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Abstract In recent years, lean-burn gasoline Spark-Ignition (SI) engines have been a major subject of investigations. With this solution, in fact, it is possible to simultaneously reduce NOx raw emissions and fuel consumption due to decreased heat losses, higher thermodynamic efficiency, and enhanced knock resistance. However, the real applicability of this technique is strongly limited by the increase in cyclic variation and the occurrence of misfire, which are typical for the combustion of homogeneous lean air/fuel mixtures. The employment of a Pre-Chamber (PC), in which the combustion begins before proceeding in the main combustion chamber, has already shown the capability of significantly extending the lean-burn limit. In this work, the potential of an ultralean PC SI engine for a decisive improvement of the thermal efficiency is presented by means of numerical and experimental analyses.

Abstract In the United States (USA), transportation is the largest single source of greenhouse gas (GHG) emissions, representing 27% of total GHGs emitted in 2020. Eighty-three percent of these came from road transport, and 57% from light-duty vehicles (LDVs). Internal combustion engine (ICE) vehicles, which still form the bulk of the United States (US) fleet, struggle to meet climate change targets. Despite increasingly stringent regulatory mechanisms and technology improvements, only three US states have been able to reduce their transport emissions to the target of below 1990 levels. Fifteen states have made some headway to within 10% of their 1990 baseline. Largely, however, it appears that current strategies are not generating effective results. Current climate-change mitigation measures in road transport tend to be predominantly technological.

Abstract This article presents an investigation of energy transfer, flame propagation, and emissions formation mechanisms in a four-cylinder, downsized and boosted, spark ignition engine fuelled by either directly injected compressed natural gas (DI CNG) or gasoline (GDI). Three different charge preparation strategies are examined for both fuels: stoichiometric engine operation without external dilution, stoichiometric operation with external exhaust gas recirculation (EGR), and lean burn. In this work, experiments and engine modelling are first used to analyze the energy transfer throughout the engine system. This analysis shows that an early start of fuel injection (SOI) improves fuel efficiency through lower unburned fuel energy at low loads with stoichiometric DI CNG operation.

Abstract The equivalent consumption minimization strategy (ECMS) is an instantaneous optimization method implemented online for hybrid electric vehicles (HEVs) to improve fuel economy. To fulfill the near-optimal performance of ECMS, equivalent factors (EFs) must be well tuned for different powertrains and driving cycles. This study proposes a hierarchical offline optimization framework which tunes the penalty value of state of charge (SOC) balance in the outer layer and optimizes EFs based on heuristic algorithms in the inner layer. A comprehensive analysis is conducted to evaluate three heuristic algorithms, including the genetic algorithm (GA), the nonlinear-inertia-decreasing particle swarm optimization algorithm (NLPSO), and the novel firefly algorithm (FA). The traversal optimization method (TOM) is chosen as the benchmark. Besides, a sensitivity analysis is carried out to reveal the impact of the penalty value on the battery SOC balance.

Abstract Electric power steering (EPS) systems have become the most advantageous steering system used in vehicles. They provide better fuel efficiency and a more compact design over traditional hydraulic power steering (HPS) systems. However, EPS systems are afflicted with unwanted noise and vibration that can undermine the safety of drivers. This article presents a mathematical framework for vibration analysis in a column-type EPS system. The steering column is modeled as a continuous clamped column. The equations of motion are derived using Hamilton’s principle, and explicit expressions are presented for the frequency and transmissibility equations. A three-degrees-of-freedom (3-DOF) dynamic model is also presented by an approximation of the stiffness, damping, and mass of the steering column. The results of the proposed analytical models are validated using ANSYS simulation.

Abstract Deep learning (DL)-based approaches enable unprecedented control paradigms for propulsion systems, utilizing recent advances in high-performance computing infrastructure connected to modern vehicles. These approaches can be employed to optimize diesel aftertreatment control systems targeting the reduction of emissions. The optimization of the Trapped Soot Load (TSL) reduction in the Diesel Particulate Filter (DPF) is such an example. As part of the diesel aftertreatment system, the DPF stores the soot particles resulting from the combustion process in the engine. Periodically, the stored soot is oxidized during a DPF regeneration event. The efficiency of such a regeneration influences the fuel economy, and potentially the service interval of the vehicle. The quality of a regeneration depends on the operating conditions of the DPF, the engine, and the ability to complete the regeneration event.

Abstract Demonstrating ring pack operation in an operating engine is very difficult, yet it is essential to optimize engine performance parameters such as blow-by, oil consumption, emissions, and wear. A significant amount of power is lost in friction between piston ring–cylinder liner interfaces if ring pack parameters are not optimized properly. Thus, along with these parameters, it is also necessary to reduce friction power loss in modern internal combustion engines as the oil film thickness formed between the piston ring and liner is vital for power loss reduction due to friction. Hence, it has also been a topic of research interest for decades. Piston and ring dynamics simulation software are used extensively for a better ring pack design. In this research work, a similar software for piston ring dynamics simulation reviews the ring pack performance of a four-cylinder diesel engine.

Abstract Linear internal combustion engine-linear generator integrated system (LICELGIS) is an innovative energy conversion device with the ability of converting mechanical energy into electrical energy, which allows it to be a range extender for hybrid vehicles. This article presents a fundamental analysis for the steady-state operation of the LICELGIS, concentrating on electromagnetic force and motion characteristics. Simple assumptions are made to represent ideal gases instantaneous heat release and rejection. Based on assumptions, sensitivity analysis is carried out for key factors of electromagnetic force. The theoretical velocity model in mathematics is derived from analyzing the LICELGIS theory model. It shows that fuel injection quantity and stroke length are the most sensitive factors in key parameters. The piston velocity around the top dead center (TDC) changes greater than that at any other position, which is caused by the combustion process.

Abstract Turbocharging can provide a low cost means for increasing the power output and fuel economy of an internal combustion engine. Currently, turbocharging is common in multi-cylinder engines, but due to the inconsistent nature of intake air flow, it is not commonly used in single-cylinder engines. In this article, we propose a novel method for turbocharging single-cylinder, four-stroke engines. Our method adds an air capacitor-an additional volume in series with the intake manifold, between the turbocharger compressor and the engine intake-to buffer the output from the turbocharger compressor and deliver pressurized air during the intake stroke. We analyzed the theoretical feasibility of air capacitor-based turbocharging for a single-cylinder engine, focusing on fill time, optimal volume, density gain, and thermal effects due to adiabatic compression of the intake air.

Abstract The hydrocarbons constituting the heavy tail of gasoline are key contributors to particulate matter (PM) emissions from spark-ignition (SI) engines. They are predominantly aromatic and, to a significant degree, bicyclic aromatic. For example, above a boiling point of 400°F, the content of bicyclic compounds in the United States (US) summer E10 regular-grade gasoline exceeds 50%v. Various gasoline parameters, such as the PM Index, Particulate Evaluation Index (PEI), Particulate and Soot Correlation Equation (PASCE), or Threshold Sooting Index (TSI), have been proposed as predictors of PM emissions from SI engines. In particular, the PM Index, whose value is dominated by the content of heavy aromatics and which, so far, has yielded the most predictive PM emissions models, appears to be the best metric to achieve this objective.

Abstract Nowadays, the need for a more sustainable mobility is fostering powertrain electrification as a way of reducing the carbon footprint of conventional vehicles. On the other side, the presence of multiple energy sources significantly increases the powertrain complexity and requires the development of a suitable Energy Management System (EMS) whose performance can strongly affect the fuel economy potential of the vehicle. In such a framework, this article proposes a novel methodology to reverse engineer the control strategy of a test case P2 Plug-in Hybrid Electric Vehicle (PHEV) through the analysis of experimental data acquired in a wide range of driving conditions. In particular, a combination of data obtained from On-Board Diagnostic system (OBD), Controller Area Network (CAN)-bus protocol, and additional sensors installed on the High Voltage (HV) electric net of the vehicle is used to point out any dependency of the EMS decisions on the powertrain main operating variables.

Abstract The 2010 Environmental Protection Agency (EPA) Emission Standard for heavy-duty engines required 0.2 g/bhp-hr over certification cycles (cold and hot Federal Test Procedure [FTP]), and the California Air Resources Board (CARB) standards require 0.02 g/bhp-hr for the same cycles leading to a 90% reduction of overall oxides of nitrogen (NOx) emissions. Similar reductions may be considered by the EPA through its Cleaner Trucks Initiative program. In this article, aftertreatment system components consisting of a diesel oxidation catalyst (DOC); a selective catalytic reduction (SCR) catalyst on a diesel particulate filter (DPF), or SCR-F; a second DOC (DOC2); and a SCR along with two urea injectors have been analyzed, which could be part of an aftertreatment system that can achieve the 0.02 g/bhp-hr standard.

Abstract Three-dimensional patterns representing crosshatched plateau-honed cylinder bores based on two-dimensional Fast Fourier Transform (FFT) of measured surfaces were generated and used to calculate pressure flow, shear-driven flow, and shear stress factors. Later, the flow and shear stress factors obtained by numerical simulations for various surface patterns were used to calculate lubricant film thickness and friction force between piston ring and cylinder bore contact in typical diesel engine conditions using a mixed lubrication model. The effects of various crosshatch honing angles, such as 30°, 45°, and 60°, and texture heights on engine friction losses, wear, and oil consumption were discussed in detail. It is observed from numerical results that lower lubricant film thickness values are generated with higher honing angles, particularly in mixed lubrication regime where lubricant film thickness is close to the roughness level, mainly due to lower resistance to pressure flow.

Abstract Heavy-duty (HD) diesel engines are the primary propulsion systems in use within the transportation sector and are subjected to stringent oxides of nitrogen (NOx) and particulate matter (PM) emission regulations. The objective of this study is to develop a robust calibration technique to optimize HD diesel engine for performance and emissions to meet current and future emissions standards during certification and real-world operations. In recent years, California - Air Resources Board (C-ARB) has initiated many studies to assess the technology road maps to achieve Ultra-Low NOx emissions for HD diesel applications [1]. Subsequently, there is also a major push for the complex real-world driving emissions as the confirmatory and certification testing procedure in Europe and Asia through the UN-ECE and ISO standards.

Abstract Considering the biodiesel composition, blend percentage, and temperature as input variables in the models to predict biodiesel-diesel blends’ properties is imperative. However, there are no models available in the literature to predict the properties of biodiesel-diesel blends that consider all these variables. The accuracy of spray and combustion models for diesel engines depends on the accuracy at which the fuel properties are estimated. Thus, straightforward approaches to accurately predict the properties of biodiesel-diesel blends are required. A novel reference property-based approach is proposed in the present work to predict the biodiesel-diesel blends’ properties to address this research gap. Models available in the literature correlating the properties of interest to fuel temperature were modified by including a reference property measured at 293 K.

Abstract Due to the incoming phase out of fossil fuels from the market in order to reduce the carbon footprint of the automotive sector, hydrogen-fueled engines are candidate mid-term solution. Thanks to its properties, hydrogen promotes flames that poorly suffer from the quenching effects toward the engine walls. Thus, emphasis must be posed on the heat-up of the oil layer that wets the cylinder liner in hydrogen-fueled engines. It is known that motor oils are complex mixtures of a number of mainly heavy hydrocarbons (HCs); however, their composition is not known a priori. Simulation tools that can support the early development steps of those engines must be provided with oil composition and properties at operation-like conditions. The authors propose a statistical inference-based optimization approach for identifying oil surrogate multicomponent mixtures. The algorithm is implemented in Python and relies on the Bayesian optimization technique.

Abstract Mankind’s quest for clean alternative energy sources pushes the boundaries of science and technology every passing day. Increasing environmental and human health concerns caused by conventional fuels underscores the importance of identifying an energy source which is sustainable in terms of production, emissions, and wide application. Hythane (hydrogen-methane gas) is a contender for applications ranging from transportation and combined heating-power generation to cooking. In land transportation, the use of gaseous hythane for internal combustion engines shows better performance, enhanced combustion, and lower emission than conventional liquid hydrocarbon fuels. The use of liquefied Natural Gas (NG) and hydrogen in aircraft and ships is a steppingstone to more the wide-scale use of hythane in air, sea, and rail transportation sectors which could lower emissions and operating costs.

Abstract Many consider hydrogen to be the automobile fuel of the future. Indeed, it has numerous characteristics that makes it very attractive. Hydrogen has a much higher energy density than gasoline, can be produced from water, and its only emission is water. However, there are numerous challenges associated with hydrogen. In particular, the production of hydrogen is a key issue. Currently, most hydrogen is developed from methane, resulting in hydrogen having a carbon footprint. New investments into electrolysis from renewable energy sources is showing promise as an alternative for generating hydrogen. Further, the distribution of hydrogen poses many problems, requiring substantial infrastructure to support a hydrogen economy. Additionally, hydrogen storage is a key issue since most conventional storage mechanisms are overly bulky. If these three issues can be addressed, hydrogen is posed for being a key fuel as the world tries to move away from fossil fuels.

Abstract This article is a review of automobile sensor technologies that have the potential to enhance fuel economy. Based on an in-depth review of the literature and demonstration projects, the following sensor technologies were selected for evaluation: vehicular radar systems (VRS), camera systems (CS), and vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems. V2V and V2I systems were found to have the highest merit in improving fuel economy over a wide range of integration strategies, with fuel economy improvements ranging from 5 to 20% with V2V and 10 to 25% for V2I. However, V2V and V2I systems require significant adoption for practical application which is not expected in this decade. Numerous academic studies and contemporary vehicular safety systems attest VRS as more technologically mature and robust relative to other sensors. However, VRS offers less fuel economy enhancement (~14%).

Abstract Natural gas (NG) can be compressed to a high pressure of around 200 bar for use in engines and other applications. Compressed natural gas (CNG) contains 87–92% methane (CH4) and has a low carbon-to-hydrogen ratio compared to other hydrocarbon (HC) fuels. Due to this, it can potentially reduce carbon dioxide (CO2) emissions by more than 20% compared to conventional fuels like diesel or gasoline. This makes CNG one of the most environmentally friendly fuels for internal combustion engines (ICEs). To improve the thermal efficiency of ICEs, higher compression ratios (CRs) and leaner combustion are essential. Since CNG is a gaseous fuel, it has several advantages over liquid fuels due to its favorable physical and chemical properties. A few of these advantages are minimal fuel evaporation issues, a low-carbon content in the fuel composition and a high-octane number. The CNG high-octane number allows for a high CR, resulting in higher thermal efficiency and lower emissions.