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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.

Reviewer Acknowledgment

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 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 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 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 It is widely understood that the thermal state of a light-duty vehicle at the beginning of a trip influences the vehicle performance throughout the drive cycle. Cold starts, or initial states with component temperatures near ambient conditions, are strongly correlated with reduced vehicle performance and energy efficiency and increased emissions. Despite this understanding, there is little literature available that characterizes initial thermal states beyond empirical studies and simplified analyses of dwell times. We introduce a framework that considers vehicle activity patterns, including the previous drive event, duration of the previous dwell event, and relevant ambient conditions occurring during these events. Moreover, the framework allows for technologies to influence the prominence of cold starts and warm starts.

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 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 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 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 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 The 2010 Environmental Protection Agency (EPA) Emission Standard for heavy-duty engines required 0.2 g/bhp-hr over certification cycles (cold and hot Federal Test Procedure [FTP]), and the California Air Resources Board (CARB) standards require 0.02 g/bhp-hr for the same cycles leading to a 90% reduction of overall oxides of nitrogen (NOx) emissions. Similar reductions may be considered by the EPA through its Cleaner Trucks Initiative program. In this article, aftertreatment system components consisting of a diesel oxidation catalyst (DOC); a selective catalytic reduction (SCR) catalyst on a diesel particulate filter (DPF), or SCR-F; a second DOC (DOC2); and a SCR along with two urea injectors have been analyzed, which could be part of an aftertreatment system that can achieve the 0.02 g/bhp-hr standard.

Abstract Autonomy is a key enabling factor in uncrewed aircraft system (UAS) and advanced air mobility (AAM) applications ranging from cargo delivery to structure inspection to passenger transport, across multiple sectors. In addition to guiding the UAS, autonomy will ensure that they stay safe in a large number of off-nominal situations without requiring the operator to intervene. While the addition of autonomy enables the safety case for the overall operation, there is a question as to how we can assure that the autonomy itself will work as intended. Specifically, we need assurable technical approaches, operational considerations, and a framework to develop, test, maintain, and improve these capabilities. We make the case that many of the key autonomy functions can be realized in the near term with readily assurable, even certifiable, design approaches and assurance methods, combined with risk mitigations and strategically defined concepts of operations.

Abstract Integrated control for automated vehicles in platoons with nonlinear coupled dynamics is developed in this article. A nonlinear MPC approach is used to address the multi-input multi-output (MIMO) nature of the problem, the nonlinear vehicle dynamics, and the platoon constraints. The control actions are determined by using model-based prediction in conjunction with constrained optimization. Two distinct scenarios are then simulated. The first scenario consists of the multivehicle merging into an existing platoon in a controlled environment in the absence of noise, whereas the effects of external disturbances, modeling errors, and measurement noise are simulated in the second scenario. An extended Kalman filter (EKF) is utilized to estimate the system states under the sensor and process noise effectively.

Abstract Heavy-duty (HD) diesel engines are the primary propulsion systems in use within the transportation sector and are subjected to stringent oxides of nitrogen (NOx) and particulate matter (PM) emission regulations. The objective of this study is to develop a robust calibration technique to optimize HD diesel engine for performance and emissions to meet current and future emissions standards during certification and real-world operations. In recent years, California - Air Resources Board (C-ARB) has initiated many studies to assess the technology road maps to achieve Ultra-Low NOx emissions for HD diesel applications [1]. Subsequently, there is also a major push for the complex real-world driving emissions as the confirmatory and certification testing procedure in Europe and Asia through the UN-ECE and ISO standards.

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.