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Abstract Introduction: This study evaluates driving ability in those with Charcot Marie Tooth Disease Type 1A, a hereditary peripheral neuropathy. Methods: Individuals with Charcot Marie Tooth Disease Type 1A (n = 18, age = 42 ± 7) and controls (n = 19; age = 35 ± 10) were evaluated in a driving simulator. The Charcot Marie Tooth Neuropathy Score version 2 was obtained for individuals. Rank Sum test and Spearman rank correlations were used for statistical analysis. Results: A 74% higher rate of lane departures and an 89% higher rate of lane deviations were seen in those with Charcot Marie Tooth Disease Type 1A than for controls (p = 0.005 and p < 0.001, respectively). Lane control variability was 10% higher for the individual group and correlated with the neuropathy score (rS = 0.518, p = 0.040), specifically sensory loss (rS = 0.710, p = 0.002) and pinprick sensation loss in the leg (rS = 0.490, p = 0.054).

Abstract Investigation of vehicle resistant forces and power losses is of crucial importance owing to current state of energy consumption in transport sector. Meanwhile, considerable portion of resistant forces in a ground vehicle is traced back to tires. Pneumatic tires are known to be a source of energy dissipation as a consequence of their viscoelastic nature. The current study aims to provide a modification to tire resistance by considering the power loss in a tire due to longitudinal slip. The modified tire resistance is comprised of rolling resistance and a newly introduced resistance caused by tire slip, called slip resistance. The physical model is chosen for parameters sensitivity study since the tractive force is described in this model via tangible physical parameters, e.g. tire tangential stiffness, coefficient of friction, and contact patch length.

Abstract Rolling-resistance is leading the direction of numerous tire developments due to its significant effect on fuel consumption and CO2 emissions considering the vehicles in use globally. Many attempts were made to reduce rolling-resistance in vehicles, but with no or limited success due to tire complexity and trade-offs. This article investigates the concept of multiple chambers inside the tire as a potential alternative solution for reducing rolling-resistance. To accomplish that, novel multi-chamber designs were introduced and numerically simulated through finite-element (FE) modeling. The FE models were compared against a standard design as the baseline. The influences on rolling-resistance, grip, cornering, and mechanical comfort were studied. The multi-chambers tire model reduced rolling-resistance considerably with acceptable trade-offs. Independent air volumes isolating tread from sidewalls would maintain tire’s profile effectively.

Abstract To reduce fuel consumption and carbon dioxide (CO2) emissions from mobile air conditioning (A/C) systems, “U.S. Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards” identified solar/thermal technologies such as solar control glazings, solar reflective paint, and active and passive cabin ventilation in an off-cycle credit menu. National Renewable Energy Laboratory (NREL) researchers developed a sophisticated analysis process to calculate U.S. light-duty A/C fuel use that was used to assess the impact of these technologies, leveraging thermal and vehicle simulation analysis tools developed under previous U.S. Department of Energy projects. Representative U.S. light-duty driving behaviors and weighting factors including time-of-day of travel, trip duration, and time between trips were characterized and integrated into the analysis.

Abstract The use of simulation is a long-standing industry standard at every level of flight training. Historically, given the acquisition and maintenance costs associated with such equipment, full-motion devices have been reserved for advanced corporate and airline training programs. The Motion Cueing Seat (MCS) is a relatively inexpensive alternative to full-motion flight simulators and has the potential to enhance the fixed-base flight simulation in primary flight training. In this article, we discuss the results of an evaluation of the effect of motion cueing on pilot workload and performance during primary instrument training. Twenty flight students and instructors from a collegiate flight training program participated in the study. Each participant performed three runs of a basic circuit using a fixed-base Advanced Aviation Training Device (AATD) and an MCS.

Abstract Commercial vehicles contribute to the majority of freight transportation in the United States. They are also significant fuel consumers, with over 23% of fuel used in transportation in the United States. The gas price volatility and increasingly stringent regulation on greenhouse-gas emissions have driven manufacturers to adopt new fuel-efficient technologies. Among others, an advanced transmission control strategy, which can provide tangible improvement with low incremental cost. In the commercial sector, individual drivers have little or no interest in vehicle fuel economy, contrary to fleet owners. Aggressive driving behavior can greatly increase the real-world vehicle fuel consumption. However, the effectiveness of transmission calibration to match the shift strategy to the driving characteristics is still a challenge.

Abstract This study investigates methods to precisely control a coolant pump in an internal combustion engine. The goal of this research is to minimize power consumption while still meeting optimal performance, reliability and durability requirements for an engine at all engine-operating conditions. This investigation achieves reduced fuel consumption, reduced emissions, and improved powertrain performance. Secondary impacts include cleaner air for the earth, reduced operating costs for the owner, and compliance with US regulatory requirements. The study utilizes mathematical modeling of the cooling system using heat transfer, pump laws, and boiling analysis to set limits to the cooling system and predict performance changes.

Abstract This article investigates the fuel savings potential of a series hybrid military truck using a simultaneous battery pack design and powertrain supervisory control optimization algorithm. The design optimization refers to the sizing of the lithium-ion battery pack in the hybrid configuration. The powertrain supervisory control optimization determines the most efficient way to split the power demand between the battery pack and the engine. Despite the available design and control optimization techniques, a generalized mathematical formulation and solution approach for combined design and control optimization is still missing in the literature. This article intends to fill that void by proposing a unified framework to simultaneously optimize both the battery pack size and power split control sequence. This is achieved through a combination of genetic algorithm (GA) and Pontryagin’s minimum principle (PMP) where the design parameters are integrated into the Hamiltonian function.

Abstract Fuel consumption is at an all-time high, with crude oil set to get depleted in the next two decades. Drag force is one of the major components responsible for decreasing mileage and thus increasing fuel consumption in vehicles. Using passive modifications such as spherical depressions on the body surface, aerodynamic drag experienced by passenger vehicles can be significantly reduced. Spherical depressions are designed to delay flow separation, following which the wake size is reduced, resulting in a decrease in drag force. In this study, computer-aided design (CAD) models of generalized lightweight vehicles are made with dimples at the sides of the car, having a diameter of 60 mm and a center-to-center distance of 90 mm. Several models are created having depression aspect ratios (ARs) of 2, 4, 6, and 8, and each model is simulated to velocities of 22 m/s, 24 m/s, 26 m/s, 28 m/s, and 30 m/s.

Abstract The principal issue in choosing an energy management strategy (EMS) for hybrid electric vehicles (HEVs) has been the way of determining the optimal share of electric energy in hybrid drive. In this article, a novel EMS is proposed that, along with maximum engine efficiency in the hybrid drive, can optimize the share of battery energy for the maximum efficiency of vehicle power train expanded with an imaginary power plant that, by delivering the electric energy to a grid, feeds the vehicle battery. It is proved that the expanded power train efficiency has the local maximum for a wide range of wheel power demand. The relation between the wheel power demand in hybrid drive, the share of battery energy, and the maximum efficiency of the expanded power train is conducted offline. Downloaded to the onboard control system, it enables the operation with the instantaneously optimal share of battery energy and the control system to operate with the low computational load.

Abstract To solve the problem that it is difficult for drivers to control the vehicle at low speed, a new setting scheme of pedal map is proposed to ensure that the vehicle has the speed controllability in the full speed range. In this scheme, based on obtaining the maximum and minimum driving characteristics of the vehicle and the driving resistance characteristics of the vehicle, the pedal map is divided into a sensitive area and insensitive area. In the insensitive area, acceleration hysteresis is formed, which ensures that the throttle is slightly fluctuated and has good speed stability. At the same time, the sensitive area of the accelerator pedal is formed far away from the driving resistance curve to ensure that the vehicle has a great acceleration ability. To verify the effectiveness of the proposed scheme, the data of a commercial vehicle is selected for the design of the pedal map, and the driver-vehicle closed-loop test based on the driving simulator is conducted.

Abstract Diesel trucks play a crucial role in transportation activity and a major contributor to fuel consumption and air pollution. To improve the energy efficiency of diesel trucks, we develop a truck eco-approach and departure (EAD) system based on Signal Phase and Timing (SPaT) message from signal controllers and road grade information along the path. The proposed model consists of two levels—the lower level for powertrain-based fuel consumption estimation and the upper level for optimal trajectory planning. The powertrain model is designed for a diesel engine with a six-gear transmission and well calibrated using on-board Electronic Control Unit (ECU) data. The trajectory planning model is formulated as a shortest path problem with the combination of time, distance, and speed as the state on each node and fuel consumption rate as the cost on state transition.

Abstract Driving skills and, more in general, driver’s behavior may have a major impact on vehicle performances. They can affect not only the fuel consumption of the machine but, at the same time, also its productivity and the durability of many mechanical, electronic, and hydraulic components equipped on the vehicle. In this article, a model, able to reproduce different driver’s approaches to the machine, is shown. The longitudinal driver model has been developed in Matlab/Simulink and, firstly, employed on buses and trucks applications; then it has been also exported into a wheel loader plant model designed in Simcenter AMESim. The article is focused on how the driver model, integrated into the wheel loader plant model, can simulate custom cycles with a different driving style (high/low aggressiveness). It allows, on one hand, to emulate a real driver behavior and, on the other hand, to increase simulation repeatability and reproducibility.

Abstract As emissions regulations continue to tighten, both from lower imposed limits of pollutants, such as nitrous oxides (NOx), and in-use and real-world testing, the importance of quickly heating the aftertreatment to operating temperature during a cold start, as well as maintaining this temperature during periods of low engine load, is of increasing importance. Perhaps the best method of providing the necessary heating of the aftertreatment is to direct hot exhaust gasses to it directly from the engine. For heavy-duty diesel engines that utilize turbochargers, this is achieved by fully bypassing the exhaust flow around the turbine directly to the aftertreatment. However, this disables a conventional turbocharger, limiting engine operation to near-idle conditions during the bypass period.

Abstract Federal Aviation Administration (FAA)’s 20 years of research and development with 200 unleaded blends and full-scale engine tests on 45 high-octane unleaded blends has not found a “drop-in” unleaded replacement for aviation gasoline (AVGAS) 100 low lead (100LL) fuel. In this study, analysis of compatibility via optimization of Lycoming O-320 engine fuelled with RON 97, RON 98, RON 100, and AVGAS was conducted using the Response Surface Methodology (RSM). Test fuels were compositionally characterized based on Gas Chromatography (GC) analysis and were categorized based on types of Hydrocarbon (HC). Basic fuel properties of fuels in this research were analyzed and recorded. For optimization analysis, engine speed and fuel were considered as the input parameters.

Abstract In recent years, lean-burn gasoline Spark-Ignition (SI) engines have been a major subject of investigations. With this solution, in fact, it is possible to simultaneously reduce NOx raw emissions and fuel consumption due to decreased heat losses, higher thermodynamic efficiency, and enhanced knock resistance. However, the real applicability of this technique is strongly limited by the increase in cyclic variation and the occurrence of misfire, which are typical for the combustion of homogeneous lean air/fuel mixtures. The employment of a Pre-Chamber (PC), in which the combustion begins before proceeding in the main combustion chamber, has already shown the capability of significantly extending the lean-burn limit. In this work, the potential of an ultralean PC SI engine for a decisive improvement of the thermal efficiency is presented by means of numerical and experimental analyses.

Abstract This article aims to identify the best combination of intake port angle (IPA) and cylinder head pentroof angle (PA) of a gasoline direct injection (GDI) engine to achieve a simultaneous reduction in the fuel consumption and the exhaust emissions using computational fluid dynamics (CFD) and optimization techniques. The present study is carried out on a single-cylinder, four-stroke GDI engine. The design space is bound by the range of the IPA (35°, 80°) and the PA (5°, 20°). The initial data set consists of 80 design points, which are generated using the uniform Latin hypercube (ULH) algorithm. CFD simulations were carried out at all the points in the initial data set using CONVERGE at engine speed of 2,000 rev/min and the overall equivalence ratio of 0.7 ± 0.05.

Abstract Identifying and eliminating failure scenarios is critical in the development of autonomous vehicle (AV) systems. However, finding such failures through real-world vehicle-level testing is a difficult task as system disengagements and accidents are rare occurrences. Simulation approaches have been proposed to supplement vehicle-level testing and reduce the costs associated with operating large fleets of autonomous test vehicles. While one can run more vehicles in simulation than in the real world, applying traditional Monte Carlo sampling techniques to find failures still yields an unguided search and a large waste of computing resources. A more directed method than random sampling is needed to identify failure scenarios in a computationally efficient manner. Adaptive Stress Testing (AST) is a method that uses reinforcement learning (RL) paradigms to efficiently find failure scenarios in stochastic sequential decision-making systems.

Abstract Autonomous vehicles (AVs) are being rapidly introduced into our lives. However, public misunderstanding and mistrust have become prominent issues hindering the acceptance of these driverless technologies. The primary objective of this study is to evaluate the effectiveness of a driving simulator to help the public gain an understanding of AVs and build trust in them. To achieve this aim, we built an integrated simulation platform, designed various driving scenarios, and recruited 28 participants for the experiment. The study results indicate that a driving simulator effectively decreases the participants’ perceived risk of AVs and increases perceived usefulness. The proposed methodologies and findings of this study can be further explored by auto manufacturers and policymakers to provide user-friendly AV design.

Abstract Connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications.