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Abstract The ASTM D130 was first issued in 1922 as a tentative standard for the detection of corrosive sulfur in gasoline. A clean copper strip was immersed in a sample of gasoline for three hours at 50°C with any corrosion or discoloration taken to indicate the presence of corrosive sulfur. Since that time, the method has undergone many revisions and has been applied to many petroleum products. Today, the ASTM D130 standard is the leading method used to determine the corrosiveness of various fuels, lubricants, and other hydrocarbon-based solutions to copper. The end-of-test strips are ranked using the ASTM Copper Strip Corrosion Standard Adjunct, a colored reproduction of copper strips characteristic of various degrees of sulfur-induced tarnish and corrosion, first introduced in 1954. This pragmatic approach to assessing potential corrosion concerns with copper hardware has served various industries well for a century.

Abstract Due to the increasing requirements for efficiency, the wide range of characteristics and the improved possibilities of modern development and production processes, compressors in turbochargers have become more individualized in order to adapt to the requirements of internal combustion engines. An understanding of the working mechanisms as well as an understanding of the way that losses occur in the flow allows a reduced development effort during the optimization process. This article presents three-dimensional (3D) Computational Fluid Dynamics (CFD) investigations of the loss mechanisms and quantitative calculations of individual losses. The 3D-CFD method used in this article will reduce the drawbacks of one-dimensional calculation as far as possible. For example, the twist of the blades is taken into account and the “discrete” method is used for loss calculation instead of the “average” method.

Abstract The United Nation Economic Commission for Europe (UNECE) Regulation 155—Cybersecurity and Cybersecurity Management System (UN R155) mandates the development of cybersecurity management systems (CSMS) as part of a vehicle’s lifecycle. An inherent component of the CSMS is cybersecurity risk management and assessment. Validation and verification testing is a key activity for measuring the effectiveness of risk management, and it is mandated by UN R155 for type approval. Due to the focus of R155 and its suggested implementation guideline, ISO/SAE 21434:2021—Road Vehicle Cybersecurity Engineering, mainly centering on the alignment of cybersecurity risk management to the vehicle development lifecycle, there is a gap in knowledge of proscribed activities for validation and verification testing.

Abstract This article proposes a novel approach of a hybrid system of physics and data-driven modeling for accurately estimating the state of charge (SOC) of a battery. State of Charge (SOC) is a measure of the remaining battery capacity and plays a significant role in various vehicle applications like charger control and driving range predictions. Hence the accuracy of the SOC is a major area of interest in the automotive sector. The method proposed in this work takes the state-of-the-art practice of Kalman filter (KF) and merges it with intelligent capabilities of machine learning using neural networks (NNs). The proposed hybrid system comprises a physics-based battery model and a plurality of NNs eliminating the need for the conventional KF while retaining its features of the predictor-corrector mechanism of the variables to reduce the errors in estimation.

Abstract With the introduction of advanced lightweight materials with complex microstructures and behaviors, more focus is put on the accurate determination of their forming limits, and that can only be possible through experiments as the conventional theoretical models for the forming limit curve (FLC) prediction fail to perform. Despite that, CAE engineers, designers, and toolmakers still rely heavily on theoretical models due to the steep costs associated with formability testing, including mechanical setup, a large number of tests, and the cost of a stereo digital image correlation (DIC) system. The international standard ISO 12004-2:2021 recommends using a stereo DIC system for formability testing since two-dimensional (2D) DIC systems are considered incapable of producing reliable strains due to errors associated with out-of-plane motion and deformation.

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 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 Optimization is essential in real-life mechanical engineering problems that mostly are nonlinear, depend on mixed decision variables, and are usually subject to constraints. However, most of the studied problems are modelled assuming continuous variables. A limited number of studies have been devoted to cases with mixed variables. Moreover, there is a lack of algorithm treating mixed variable problems properly. This article introduces a hybrid algorithm that can handle constrained problems depending on continuous or mixed variables. The proposed algorithm combines two meta-heuristics, Jaya and particle swarm optimization (PSO). PSO is one of the most popular methods to solve nonlinear problems, and Jaya is a novel parameter-free optimization algorithm. This new hybrid optimization algorithm is proposed in order to improve the convergence speed and to investigate what improvements it will bring to optimization problem solutions.

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 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 Safety is always a crucial aspect of developing autonomous systems, and the motivation behind this project comes from the need to address the traffic crashes occurring globally on a daily basis. The present work studies the coexistence of the novel rule-based behavioral planning framework with the five key advanced driver assistance system (ADAS) features as proposed in this article to fulfill the safety requirements and enhance the comfort of the driver/passengers to achieve a receding-horizon autopilot. This architecture utilizes data from the sensor fusion and the prediction module for the prediction time horizon of 2 s iteratively, which is continuously moving forward (hence, the receding horizon), and helps the behavior planner understand the intent of other vehicles on the road in advance.

Abstract The Noise, Vibration, and Harshness (NVH) performance of automotive powertrain (PT) mounts involves the PT source vibration, PT mount stiffness, road input, and overall transfer path design. Like safety, performance, and durability driving dynamics, vehicle-level NVH also plays a major contributing factor for electric vehicle (EV) refinement. This article highlights the recent research on PT mounting-related NVH controls on electric cars. This work’s main contribution lies in the comparative study of the internal combustion engine (ICE)-based PT mounting and EV-based PT mounting system (PMS) with specific EV challenges. Various literature on PT mounts from the passive, semi-active, and active mounting systems are studied. The parameter optimization technique for mount stiffness and location by various research papers is summarized to understand the existing methodologies and research gap in EV application.

Abstract In view of the combustion efficiency and emission performance, various new clean combustion modes put forward higher requirements for the performance of the fuel injection system, and the cavitating two-phase flow characteristics in the injector nozzle have a significant impact on the spray atomization and combustion performance. This article comprehensively discusses and summarizes the factors that affect cavitation and the effectiveness of cavitation, and presents the research status and existent problems under each factor. Among them, viscosity factors are a hot research topic that researchers are passionate about, and physical properties factors still have the value of further in-depth research. However, the importance of material surface factors ranks last since the nozzle material was determined. Establishing a more comprehensive cavitation–atomization model considering various factors is the focus of research on cavitation phenomena.

Abstract Electromagnetic forming (EMF) is a high-speed impulse forming process developed during the 1950s and 1960s to acquire shapes from sheet metal that could not be obtained using conventional forming techniques. In order to attain required deformation, EMF process applies high Lorentz force for a very short duration of time. Due to the ability to form aluminum and other low-formability materials, the use of EMF of sheet metal for automobile parts has been rising in recent years. This review gives an inclusive survey of historical progress in EMF of continuum sheet metals. Also, the EMF is reviewed based on analytical approach, finite element method (FEM) simulation-based approach and experimental approach, on formability of the metals.

Abstract This article presents a strategy for the virtual calibration of a large-scale model representing a self-piercing rivet (SPR) connection. The connection is formed between a stack of three AA6016-T4 aluminum sheets and one SPR. The calibration process involves material characterization, a detailed riveting process simulation, virtual joint unit tests, and the final large-scale model calibration. The virtual tests were simulated by detailed solid element FE models of the joint unit. These detailed models were validated using experimental tests, namely peeling, single-lap joint, and cross-tests. The virtual parameter calibration was compared to the experimental calibration and finally applied to component test simulations. The article contains both experiments and numerical models to characterize the mechanical behavior of the SPR connection under large deformation and failure.

Abstract The acidification of lubricating oils during engine operation, and the subsequent additive neutralization, is an important challenge for Original Equipment Manufacturers and end-users. Often the decline in Total Base Number (TBN) and increase in Total Acid Number (TAN) is measured during engine operation as an indication of the oil’s condition and lifetime. This is clearly an oversimplification given that no consideration is given to the type of acid, how corrosive it is, or the type of base and how effective it is at neutralizing. Acids can be broadly categorized into mineral acids such as sulfuric/nitric and organic acids such as acetic. Traditionally, research has focused on understanding the effects of mineral acids such as sulfuric, which can be formed during the combustion of sulfur-containing fuel.

Abstract Active suspension was a topic of great research interest near the end of last century. Ultimately broad bandwidth active systems were found to be too expensive in terms of both energy and financial cost. This past work, developing the ultimate vehicle suspension, has relevance for today’s vehicle designers working on more efficient and effective suspension systems for practical vehicles. From a control theorist’s perspective, it provides an interesting case study in the use of “practical” knowledge to allow “better” performance than predicted by theoretically optimal linear controllers. A brief history of active suspension will be introduced. Peter Wright, David Williams, and others at Lotus developed their Lotus modal control concept. In a parallel effort, Dean Karnopp presented the notion of inertial (Skyhook) damping. These concepts will be compared, the combination of these two distinctly different efforts will be discussed, and eventual vehicle results presented.

Abstract In this work, an in situ multispecies portable emission measurement system (PEMS) is presented. The system is based on tunable diode laser absorption spectroscopy (TDLAS) and is capable of measuring tailpipe emissions without the necessity of online calibration. It is intended for application on passenger cars within the real drive emission (RDE) procedure of the Worldwide Harmonized Light Duty Test Procedure (WLTP). In contrast to the extractive measurement principles of commercially available PEMS, the introduced measurement system does not require gas sampling or preconditioning and thus does not suffer from the same low-pass filter effects on the measurements. These differences are suspected to have an impact on certification-relevant measurement data. Measurements have been conducted on a 3-cylinder 1 liter EURO 6 b gasoline engine test bench to investigate the differences between the presented measurement system and a commercially available PEMS.