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Engine downsizing, boosting, direct injection and variable valve actuation, have become industry standards for reducing CO2 emissions in current production vehicles. Because of the increasing complexity of the engine air path system and the high number of degrees of freedom for engine charge management, the design of air path control algorithms has become a difficult and time consuming process. One possibility to reduce the control development time is offered by Software-in-the-Loop (SIL) or Hardware-in-the-Loop (HIL) simulation methods. However, it is significantly challenging to identify engine air path system simulation models that offer the right balance between fidelity, mathematical complexity and computational burden for SIL or HIL implementation.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

Simulation-based design of connected and automated hybrid-electric vehicles is a challenging problem. The design space is large, the systems are complex, and the influence of connected and autonomous technology on the process is a new area of research. The Ohio State University EcoCAR Mobility Challenge team developed a comprehensive design and simulation approach as a solution. This paper covers the detailed simulation work conducted after initial design space reduction was performed to arrive at a P0-P4 hybrid vehicle with a gasoline engine. Two simulation environments were deployed in this strategy, each with unique advantages. The first was Autonomie, which is a commercial software tool that is well-validated through peer-reviewed studies. This allowed the team to evaluate a wide range of components in a robust simulation framework.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

Forensic injury accident reconstruction is used to determine what happened in an accident, typically for use in a court of law. In order to be admitted as evidence in a trial, the analysis must be scientifically sound, and performed utilizing standard scientific methods. The use of motion capture methods for forensic biomechanical applications provides an accurate, scientifically proven technique that has advantages not only due to its technical capabilities, but also in its highly visual manner of result presentation. This paper illustrates the use of a near-infrared motion capture system in two different types of forensic biomechanics applications. In one case, motion capture was utilized to capture externally applied force data and synchronized with the three-dimensional human body posture to perform a musculoskeletal biomechanics analysis.

This research focuses on the development and performance of analytical models to simulate a tractor-semitrailer in straight-ahead braking. The simulations were modified and tuned to simulate full-treadle braking with all brakes functioning correctly, as well as the behavior of the tractor-semitrailer rig under full braking with selected brakes disabled. The models were constructed in TruckSim and based on a tractor-semitrailer used in dry braking performance testing. The full-scale vehicle braking research was designed to define limits for engineering estimates on stopping distance when Class 8 air-braked vehicles experience partial degradation of the foundation brake system. In the full scale testing, stops were conducted from 30 mph and 60 mph, with the combination loaded to 80,000 lbs (gross combined weight or GCW), half payload, and with the tractor-semitrailer unladen (lightly loaded vehicle weight, or LLVW).

The Federal Side Impact Test Procedure prescribed by FMVSS 214, simulates a central, orthogonal intersection collision between two moving vehicles by impacting the side of the stationary test vehicle with a moving test buck in a crabbed configuration. While the pre- and post-impact speeds of the vehicles involved in an accident can not be duplicated using this method, closing speeds, vehicle damage, vehicle speed changes and vehicle accelerations can be duplicated. These are the important parameters for the examination of vehicle restraint system performance and the prediction of occupant injury. The acceptability of this method of testing is not as obvious for the reconstruction of accidents where the impact is non-central, or the angle of impact is not orthogonal. This paper will examine the use of crash testing with a single moving vehicle to simulate oblique or non-central collisions between two moving vehicles.

Thoracic injury criteria have been previously developed to predict thoracic injury for vehicle occupants as a function of biomechanical response. Historically, biomechanical testing of post-mortem human surrogates (PMHS) for injury criteria development has primarily been focused on mid-sized males. Response targets and injury criteria for other demographics, including small females, have been determined by scaling values from mid-sized males. The objective of this study was to explore the applicability of scaled injury criteria to their representative population. Two PMHS were subjected to a side-impact loading condition which replicates a near-side, MDB-to-vehicle impact for the driver. This was accomplished using the Advanced Side Impact System, or ASIS, on a HYGE sled. The sled acceleration matched the acceleration profile of an impacted vehicle, while the four pneumatic cylinders of the ASIS produced realistic door intrusion.

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions.

Topology optimization is a branch of structural optimization which solves an optimal material distribution problem. The resulting structural topology, for a given set of boundary conditions and constraints, has an optimal performance (e.g. minimum compliance). Conventional 3D topology optimization algorithms achieve quality optimized results; however, it is an extremely computationally intensive task which is, in general, impractical and computationally unachievable for real-world structural optimal design processes. Therefore, the current development of rapid topology optimization technology is experiencing a major drawback. To address the issues, a new approach is presented to utilize the powerful abilities of large deep learning models to replicate this design process for 3D structures. Adversarial models, primarily Wasserstein Generative Adversarial Networks (WGAN), are constructed which consist of 2 deep convolutional neural networks (CNN) namely, a discriminator and a generator.

Battery packs used in electrified automotive powertrains support heavy electrical loads resulting in significant heat generation within them. Cooling systems are used to regulate the battery pack temperatures, helping to slow down battery aging. Vehicle-level energy consumption simulations serve as a first step for determining the specifications of a battery cooling system based on the duty cycle and interactions with the rest of the powertrain. This paper presents the development of a battery model that takes into account the energy impact of heating in the battery and demonstrates its use in a vehicle-level energy consumption simulator to set the specifications of a suitable cooling system for a vehicle application. The vehicle application used in this paper is a Class 6 Pickup and Delivery commercial vehicle with a Range-Extended Electric Vehicle (REEV) powertrain configuration.

Many smart cities and car manufacturers have been investing in Vehicle to Infrastructure (V2I) applications by integrating the Dedicated Short-Range Communication (DSRC) technology to improve the fuel economy, safety, and ride comfort for the end users. For example, Columbus, OH, USA is placing DSRC Road Side Units (RSU) to the traffic lights which will publish traffic light Signal Phase and Timing (SPaT) information. With DSRC On Board Unit (OBU) equipped vehicles, people will start benefiting from this technology. In this paper, to accelerate the V2I application development for Connected and Autonomous Vehicles (CAV), a Hardware in the Loop (HIL) simulator with DSRC RSU and OBU is presented. The developed HIL simulator environment is employed to implement, develop and evaluate V2I connected vehicle applications in a fast, safe and cost-effective manner.

Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program.

This paper and the associated lecture present an overview of technology trends and of market and business opportunities created by technology, as well as of the challenges posed by environmental and economic considerations. Commercial vehicles are one of the engines of our economy. Moving goods and people efficiently and economically is a key to continued industrial development and to strong employment. Trucks are responsible for nearly 70% of the movement of goods in the USA (by value) and represent approximately 300 billion of the 3.21 trillion annual vehicle miles travelled by all vehicles in the USA while public transit enables mobility and access to jobs for millions of people, with over 10 billion trips annually in the USA creating and sustaining employment opportunities.

With the advancement of the autonomous vehicle development, the different possibilities of improving fuel economy have increased significantly by changing the driver or powertrain response under different traffic conditions. Development of new fuel-efficient driving strategies requires extensive experiments and simulations in traffic. In this paper, a fuel efficiency simulator environment with existing simulator software such as Simulink, Vissim, Sumo, and CarSim is developed in order to reduce the overall effort required for developing new fuel-efficient algorithms. The simulation environment is created by combining a mid-sized sedan MATLAB-Simulink powertrain model with a realistic microscopic traffic simulation program. To simulate the traffic realistically, real roads from urban and highway sections are modeled in the simulator with different traffic densities.

The parameters determining shift quality control in automatic transmissions are determined as part of the calibration of the transmission control. The resulting control system typically has three components: feedforward control, where the control output is determined before a gearshift; feedback control, where the control output is determined during the gearshift based on sensed feedback; and learning control (adaptation), where the feedforward or feedback controller parameters are modified after the current gearshift has ended and before the next similar gearshift begins. Gearshifts involving the same ratio change are referred to here as similar gearshifts, though such gearshifts may involve differences in other variables such as vehicle speed or engine torque.

The objective is to determine whether responses and injury risks for pediatric occupants in child restraint systems (CRS) are affected by vehicle seat cushion stiffness and fore/aft cushion length. Eighteen sled tests were conducted using the Federal Motor Vehicles Safety Standard (FMVSS) 213 frontal pulse (48 km/h). Seats from a recent model year vehicle were customized by the manufacturer with three different levels of cushion stiffness: compliant, mid-range, and stiff. Each stiffness level was quantified using ASTM D 3574-08 and all were within the realistic range of modern production seats. The usable length of each seat cushion was manipulated using foam spacers provided by the manufacturer. Two different seat lengths were examined: short (34.0 cm) and long (43.5 cm).

In the last decade, the use of Driver-in-the-Loop (DiL) simulators has significantly increased in research, product development, and motorsports. To be used as a verification tool in research, simulators must show a level of correlation with real-world driving for the chosen use case. This study aims to assess the validity of a low-cost, limited travel Vehicle Dynamics Driver-in-Loop (VDDiL) simulator by comparing on-road and simulated driving data using a statistical evaluation of longitudinal and lateral metrics. The process determines if the simulator is appropriate for verifying control strategies and optimization algorithms for longitudinal vehicle dynamics and evaluates consistency in the chosen metrics. A validation process explaining the experiments, choice of metrics, and analysis tools used to perform a validation study from the perspective of the longitudinal vehicle model is shown in this study.

Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping.