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Abstract 2020-2021 Reviewers

Abstract 2020-2021 Reviewers

Abstract 2020-2021 Reviewers

2020-2021 Reviewers

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 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 This work puts presents an all-inclusive state of the art bibliographical review of all categories of electrified transportation (xEVs) standards, issued by the most important standardization organizations. Firstly, the current status for the standards by major organizations is presented followed by the graphical representation of the number of standards issued. The review then takes into consideration the interpretation of the xEVs standards developed by all the major standardization organizations across the globe. The standards are differentiated categorically to deliver a coherent view of the current status followed by the explanation of the core of these standards. The ISO, IEC, SAE, IEEE, UL, ESO, NTCAS, JARI, JIS and ARAI electrified transportation vehicles xEV Standards from USA, Europe, Japan, China and India were evaluated. A total approximated of 283 standards in the area have been issued.

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 Identity-Anonymized CAN (IA-CAN) protocol is a secure CAN protocol, which provides the sender authentication by inserting a secret sequence of anonymous IDs (A-IDs) shared among the communication nodes. To prevent malicious attacks from the IA-CAN protocol, a secure and robust system error recovery mechanism is required. This article presents a central management method of IA-CAN, named the IA-CAN with a global A-ID, where a gateway plays a central role in the session initiation and system error recovery. Each ECU self-diagnoses the system errors, and (if an error happens) it automatically resynchronizes its A-ID generation by acquiring the recovery information from the gateway. We prototype both a hardware version of an IA-CAN controller and a system for the IA-CAN with a global A-ID using the controller to verify our concept.

Abstract Composite ceramic brake discs are made of ceramic material reinforced with carbon fibers and offer exceptional advantages that translate directly into higher vehicle performance. In the case of an electric vehicle, it could increase the range of the vehicle, and in the case of conventional internal combustion engine vehicles, it means lower fuel consumption (and consequently lower CO2 emissions). These discs are typically characterized by complex internal geometries, further complicated by the presence of drilling holes on both friction surfaces. To estimate the aerothermal performance of these discs, and for the thermal management of the vehicle, a reliable model for predicting the air flowing across the disc channels is needed. In this study, a real carbon-ceramic brake disc with drilling holes was investigated in a dedicated test rig simulating the wheel corner flow conditions experimentally using the particle image velocimetry technique and numerically.

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 To design better power converters with enhancement-mode Gallium Nitride high-electron-mobility transistor (eGaN HEMT) for emerging applications such as Electric Vehicles (EV), it is essential to model their switching transients and loss accurately. Analytical modeling has proved to be an effective approach to study the transistor’s dynamic behaviors and analyze the switching energy loss during the turn-on and turn-off transients. Furthermore, it helps to understand the essential factors that influence the switching transients and loss calculation. The accuracy of the analytical model mainly depends on the equivalent circuits and the parasitic parameters inside the transistor packaging and external circuits under different switching stages. It is always challenging to extract the parasitic parameters accurately due to its natural character of nonlinearity and complex correlation during the switching transients.

Abstract In the automotive domain, the overall complexity of technical components has increased enormously. Formerly isolated, purely mechanical cars are now a multitude of cyber-physical systems that are continuously interacting with other IT systems, for example, with the smartphone of their driver or the backend servers of the car manufacturer. This has huge security implications as demonstrated by several recent research papers that document attacks endangering the safety of the car. However, there is, to the best of our knowledge, no holistic overview or structured description of the complex automotive domain. Without such a big picture, distinct security research remains isolated and is lacking interconnections between the different subsystems. Hence, it is difficult to draw conclusions about the overall security of a car or to identify aspects that have not been sufficiently covered by security analyses.

Abstract Present-day vehicles come with a variety of new features like the pre-crash warning, the vehicle-to-vehicle communication, semi-autonomous driving systems, telematics, drive by wire. They demand very high bandwidth from in-vehicle networks. Various ECUs present inside the automotive transmits useful information via automotive multiplexing. Transmission of data in real-time achieves optimum functionality. The high bandwidth and high-speed requirement can be achieved either by using multiple buses or by implementing higher bandwidth. But, by doing so, the cost of the network as well as the complexity of the wiring increases. Another option is to implement higher layer protocol which can reduce the amount of data transferred by using data reduction (DR) techniques, thus reducing the bandwidth usage. The implementation cost is minimal as the changes are required in the software only and not in hardware.

Abstract Connected vehicles and intelligent transportation systems are currently evolving into highly interconnected digital environments. Due to the interconnectivity of different systems and complex communication flows, a joint risk analysis for combining safety and security from a system perspective does not yet exist. We introduce a novel method for joint risk assessment in the automotive sector as a combination of the Diamond Model, Failure Mode and Effects Analysis (FMEA), and Factor Analysis of Information Risk (FAIR). These methods have been sequentially composed, which results in a comprehensive risk management approach to information security in an intelligent transport system (ITS). The Diamond Model serves to identify and structurally describe threats and scenarios, the widely accepted FMEA provides threat analysis by identifying possible error combinations, and FAIR provides a quantitative estimation of probabilities for the frequency and magnitude of risk events.

Abstract The article presents a concept of a new turbine engine in the field of pressure gain combustion (PGC). The engine operates according to the Humphrey thermodynamic cycle, where pressure increase was obtained in a combustion chamber closed with a valve. The intake mushroom valve and exhaust swing valve were designed for self-acting. They operate as a result of gas pressure and centrifugal force. The engine consists of two rotating combustion chambers and a counterrotating radial turbine, in both of which mechanical power was generated. The gas flow in the mobile valve system, combustion process, gas expansion, and torque generation were analyzed by means of Computational Fluid Dynamics (CFD) analysis. The investigated engine concept is characterized by significant energy efficiency.

Abstract This work presents a generalized methodology for the optimal thermal management of different powertrain devices. The methodology is based on the adoption of an electrically driven pump and on the development of a specifically designed controller algorithm. This is achieved following a Model Predictive Control approach and requires a generalized lumped-parameters model of the thermal exchange between the device walls and the coolant. The methodology is validated at a test rig, with reference to a four-cylinder spark-ignition engine. Results show that the proposed approach allows a reduction in fuel consumption of about 2-3% during the engine warm-up, a decrease in fuel consumption of about 1-2% during fully warmed operation, and an estimated fuel consumption reduction of about 2.5-3% in an NEDC. Finally, the investigation highlights that the proposed approach reduces the risk of after-boiling when the engine is rapidly switched off after a prolonged high-load operation.

Abstract Among the myriad of potential hybrid powertrain architectures, selecting the optimal for an application is a daunting task. Whenever available, computer models greatly assist in it. However, some aspects, such as pollutant emissions, are difficult to model, leaving no other option than to test. Validating plausible options before building the powertrain prototype has the potential of accelerating the vehicle development even more, doing so without shipping components around the world. This work concerns the design of a system to virtually couple—that is, avoiding physical contact—geographically distant test rigs in order to evaluate the components of a powertrain. In the past, methods have been attempted, either with or without assistance of mathematical models of the coupled components (observers). Existing methods are accurate only when the dynamics of the systems to couple are slow in relation to the communication delay.

Abstract A pre-chamber ignition system has the potential to reduce the burn duration of lean-burn gasoline engine combustion and can achieve a reduced knock occurrence from the distributed ignition sources. Pre-chamber ignition produces high-velocity turbulent jets, and these jets often reach sonic velocity and produce shock waves inside the combustion chamber. These shock waves make knock detection difficult with a conventional surface-mounted acoustic knock sensor. This article discusses how an acoustic knock sensor works with a pre-chamber ignition and evaluates different cylinder pressure-based knock detection strategies and proposes a method that eliminates the influence of jet-induced oscillations on knock detection.

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.