, K-nearest neighbors (KNN) and Support Vector Machines (SVM), are compared for their classification capabilities of combustion events. Seven conditions are used as the input features for the ML models viz.

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels.

Gasoline Direct Injection Compression Ignition (GDCI) is a partially premixed low temperature combustion process that has demonstrated high fuel efficiency with full engine load range capabilities, while emitting very low levels of particulate matter (PM) and oxides of nitrogen (NOx). In the current work, a comparison of engine combustion, performance, and emissions has been made among E10 gasoline and several full-boiling range naphtha fuels on a Gen 2 single-cylinder GDCI engine with compression ratio of 15:1. Initial results with naphtha demonstrated improved combustion and efficiency at low loads. With naphtha fuel, hydrocarbon and carbon monoxide emissions were generally reduced at low loads but tended to be higher at mid-loads despite the increased fuel reactivity. At higher loads, naphtha required less boost pressure compared to gasoline, however, up to 20% additional EGR was required to maintain combustion phasing.

An optically accessible single-cylinder high-speed direct-injection (HSDI) Diesel engine equipped with a Bosch common rail injection system was used to study the spray and combustion processes for European low sulfur diesel, bio-diesel, and their blends at different blending ratio. Influences of injection timing and fuel type on liquid fuel evolution and combustion characteristics were investigated under similar loads. The in-cylinder pressure was measured and the heat release rate was calculated. High-speed Mie-scattering technique was employed to visualize the liquid distribution and evolution. High-speed combustion video was also captured for all the studied cases using the same frame rate. NOx emissions were measured in the exhaust pipe. The experimental results indicated that for all of the conditions the heat release rate was dominated by a premixed combustion pattern and the heat release rate peak became smaller with injection timing retardation for all test fuels.

In recent years gasoline compression ignition (GCI) has been shown to offer an attractive combination of low criteria pollutants and high efficiency. However, enabling GCI across the full engine load map poses several challenges. At high load, the promotion of partial premixing of air and fuel is challenging due to the diminished ignition-delay characteristics at high temperatures, while under low load operations, maintaining combustion robustness is problematic due to the low reactivity of gasoline. Variable valve actuation (VVA) offers a means of addressing these challenges by providing flexibility in effective compression ratio. In this paper, the effects of VVA were studied at high loads in a prototype heavy-duty GCI engine using a gasoline research octane number (RON) 93 at a geometric compression ratio (CR) of 15.7. Both late intake valve closing (LIVC) and early intake valve closing (EIVC) strategies were analyzed as a measure to reduce the effective compression ratio.