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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 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 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 Vehicle-to-infrastructure (V2I) connectivity technology presents the opportunity for vehicles to perform autonomous longitudinal control to navigate safely and efficiently through sequences of V2I-enabled intersections, known as connected corridors. Existing research has proposed several control systems to navigate these corridors while minimizing energy consumption and travel time. This article analyzes and compares the simulated performance of three different autonomous navigation systems in connected corridors: a V2I-informed constant acceleration kinematic controller (V2I-K), a V2I-informed model predictive controller (V2I-MPC), and a V2I-informed reinforcement learning (V2I-RL) agent. A rules-based controller that does not use V2I information is implemented to simulate a human driver and is used as a baseline. The performance metrics analyzed are net energy consumption, travel time, and root-mean-square (RMS) acceleration.

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 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 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 In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist.

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 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 In order to maximize the reluctance torque component, multiple-layer flux barriers are usually employed in permanent magnet-assisted synchronous reluctance (PMAREL) motors. However, the permanent magnet (PM) dimension of each layer should be carefully designed to achieve the best performance with the minimum PM material. This article investigates this issue and proposes a method to define the PM width according to the sinusoidal no-load airgap flux density distribution. First, the accuracy of the no-load magnetic circuit for airgap flux density calculation is verified with finite element analysis (FEA), considering single or multiple flux-barriers per pole. The effects of the location, width, and thickness of the PM are investigated separately. Then the PM width is derived by the equations developed from the no-load magnetic circuit. The proposed method reduces both the PM mass and the torque ripple.

Abstract Modern Automated Driving (AD) systems rely on safety measures to handle faults and to bring the vehicle to a safe state. To eradicate lethal road accidents, car manufacturers are constantly introducing new perception as well as control systems. Contemporary automotive design and safety engineering best practices are suitable for analyzing system components in isolation, whereas today’s highly complex and interdependent AD systems require a novel approach to ensure resilience to multiple-point failures. We present a holistic and cost-effective safety concept unifying advanced safety measures for handling multiple-point faults. Our proposed approach enables designers to focus on more pressing issues such as handling fault-free hazardous behavior associated with system performance limitations. To verify our approach, we developed an executable model of the safety concept in the formal specification language mCRL2.

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 Performing an uncertainty analysis for complex measurement tasks, such as those found in engine research, presents unique challenges. Also, because of the excessive computational costs, modeling-based approaches, such as a Monte Carlo approach, may not be practical. This work provides a traditional statistical approach to uncertainty analysis that incorporates the uncertainty tree, which is a graphical tool for complex uncertainty analysis. Approaches to calculate the required sensitivities are discussed, including issues associated with numerical differentiation, numerical integration, and post-processing. Trimming of the uncertainty tree to remove insignificant contributions is discussed. The article concludes with a best practices guide in the Appendix to uncertainty propagation in experimental engine combustion post-processing, which includes suggested post-processing techniques and down-selected functional relationships for uncertainty propagation.

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 In recent years, countries worldwide have framed policies for faster adoption of electric vehicles. To meet the requirements of electric vehicles, research activities in academia as well as in industry have intensified. One of the significant areas of research is low-cost and high-efficiency electric drive for these vehicles, and their control over a wide range of operations. In this article, an electric vehicle drive with direct torque control of induction motor is presented. This article addresses the impact of reference flux linkage on the operation of induction motor for direct torque control over a wide speed range. A nonlinear equivalent circuit model of an induction motor is considered to obtain values of reference flux linkage. The method uses the nonlinear equivalent circuit parameters to do the offline calculation to determine the reference flux linkage, and a lookup table is generated.

Abstract An efficient vehicle thermal management is essential to fulfil the requirements of fuel consumption and passenger comfort. Therefore, the design and dimensioning of the cooling system is under high scrutiny in new vehicle architectures. With increasing electrification, no longer just the load peaks define the design frame but also the dynamics of thermal loading and recovery. Consequently, electrified vehicle architectures such as plug-in hybrid fuel cell vehicles demand for alternative approaches regarding the design of cooling systems and the definition of the decisive criteria. This article presents a new methodology for designing the cooling system related to its demands in customer operation. The recorded fleet data is first filtered for high load driving, using the so-called thermal load integral (LI) as a filter criterion.

Abstract The tailpipe zero-emission legislation has pushed the automotive industry toward more electrification. Regenerative braking is the capability of electric machines to provide brake torque. So far, the regenerative braking feature is primarily considered due to its effect on energy efficiency. However, using individual e-machines for each wheel makes it possible to apply the antilock braking function due to the fast torque-tracking characteristics of permanent magnet synchronous motors (PMSM). Due to its considerable cost reduction, in this article, a feasibility study is carried out to investigate if the ABS function can be done purely through regenerative braking using a mid-fidelity model-based approach. An uni-tire model of the vehicle with a surface-mount PMSM (SPMSM) model is used to verify the idea. The proposed ABS control system has a hierarchical structure containing a high-level longitudinal slip controller and a low-level SPMSM torque controller.

Abstract A modular, stationary IC engine blow rig for differential and integral flow field measurements using particle image velocimetry (PIV) has been developed. Unlike conventional PIV blow rigs, the given design is capable of operating under near-reality charge air conditions, that is, highly pressurized, hot intake air supply at high flow rates. Its conceptual flexibility as well as peripheral infrastructure allow for comprehensive and wide-ranging flow field analysis. Because of a modular architecture, it is neither confined to a specific cylinder head design nor limited solely to the application of PIV for differential flow field analysis. It also already accounts for direct inlet flow determination through an additional PIV access point upstream of the cylinder head. The inlet and outlet ducts have been designed with regular shapes and smooth walls, such that a digital twin-type CFD model of the blow rig is conveniently feasible.