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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 Additive manufacturing (AM) technologies can produce lighter parts; reduce manual assembly processes; reduce the number of production steps; shorten the production cycle; significantly reduce material consumption; enable the production of prostheses, implants, and artificial organs; and produce end-user products since it is used in many sectors for many reasons; it has also started to be used widely, especially in the field of aerospace. In this study, polylactic acid (PLA) was preferred for the antenna substrate because it is environmentally friendly, easy to recycle, provides convenience in production design with a three-dimensional (3D) printer, and is less expensive compared to other available materials. Copper (Cu) tape and graphene filament were employed for the antenna patch component due to their benefits.

Abstract Regulations limiting GreenHouse Gases (GHG) from Heavy-Duty (HD) commercial vehicles in the United States (US) and European Union will phase in between the 2024 and 2030 model years. These mandates require efficiency improvements at both the engine and vehicle levels, with the most stringent reductions required in the heaviest vehicles used for long-haul applications. At the same time, a 90% reduction in oxides of nitrogen (NOx) will be required as part of new regulations from the California Air Resources Board. Any technologies applied to improve engine efficiency must therefore not come at the expense of increased NOx emissions. Research into advanced engine architectures and components has identified improved turbomachine efficiency as one of the largest potential contributors to engine efficiency improvement. However this comes at the cost of a reduced capability to drive high-pressure Exhaust Gas Recirculation (EGR).

Abstract The predictive control of commercial vehicle energy management systems, such as vehicle thermal management or waste heat recovery (WHR) systems, are discussed on the basis of information sources from the field of environment recognition and in combination with the determination of the vehicle system condition. In this article, a mathematical method for predicting the exhaust gas mass flow and the exhaust gas temperature is presented based on driving data of a heavy-duty vehicle. The prediction refers to the conditions of the exhaust gas at the inlet of the exhaust gas recirculation (EGR) cooler and at the outlet of the exhaust gas aftertreatment system (EAT). The heavy-duty vehicle was operated on the motorway to investigate the characteristic operational profile.

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 One of the most important factors affecting the performance of vehicle active chassis control systems is the tire-road friction coefficient. Accurate estimation of the friction coefficient can lead to better performance of these controllers. In this study, a new three-step friction estimation algorithm, based on intelligent tire concept, is proposed, which is a combination of experiment-based and vehicle dynamic based approaches. In the first step of the proposed algorithm, the normal load is estimated using a trained Artificial Neural Network (ANN). The network was trained using the experimental data collected using a portable tire testing trailer. In the second step of the algorithm, the tire forces and the wheel longitudinal velocity are estimated through a two-step Kalman filter. Then, in the last step, using the estimated tire normal load and longitudinal and lateral forces, the friction coefficient can be estimated.

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 article compares the accuracy of different exhaust gas recirculation (EGR) correction factor models under engine conditions. The effect of EGR on the laminar burning velocity of a EURO VI E10 specification gasoline (10% Ethanol content by volume) has been back calculated from engine pressure trace data, using the Leeds University Spark Ignition Engine Data Analysis (LUSIEDA) reverse thermodynamic code. The engine pressure data ranges from 5% to 25% EGR (by mass) with the running conditions, such as spark advance and pressure at intake valve closure, changed to maintain a constant engine load of 0.79 MPa gross mean effective pressure (GMEP). Based on the experimental data, a correlation is suggested on how the laminar burning velocity reduces with increasing EGR mass fraction.

Abstract The technology focus in the automotive sector has moved toward battery electric vehicles (BEVs) over the last few years. This shift has been ascribed to the importance of reducing greenhouse gas (GHG) emissions from transportation to mitigate the effects of climate change. In Europe, countries are proposing future bans on vehicles with internal combustion engines (ICEs), and individual United States (U.S.) states have followed suit. An important component of these complex decisions is the electricity generation GHG emission rates both for current electric grids and future electric grids. In this work we use 2019 U.S. electricity grid data to calculate the geographically and temporally resolved marginal emission rates that capture the real-world carbon emissions associated with present-day utilization of the U.S. grid for electric vehicle (EV) charging or any other electricity need.

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 A Direct Yaw-Moment Control (DYC) logic for a rear-wheel-drive electric-powered vehicle is proposed. The vehicle is a Formula SAE (FSAE) type race car, with two electric motors powering each rear wheel. Vehicle baseline balance is neutral at low speeds, for increased maneuverability, and increases understeering at high speeds (due to the aerodynamic configuration) for stability. A controller that can deal with these yaw response variations, modelling uncertainties, and vehicle nonlinear behavior at limit handling is proposed. A two-level control strategy is considered. For the upper level, yaw rate and sideslip angle are considered as feedback control variables and a cubic-error Proportional Derivative (PD) controller is proposed for the feedback control. For the lower level, a traction control algorithm is used, together with the yaw moment requirement, for torque allocation.

Abstract The cold crank-start stage, including the first three engine cycles, is responsible for a significant amount of the cold-start phase emissions in a Gasoline Direct Injection (GDI) engine. The engine crank-start is highly transient due to substantial engine speed changes, Manifold Absolute Pressure (MAP) dynamics, and in-cylinder temperatures. Combustion characteristics change depending on control inputs variations, including throttle angle and spark timing. Fuel injection strategy, timing, and vaporization dynamics are other parameters causing cold-start first cycles analysis to be more complex. Hybrid Electric Vehicles (HEVs) provide elevated cranking speed, enabling technologies such as cam phasing to adjust the valve timing and throttling, and increased fuel injection pressure from the first firings.

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 Aiming to improve the handling performance of heavy tractor semi-trailer during turning or changing lanes at high speed, a hierarchical structure controller is proposed and a hardware-in-the-loop (HIL) test bench of the electronic pneumatic braking system is developed to validate the proposed controller. In the upper controller, a Kalman filter observer based on the heavy tractor semi-trailer dynamic model is used to estimate the yaw rates and sideslip angles of the tractor and trailer. Simultaneously, a sliding mode direct yaw moment controller is developed, which takes the estimated yaw rates and sideslip angles and the reference values calculated by the three-degrees-of-freedom dynamic model of the heavy tractor semi-trailer as the control inputs. In the lower controller, the additional yaw moments of tractor and trailer are transformed into corresponding wheel braking forces according to the current steering characteristics.

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 Motion planning for autonomous vehicles remains challenging, especially in environments with multiple vehicles and high speeds. Autonomous racing offers an opportunity to develop algorithms that can deal with such situations and adds the requirement of following race rules. We propose a hybrid local planning approach capable of generating rule-compliant trajectories at the dynamic limits for multi-vehicle oval racing. The planning method is based on a spatiotemporal graph, which is searched in a two-step process to exploit the dynamic limits on the one hand and achieve a long planning horizon on the other. We introduce a soft-checking procedure that can handle cases where no collision-free, feasible, or rule-compliant solutions are found to restore an admissible state as quickly as possible. We also present a state machine explicitly designed for fully autonomous operation on a racetrack, acting on a higher level of the planning algorithm.

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 One of the key factors for accurate mass burn fraction and energy conversion point calculations is the accuracy of the compression ratio. The method presented in this article suggests a workflow that can be applied to determine or correct the compression ratio estimated geometrically or measured using liquid displacement. It is derived using the observation that, in a motored engine, the heat losses are symmetrical about a certain crank angle, which allows for the derivation of an expression for the clearance volume [1]. In this article, a workflow is implemented in real time, in a current production engine indicating system. The goal is to improve measurement data quality and stability for the energy conversion points calculated during measurement procedures. Experimental and simulation data is presented to highlight the benefits and improvement that can be achieved, especially at the start of combustion.