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The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

Connectivity and autonomy in vehicles promise improved efficiency, safety and comfort. The increasing use of embedded systems and the cyber element bring with them many challenges regarding cyberattacks which can seriously compromise driver and passenger safety. Beyond penetration testing, assessment of the security vulnerabilities of a component must be done through the design phase of its life cycle. This paper describes the development of a benchtop testbed which allows for the assurance of safety and security of components with all capabilities from Model-in-loop to Software-in-loop to Hardware-in-loop testing. Environment simulation is obtained using the AV simulator, CARLA which provides realistic scenarios and sensor information such as Radar, Lidar etc. MATLAB runs the vehicle, powertrain and control models of the vehicle allowing for the implementation and testing of customized models and algorithms.

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections.

Improvements in highway fuel economy require clever design and novel methods to reduce the drag coefficient. The integration of active flow control devices into vehicle design shows promise for greater reductions in drag coefficient. This paper examines the use of fluidic oscillators for separation control at the rear of an Ahmed vehicle model. A fluidic oscillator is a simple device that generates a sweeping jet output, similar to some windshield wiper spray nozzles, and is increasingly recognized as an efficient means to control separation. In this study, fluidic oscillators were used to blow unsteady air jets and control flow separation on rear boat-tail flaps, achieving drag reductions greater than 70 counts. The method appears to scale favorably to a larger model, and realistic effects such as a rolling road appear to have a small impact on the oscillator's control authority.

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.

In early phases of conceptual design stages for developing a new car in the modern automobile industry, the lack of systematic methodology to efficiently converge to an agreement between the aesthetics and aerodynamic performance tremendously increases budget and time. During these procedures, one of the most important tasks is to create geometric information which is versatilely morphable upon the demands of both of stylists and engineers. In this perspective, this paper proposes a Spline-based Modeling Algorithm (SMA) to implement into performing aerodynamic design optimization research based on CFD analysis. Once a 3-perspective schematic of a car is given, SMA regresses the backbone boundary lines by using optimum polynomial interpolation methods with the best goodness of fit, eventually reconstructing the 3D shape by linearly interpolating from the extracted boundaries minimizing loss of important geometric features.

Since, ice accretion can significantly degrade the performance and the stability of an airborne vehicle, it is imperative to be able to model it accurately. While ice accretion studies have been performed on airplane wings and helicopter blades in abundance, there are few that attempt to model the process on more complex geometries such as fuselages. This paper proposes a methodology that extends an existing in-house Extended Messinger solver to complex geometries by introducing the capability to work with unstructured grids and carry out spatial surface streamwise marching. For the work presented here commercial solvers such as STAR-CCM+ and ANSYS Fluent are used for the flow field and droplet dispersed phase computations. The ice accretion is carried out using an in-house icing solver called GT-ICE. The predictions by GT-ICE are compared to available experimental data, or to predictions by other solvers such as LEWICE and STAR-CCM+.

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.

Two-scale command shaping is a recently proposed feedforward control method aimed at mitigating undesirable vibrations in nonlinear systems. The TSCS strategy uses a scale separation to cancel oscillations arising from nonlinear behavior of the system, and command shaping of the remaining linear problem. One promising application of TSCS is in reducing engine restart and shutdown vibrations found in conventional and in hybrid electric vehicle powertrains equipped with start-stop features. The efficacy of the TSCS during internal combustion engine restart has been demonstrated theoretically and experimentally in the authors’ prior works. The present article presents simulation results and describes the verified experimental apparatus used to study TSCS as applied to the ICE shutdown case. The apparatus represents a typical HEV powertrain and consists of a 1.03 L three-cylinder diesel ICE coupled to a permanent magnet alternating current electric machine through a spur gear coupling.

With the current drive of automotive and technology companies towards producing vehicles with higher levels of autonomy, it is inevitable that there will be an increasing number of SAE level L4-L5 autonomous vehicles (AVs) on roadways in the near future. Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways. The roadways used in these simulations were replicas of real roadways in and around Columbus, Ohio, including an AV shuttle routes in operation.

Dynamic programming (DP) has been used for optimal control of hybrid powertrain and vehicle speed optimization particularly in design phase for over a couple of decades. With the advent of autonomous and connected vehicle technologies, automotive industry is getting closer to implementing predictive optimal control strategies in real time applications. The biggest challenge in implementation of optimal controls is the limitation on hardware which includes processor speed, IO speed, and random access memory. Due to the use of autonomous features, modern vehicles are equipped with better onboard computational resources. In this paper we present a comparison between multiple hardware options for dynamic programming. The optimal control problem considered, is the optimization of travel time and fuel economy by tuning the torque split ratio and vehicle speed while maintaining charge sustaining operation.

Power-split hybrid-electric vehicles (HEVs) employ two power paths between the internal combustion (IC) engine and the driven wheels routed through gearing and electric machines (EMs) composing an electrically variable transmission (EVT). The EVT allows IC engine control such that rotational speed can be independent of vehicle speed at all times. By breaking the rigid mechanical connection between the IC engine and the driven wheels, the EVT allows the IC engine to operate in the most efficient region of its characteristic brake specific fuel consumption (BSFC) map. If the most efficient IC engine operating point produces more power than is requested by the driver, the excess IC engine power can be stored in the energy storage system (ESS) and used later. Conversely, if the most efficient IC engine operating point does not meet the power request of the driver, the ESS delivers the difference to the wheels through the EMs.

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.

With the advent of autonomous vehicles, the human driver’s attention will slowly be relinquished from the driving task. It will allow drivers to participate in more non-driving related activities, such as engaging with information and entertainment systems. However, the automated driving system would need to notify the driver of upcoming points-of-interest on the road when the driver’s attention is focused on their screen rather than on the road or driving display. In this paper, we investigated whether providing directional alerts for an upcoming point-of-interest (POI) in or around the user’s active screen can augment their ability in relocating their visual attention to the POI on the road when traveling in a vehicle with Conditional Driving Automation. A user study (N = 15) was conducted to compare solutions for alerts that presented themselves in the participants’ central and peripheral field of view.

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies, such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication and automated driving capability. As the world of transportation gets more and more connected through these technologies, the need to implement algorithms with V2I capability is amplified. In this paper, an algorithm called Pass at Green, utilizing V2I and vehicle longitudinal automation to modify the speed profile of a mid-size generic vehicle to decrease fuel consumption has been studied. Pass at Green (PaG) uses Signal Phase and Timing (SPaT) information acquired from upcoming traffic lights, which are the current phase of the upcoming traffic light and remaining time that the phase stays active.

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.

This paper presents a comparative analysis of two different power-split hybrid-electric vehicle (HEV) powertrains using backward-looking simulations. Compared are the front-wheel drive (FWD) Toyota Hybrid System II (THS-II) and the FWD General Motors Allison Hybrid System II (GM AHS-II). The Toyota system employs a one-mode electrically variable transmission (EVT), while the GM system employs a two-mode EVT. Both powertrains are modeled with the same assumed mid-size sedan chassis parameters. Each design employs their native internal combustion (IC) engine because the transmission's characteristic ratios are designed for the respective brake specific fuel consumption (BSFC) maps. Due to the similarities (e.g., power, torque, displacement, and thermal efficiency) between the two IC engines, their fuel consumption and performance differences are neglected in this comparison.

For a better understanding of soot formation and oxidation processes in conventional diesel and biodiesel spray flames, the morphology, microstructure and sizes of soot particles directly sampled in spray flames fuelled with US#2 diesel and soy-methyl ester were investigated using transmission electron microscopy (TEM). The soot samples were taken at 50mm from the injector nozzle, which corresponds to the peak soot location in the spray flames. The spray flames were generated in a constant-volume combustion chamber under a diesel-like high pressure and high temperature condition (6.7MPa, 1000K). Direct sampling permits a more direct assessment of soot as it is formed and oxidized in the flame, as opposed to exhaust PM measurements. Density of sampled soot particles, diameter of primary particles, size (gyration radius) and compactness (fractal dimension) of soot aggregates were analyzed and compared. No analysis of the soot micro-structure was made.

The blade crossing event of a coaxial counter-rotating rotor is a potential source of noise and impulsive blade loads. Blade crossings occur many times during each rotor revolution. In previous research by the authors, this phenomenon was analyzed by simulating two airfoils passing each other at specified speeds and vertical separation distances, using the compressible Navier-Stokes solver OVERFLOW. The simulations explored mutual aerodynamic interactions associated with thickness, circulation, and compressibility effects. Results revealed the complex nature of the aerodynamic impulses generated by upper/lower airfoil interactions. In this paper, the coaxial rotor system is simulated using two trains of airfoils, vertically offset, and traveling in opposite directions. The simulation represents multiple blade crossings in a rotor revolution by specifying horizontal distances between each airfoil in the train based on the circumferential distance between blade tips.

A fuel cell powered airplane has been designed and constructed at the Georgia Insitute of Technology to develop an understanding of the design and implementation challenges of fuel cell-powered unmanned aerial vehicles (UAVs). A custom 448W net output proton exchange membrane fuel cell powerplant has been constructed and tested. A demonstrator aircraft was designed and built to accommodate this powerplant and the fuel cell powered aircraft has performed seven test flights to date. Test data show that the aircraft performance validates the models used for design and optimization and that the fuel cell aircraft is capable of longer endurance, higher performance test flights.