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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 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 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 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 Quantifying exhaust gas composition and temperature in vehicles with internal combustion engines (ICEs) is crucial to understanding and reducing emissions during transient engine operation. This is particularly important before the catalytic converter system lights off (i.e., during cold start). Most commercially available gas analyzers and temperature sensors are far too slow to measure these quantities on the timescale of individual cylinder-firing events, thus faster sensors are needed. A two-color mid-infrared (MIR) laser absorption spectroscopy (LAS) sensor for gas temperature and carbon monoxide (CO) mole fraction was developed and applied to address this technology gap. Two quantum cascade lasers (QCLs) were fiber coupled into one single-mode fiber to facilitate optical access in the test vehicle exhaust. The QCLs were time-multiplexed in order to scan across two CO absorption transitions near 2013 and 2060 cm–1 at 15 kHz.

Abstract Effective circumstance perception technology is the prerequisite for the successful application of autonomous driving, especially the detection technology of traffic objects that affects other tasks such as driving decisions and motion execution in autonomous vehicles. However, recent studies show that a single sensor cannot perceive the surrounding environment stably and effectively in complex circumstances. In the article, we propose a multi-scale feature fusion framework that exploits a dual backbone network to extract camera and radar feature maps and performs feature fusion on three different feature scales using a new fusion module. In addition, we introduce a new generation mechanism of radar projection images and relabel the nuScenes dataset since there is no other suitable autonomous driving dataset for model training and testing.

Abstract The Formula SAE (FSAE) is an international engineering competition where a Formula style race car is designed and built by students from worldwide universities. According to FSAE regulation, an air restrictor with circular cross section of 20 mm for gasoline-fuelled and 19 mm for E-85-fuelled vehicles is to be incorporated between the throttle valve and engine inlet. The sole purpose of this regulation is to limit the airflow to the engine used. The only sequence allowed is throttle valve, restrictor and engine inlet. A new approach of combining ram theory and acoustic theory methods are investigated to increase the performance of the engine by designing an optimized intake runner for a particular engine speed range and an optimized plenum volume in this range. Engine performance characteristics such as brake power, brake torque and volumetric efficiency are taken into considerations.

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 The demand for contactless, rapid manufacturing has increased over the years, especially during the COVID-19 pandemic. Additive manufacturing (AM), a type of rapid manufacturing, is a computer-based system that precisely manufactures products. It proves to be a faster, cheaper, and more efficient production system when integrated with cloud-based manufacturing (CBM). Similarly, the need for semiconductors has grown exponentially over the last five years. Several companies could not keep up with the increasing demand for many reasons. One of the main reasons is the lack of a workforce due to the COVID-19 protocols. This article proposes a novel technique to manufacture semiconductor chips in a fast-paced manner. An algorithm is integrated with cloud, machine vision, sensors, and email access to monitor with live feedback and correct the manufacturing in case of an anomaly.

Abstract Electrochemical impedance spectroscopy (EIS) is widely used to diagnose the state of health (SOH) of lithium-ion batteries. One of the essential steps for the diagnosis is to analyze EIS with an equivalent circuit model (ECM) to understand the changes of the internal physical and chemical processes. Due to numerous equivalent circuit elements in the ECM, existing parameter identification methods often fail to meet the requirements in terms of identification accuracy or convergence speed. Therefore, this article proposes a novel impedance model parameter identification method based on the random mutation differential evolution (RMDE) algorithm. Compared with methods such as nonlinear least squares, it does not depend on the initial values of the parameters. The method is compared with chaos particle swarm optimization (CPSO) algorithm and genetic algorithm (GA), showing advantages in many aspects.

Abstract In this article, a novel hybrid metaheuristic based on passing vehicle search (PVS) cultural algorithm (CA) is proposed. This contribution has a twofold aim: First is to present the new hybrid PVS-CA. Second is to prove the effectiveness of the proposed algorithm for adaptive signal timing optimization. For this, a system that can adapt efficiently to the real-time traffic situation based on priority signal control is developed. Hence, Transit Signal Priority (TSP) techniques have been used to adjust signal phasing in order to serve emergency vehicles (EVs) and manage the tram priority in a coordinated tram intersection. The system used in this study provides cyclic signal operation based on a real-time control approach, including an optimization process and a database to manage the sensor data from detectors for real-time predictions of EV and tram arrival time.

Abstract Safety is always a crucial aspect of developing autonomous systems, and the motivation behind this project comes from the need to address the traffic crashes occurring globally on a daily basis. The present work studies the coexistence of the novel rule-based behavioral planning framework with the five key advanced driver assistance system (ADAS) features as proposed in this article to fulfill the safety requirements and enhance the comfort of the driver/passengers to achieve a receding-horizon autopilot. This architecture utilizes data from the sensor fusion and the prediction module for the prediction time horizon of 2 s iteratively, which is continuously moving forward (hence, the receding horizon), and helps the behavior planner understand the intent of other vehicles on the road in advance.

Abstract This article is a review of automobile sensor technologies that have the potential to enhance fuel economy. Based on an in-depth review of the literature and demonstration projects, the following sensor technologies were selected for evaluation: vehicular radar systems (VRS), camera systems (CS), and vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems. V2V and V2I systems were found to have the highest merit in improving fuel economy over a wide range of integration strategies, with fuel economy improvements ranging from 5 to 20% with V2V and 10 to 25% for V2I. However, V2V and V2I systems require significant adoption for practical application which is not expected in this decade. Numerous academic studies and contemporary vehicular safety systems attest VRS as more technologically mature and robust relative to other sensors. However, VRS offers less fuel economy enhancement (~14%).

Abstract In the past years, the automotive industry has been integrating multiple hardware in the vehicle to enable new features and applications. In particular automotive applications, it is important to monitor the actions and behaviors of drivers and passengers to promote their safety and track abnormal situations such as social disorders or crimes. These applications rely on multiple sensors that generate real-time data to be processed, and thus, they require adequate data acquisition and analysis systems. This article proposes a prototype to enable in-vehicle data acquisition and analysis based on the middleware framework Robot Operating System (ROS). The proposed prototype features two processing devices and enables synchronized audio and video acquisition, storage, and processing. It was assessed through the implementation of a live inference system consisting of a face detection algorithm from the data gathered from the cameras and the microphone.

Abstract The acidification of lubricating oils during engine operation, and the subsequent additive neutralization, is an important challenge for Original Equipment Manufacturers and end-users. Often the decline in Total Base Number (TBN) and increase in Total Acid Number (TAN) is measured during engine operation as an indication of the oil’s condition and lifetime. This is clearly an oversimplification given that no consideration is given to the type of acid, how corrosive it is, or the type of base and how effective it is at neutralizing. Acids can be broadly categorized into mineral acids such as sulfuric/nitric and organic acids such as acetic. Traditionally, research has focused on understanding the effects of mineral acids such as sulfuric, which can be formed during the combustion of sulfur-containing fuel.

Abstract Neutralization of acidic contaminants in engine lubricating oil is an important topic for engine manufacturers. Often, the deterioration in total base number (TBN) and increase in total acid number (TAN) during engine test operation is used as an indication of oil lifetime. This is clearly an oversimplification given that no consideration is given to how corrosive the acid is, and how effective the base is at neutralizing different acids. The work detailed here will explore how the presence of inorganic acids can be combated by lubricant additives, such as overbased detergents, through rapid neutralization. To achieve this, stopped-flow UV/visible spectroscopy has been used to measure the reaction kinetics between an overbased detergent and sulfuric acid containing water-in-oil (w/o) microemulsion droplets. The key structural properties of overbased detergents that contribute to effective acid neutralization will be explored.

Abstract In this work, an in situ multispecies portable emission measurement system (PEMS) is presented. The system is based on tunable diode laser absorption spectroscopy (TDLAS) and is capable of measuring tailpipe emissions without the necessity of online calibration. It is intended for application on passenger cars within the real drive emission (RDE) procedure of the Worldwide Harmonized Light Duty Test Procedure (WLTP). In contrast to the extractive measurement principles of commercially available PEMS, the introduced measurement system does not require gas sampling or preconditioning and thus does not suffer from the same low-pass filter effects on the measurements. These differences are suspected to have an impact on certification-relevant measurement data. Measurements have been conducted on a 3-cylinder 1 liter EURO 6 b gasoline engine test bench to investigate the differences between the presented measurement system and a commercially available PEMS.

Abstract The transient surface pressure over a full-scale, operational compact automotive vehicle—a Volkswagen Golf 7—exposed to transient crosswinds with relative yaw angles of β = 22-45° has been characterized. Experiments were performed at the BMW side-wind facility in Aschheim, Germany. Measurements of the incoming flow in front of the car were taken with eleven five-hole dynamic pressure probes, and separately, time-resolved surface pressure measurements at 188 locations were performed. Unsteady characteristics (not able to be identified in quasi-steady modelling) have been identified: the flow in separated regions on the vehicle’s leeward side takes longer to develop than at the windward side, and spatially, the vehicle experiences local crosswind as it gradually enters the crosswind.

Abstract In this article, a methodology is presented to assess the influence of time-averaged deformations on a production car of the 2018 A-class due to wind load. Exemplary, the deformations of the front and rear bumper are investigated. The aerodynamic development of vehicles at Mercedes-Benz is divided into several phases. When comparing, force coefficients differences can be observed between these distinct hardware stages as well as when comparing steady-state simulations to wind tunnel measurements. In early phases when prototype vehicles are not yet available, so-called aero foam models are used. These are well-defined full-sized vehicle models as the outer skin is milled from Polyurethane. Important aerodynamic characteristics such as an engine compartment with a cooling module, deflecting axles with rotatable wheels, and underbody covers are represented.

Abstract The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model.