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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 Collision alert and avoidance systems (CAS) could help to minimize driver errors. They are instrumental as an advanced driver-assistance system (ADAS) when the vehicle is facing potential hazards. Developing effective ADAS/CAS, which provides alerts to the driver, requires a fundamental understanding of human sensory perception and response capabilities. This research explores the premise that external stimulation can effectively improve drivers’ reaction and response capabilities. Therefore this article proposes a light-emitting diode (LED)-based driver warning system to prevent potential collisions while evaluating novel signal processing algorithms to explore the correlation between driver brain signals and external visual stimulation. When the vehicle approaches emerging obstacles or potential hazards, an LED light box flashes to warn the driver through visual stimulation to avoid the collision through braking.

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 discusses a multi-item planning and scheduling problem in a job-shop system with consideration of energy consumption. Planning is considered by a set of periods, each one is characterized by a demand, energy, and length. Scheduling is determined by the sequences of jobs on available resources. A Mixed-Integer Linear Programming (MILP) problem is formulated to integrate planning and scheduling, it is considered as an NP-difficult problem. A Genetic Algorithm (GA) is then developed to solve the MILP, and then a hybridized approach of simulated annealing with genetic algorithm (HGASA) is presented to optimize the results. Finally, numerical results are presented and analyzed to evaluate the effectiveness of the proposed algorithms.

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 An energy management strategy (EMS) has an essential role in ameliorating the efficiency and lifetime of the powertrain components in a hybrid fuel cell vehicle (HFCV). The EMS of intelligent HFCVs is equipped with advanced data-driven techniques to efficiently distribute the power flow among the power sources, which have heterogeneous energetic characteristics. Decentralized EMSs provide higher modularity (plug and play) and reliability compared to the centralized data-driven strategies. Modularity is the specification that promotes the discovery of new components in a powertrain system without the need for reconfiguration. Hence, this article puts forward a decentralized reinforcement learning (Dec-RL) framework for designing an EMS in a heavy-duty HFCV. The studied powertrain is composed of two parallel fuel cell systems (FCSs) and a battery pack.

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 Automotive subframe is a critical chassis component as it connects with the suspension, drive units, and vehicle body. All the vibration from the uneven road profile and drive units are passed through the subframe to the vehicle body. OEMs usually have specific component-level drive point dynamic stiffness (DPDS) requirements for subframe suppliers to achieve their full vehicle NVH goals. Traditionally, the DPDS improvement for subframes welded with multiple stamping pieces is done by thickness and shape optimization. The thickness optimization usually ends up with a huge mass penalty since the stamping panel thickness has to be changed uniformly not locally. Structure shape and section changes normally only work for small improvements due to the layout limitations. Tuned rubber mass damper (TRMD) has been widely used in the automotive industry to improve the vehicle NVH performance thanks to the minimum mass it adds to the original structure.

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 Mechanical friction simulations offer a valuable tool in the development of internal combustion engines for the evaluation of optimization studies in terms of time efficiency. However, system modeling and evaluation of model performance may be highly complex. A high number of interacting submodels and parameters as well as a limited model transparency contribute to uncertainties in the modeling process. In particular, model calibration and validation are complicated by the unknown effect of parameters on the model output. This article presents an advanced and model-independent methodology for identifying sensitive parameters of engine friction. This allows the user to investigate an unlimited number of parameters of a model whose structure and properties are prior unknown.

Abstract Autonomous vehicles (AVs) are expected to have a great impact on mobility by decreasing commute time and vehicle fuel consumption and increasing safety significantly. However, there are still issues that can jeopardize their wide impact and their acceptance by the public. One of the main limitations is motion sickness (MS). Hence, the last year’s research is focusing on improving motion comfort within AVs. On one hand, users are expected to perceive AVs driving style as more aggressive, as it might result in excessive head and body motion. Therefore, speed reduction should be considered as a countermeasure of MS mitigation. On the other hand, the excessive reduction of speed can have a negative impact on traffic. At the same time, the user’s dissatisfaction, i.e., acceptance and subjective comfort, will increase due to a longer journey time.

Abstract A kinematic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towing vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point.

Abstract This article explores the value of simulation for autonomous-vehicle research and development. There is ample research that details the effectiveness of simulation for training humans to fly and drive. Unfortunately, the same is not true for simulations used to train and test artificial intelligence (AI) that enables autonomous vehicles to fly and drive without humans. Research has shown that simulation “fidelity” is the most influential factor affecting training yield, but psychological fidelity is a widely accepted definition that does not apply to AI because it describes how well simulations engage various cognitive functions of human operators. Therefore, this investigation reviewed the literature that was published between January 2010 and May 2022 on the topic of simulation fidelity to understand how researchers are defining and measuring simulation fidelity as applied to training AI.

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 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 Two new misfire detection indexes for single-cylinder motorcycle engines—dubbed gap distance (GD) and gap slope (GS)—are proposed in this study. GD and GS quantify the change in engine angular acceleration using the tooth time measured by the crankshaft position sensor (CKPS). GD is defined as the product of the spacing distance I (the distance from the top dead center at the explosion stroke [TDC2] to the engine speed trend line parallel to the engine speed axis) and spacing distance II (the distance from the bottom dead center at the expansion stroke [BDC2] to the engine speed trend line parallel to the engine speed axis). GS is defined as the difference between the two slopes between the engine speed inclination line and the engine speed trend line. Here the engine speed trend line connects two engine speeds at the top dead center at the intake stroke (TDC1) of the current and subsequent cycles.

Abstract Automotive interior coatings for flexible and rigid substrates represent an important segment within automotive coating space. These coatings are used to protect plastic substrates from mechanical and chemical damage, in addition to providing colour and design aesthetics. These coatings are expected to resist aggressive chemicals, fluids, and stains while maintaining their long-term physical appearance and mechanical integrity. Designing such coatings, therefore, poses significant challenges to the formulators in effectively balancing these properties. Among many factors affecting coating properties, the cross-link density (XLD) and solubility parameter (δ) of coatings are the most predominant factors.