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Abstract Connected autonomous vehicles that employ internet connectivity are technologically complex, which makes them vulnerable to cyberattacks. Many cybersecurity researchers, white hat hackers, and black hat hackers have discovered numerous exploitable vulnerabilities in connected vehicles. Several studies indicate consumers do not fully trust automated driving systems. This study expanded the technology acceptance model (TAM) to include cybersecurity and level of trust as determinants of technology acceptance. This study surveyed a diverse sample of 209 licensed US drivers over 18 years old. Results indicated that perceived ease of use positively influences perceived usefulness, perceived ease of usefulness negatively influences perceived cyber threats, and perceived cyber threats negatively influence the level of trust.

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 This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract Brake squeal reduces comfort for the vehicle occupants, damages the reputation of the respective manufacturer, and can lead to financial losses due to cost-intensive repair measures. Mode coupling is mainly held responsible for brake squeal today. Two adjacent eigenfrequencies converge and coalesce due to a changing bifurcation parameter. Several approaches have been developed to suppress brake squeal through structural changes. The main objective is to increase the distance of coupling eigenfrequencies. This work proposes a novel approach to structural modifications and sizing optimization aiming for a start at shifting a single component eigenfrequency. Locations suitable for structural changes are derived such that surrounding modes do not significantly change under the modifications. The positions of modifications are determined through a novel sensitivity calculation of the eigenmode to be shifted in frequency.

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.

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 Artificial neural networks (ANNs) have found increasing usage in regression problems because of their ability to map complex nonlinear relationships. In recent years, ANN regression model applications have rapidly increased in the engine calibration and controls area. The data used to build ANN models in engine calibration and controls area generally consists of noise due to instrument error, sensor precision, human error, stochastic process, etc. Filtering the data helps in reducing noise due to instrument error, but noise due to other sources still exist in data. Furthermore, many researchers have found that ANNs are susceptible to learning from noise. Also ANNs cannot quantify the uncertainty of their output in critical applications. Hence, a methodology is developed in the present manuscript which computes the noise-based confidence interval using engine test data. Moreover, a method to assess the risk of ANN learning from noise is also developed.

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 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 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 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 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 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 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.