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The rise of Software-Defined Vehicles (SDV) has rapidly advanced the development of Advanced Driver Assistance Systems (ADAS), Autonomous Vehicle (AV), and Battery Electric Vehicle (BEV) technology. While AVs need power to compute data from perception to controls, BEVs need the efficiency to optimize their electric driving range and stand out compared to traditional Internal Combustion Engine (ICE) vehicles. AVs possess certain shortcomings in the current world, but SAE Level 2+ (L2+) Automated Vehicles are the focus of all major Original Equipment Manufacturers (OEMs). The most common form of an SDV today is the amalgamation of AV and BEV technology on the same platform which is prominently available in most OEM’s lineups. As the compute and sensing architectures for L2+ automated vehicles lean towards a computationally expensive centralized design, it may hamper the most important purchasing factor of a BEV, the electric driving range.

Structural Health Monitoring (SHM), especially in the field of rotary machinery diagnosis, plays a crucial role in determining the defect category as well as its intensity in a machine element. This paper proposes a new framework for real-time classification of structural defects in a roller bearing test rig using time domain-based classification algorithms. Along with the bearing defects, the effect of eccentric shaft loading has also been analyzed. The entire system comprises of three modules: sensor module – using accelerometers for data collection, data processing module – using time-domain based signal processing algorithms for feature extraction, and classification module – comprising of deep learning algorithms for classifying between different structural defects occurring within the inner and outer race of the bearing.

An energy management strategy for a prototype P4 parallel hybrid Chevrolet Blazer is developed for the EcoCAR Mobility Challenge. The objective of the energy management strategy is to reduce energy consumption while maintaining the drive quality targets of a conventional vehicle. A comprehensive model of the hybrid powertrain and vehicle physics is constructed to aid in the development of the control strategy. To improve fuel efficiency, a Willans line model is developed for the conventional powertrain and used to develop a rule-based torque split strategy. The strategy maximizes high efficiency engine operation while reducing round trip losses. Calibratable parameters for the torque split operating regions allow for battery state of charge management. Torque request and filtering algorithms are also developed to ensure the hybrid powertrain can smoothly and reliably meet driver demand.

The pre-prototype development of a simulated rule-based hybrid energy management strategy for a 2019 Chevrolet Blazer RS converted parallel P4 full hybrid is presented. A vehicle simulation model is developed using component bench data and validated using EPA-reported dynamometer fuel economy test data. A combined Willans line model is proposed for the engine and transmission, with hybrid control rules based on efficiency-derived engine power thresholds. Algorithms are proposed for battery state of charge (SOC) management including engine loading and one pedal strategies, with battery SOC maintained within 20% to 80% safe limits and charge balanced behavior achieved. The simulated rule-based hybrid control strategy for the hybrid vehicle has an energy consumption reduction of 20% for the Hot 505, 3.6% for the HwFET, and 12% for the US06 compared to the stock vehicle.

The influence of driver modeling and drive cycle target speed trace modification on vehicle dynamics within energy consumption simulations is studied. EPA dynamometer speed error criteria and the SAE J2951 Drive Quality Evaluation for Chassis Dynamometer Testing standard are applied to simulation outputs as proposed components of simulation validation, providing guidelines for acceptable vehicle speed outputs and allowing comparison of simulation results to reported EPA dynamometer test statistics. The combined effect of driver model tuning and drive cycle interpolation methods is investigated for the UDDS, HwFET and US06 drive cycles, with EPA-specified linearly interpolated speed trace and a PI controller driver as a baseline result.

A new and unique electric vehicle powertrain model based on bidirectional power flow for propel and regenerative brake power capture is developed and applied to production battery electric vehicles. The model is based on a Willans line model to relate power input from the battery and power output to tractive effort, with one set of parameters (marginal efficiency and an offset loss) for the bidirectional power flow through the powertrain. An electric accessory load is included for the propel, brake and idle phases of vehicle operation. In addition, regenerative brake energy capture is limited with a regen fraction (where the balance goes to friction braking), a power limit, and a low-speed cutoff limit. The purpose of the model is to predict energy consumption and range using only tractive effort based on EPA published road load and test mass (test car list data) and vehicle powertrain parameters derived from EPA reported unadjusted UDDS and HWFET energy consumption.

Modern thermal propulsion systems (TPS) as part of hybrid powertrains are becoming increasingly complex. They have an increased number of components in comparison to traditionally powered vehicles leading to increased demand in packaging requirements. Many of the components in these systems relate to achieving efficiency gains, weight saving and pollutant reduction. This includes turbochargers and diesel or gasoline particulate filters for example and these are known to be very sensitive to inlet boundary conditions. When overcoming packaging requirements, sub-optimal flow distributions throughout the TPS can easily occur. Moreover, the individual components are often designed in isolation assuming relatively flat and artificially quiescent inlet flow conditions in comparison to those they are actually presented with. Thus, some of the efficiency benefits are lost through reduced component aerodynamic efficiency.

The application of Computational Fluid Dynamics (CFD) for three-dimensional (3D) combustion analysis coupled with detailed chemistry in engine development is hindered by its expensive computational cost. Chemistry computation may occupy as much as 90% of the total computational cost. In the present paper, a new two-step iso-octane combustion model was developed for spark-ignited (SI) engine to maximize computational efficiency while maintaining acceptable accuracy. Starting from the model constants of an existing global combustion model, the new model was developed using an approach based on sensitivity analysis to approximate the results of a reference skeletal mechanism. The present model involves only five species and two reactions and utilizes only one uniform set of model constants. The validation of the new model was performed using shock tube and real SI engine cases.

An unconventional conceptual design of truck chassis with multi-functional cross-members is proposed, and an optimization framework is developed to optimize its structure to minimize mass while satisfying stiffness and modal frequency constraints. The side rails are C-sectional channels of variable height and were divided into six sections, each with different thickness distribution for the flanges and the web. The gearbox cross-member and the intermediate cross-members are compressed-air cylinders, and hence they act as multi-functional components. The dimensions and thickness of the side rails and the air-tank cross members are defined by a set of parameters which are considered as design variables in the optimization problem. The structure consists of three additional fixed cross-members which are modeled using beam elements. The limits of the design variables are decided while considering manufacturing limits.

Vehicle surface contamination can degrade not only soldier vision but also the effectiveness of camera and sensor systems mounted externally on the vehicle for autonomy and situational awareness. In order to control vehicle surface contamination, a better understanding of dust particle generation, transport and accumulation is necessary. The focus of the present work is simulation of vehicle surface contamination on the rear part of the vehicle due to the interaction of the combat vehicle track with the ground and dust in the surrounding ambient atmosphere. A notional tracked military vehicle is used for the Computational fluid dynamics (CFD) simulation. A CFD methodology with one-way-coupled Lagrangian particle modeling is used. The simulation is initially run with only air flow to solve the air pressure, velocity, and turbulence quantities in a steady state condition.

This paper focuses on the analysis of an EcoRouting system with minimum and maximum number of conditional stops. The effect on energy consumption with the presence and absence of road-grade information along a route is also studied. An EcoRouting system has been developed that takes in map information and converts it to a graph of nodes containing route information such as speed limits, stop lights, stop signs and road grade. A variable acceleration rate synthesis methodology is also introduced in this paper that takes into consideration distance, acceleration, cruise speed and jerk rate as inputs to simulate driver behavior on a given route. A simulation study is conducted in the town of Blacksburg, Virginia, USA to analyze the effects of EcoRouting in different driving conditions and to examine the effects of road grade and stop lights on energy consumption.

Optimizing the trade-off between reliability and cost of operating a microgrid, including vehicles as both loads and sources, can be a challenge. Optimal energy management is crucial to develop strategies to improve the efficiency and reliability of microgrids, as well as new communication networks to support optimal and reliable operation. Prior approaches modeled the grid using MATLAB, but did not include the detailed physics of loads and sources, and therefore missed the transient effects that are present in real-time operation of a microgrid. This article discusses the implementation of a physics-based detailed microgrid model including a diesel generator, wind turbine, photovoltaic array, and utility. All elements are modeled as sources in Simulink. Various loads are also implemented including an asynchronous motor. We show how a central control algorithm optimizes the microgrid by trying to maximize reliability while reducing operational cost.

The objective of this study was to optimize the occupant restraint systems for a light tactical vehicle in frontal crashes. A combination of sled testing and computational modeling were performed to find the optimal seatbelt and airbag designs for protecting occupants represented by three size of ATDs and two military gear configurations. This study started with 20 sled frontal crash tests to setup the baseline performance of existing seatbelts, which have been presented previously; followed by parametric computational simulations to find the best combinations of seatbelt and airbag designs for different sizes of ATDs and military gear configurations involving both driver and passengers. Then 12 sled tests were conducted with the simulation-recommended restraint designs. The test results were further used to validate the models. Another series of computational simulations and 4 sled tests were performed to fine-tune the optimal restraint design solutions.

A full-system, end-to-end blast modeling and simulation of vehicle underbody buried blast events typically includes detailed modeling of soil, high explosive (HE) charge and air. The complex computations involved in these simulations take days to just capture the initial 50-millisecond blast-off phase, and in some cases, even weeks. The single most intricate step in the buried blast event simulation is in the modeling of the explosive loading on the underbody structure from the blast products; it is also one of the most computationally expensive steps of the simulation. Therefore, there is significant interest in the modeling and simulation community to develop various methodologies for fast running tools to run full simulation events in quicker turnarounds of time.

In this study, we extend and improve on the methods introduced by Brudnak et al. [1] by adding a second objective to the reduction of test time. This second objective under consideration is to diminish or reduce the number of cuts or deletions to the time histories during an editing process. As discussed in [1], segment-based methods consider each segment for retention or deletion based on its own localized severity, not considering the segments around it. As a result, retained segments can be widely scattered in the time domain depending on signal characteristics and therefore a large number of cuts can be induced unintentionally. Regardless of the joining method, such cuts and joins require artificial signal processing and should therefore be minimized. In this paper we present techniques to minimize these cuts while at the same time maintaining our original goals of time reduction and severity retention.

The goal of this paper is to estimate near-side injury risk in vehicles with the best side impact performance in the U.S. New Car Assessment Program (NCAP). The longer-term goal is to predict the incidence of crashes and injury outcomes in the U.S. in a future fleet of the 2025-time frame after current active and passive safety countermeasures are fully implemented. Our assumption was that, by 2025, all new vehicles will have side impact passive safety performance equivalent to current U.S. NCAP five star ratings. The analysis was based on real-world crashes extracted from case years 2010-2015 in the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) in which front-row occupants of late-model vehicles (Model Year 2011+) were exposed to a near-side crash.

Military vehicles are typically armored, hence the open surface area for heat rejection is limited. Hence, the cooling parasitic load for a given heat rejection can be considerably higher and important to consider upfront in the system design. Since PEMFCs operate at low temp, the required cooling flow is larger to account for the smaller delta temperature to the air. This research aims to address the combined problem of optimal sizing of the lithium ion battery and PEM Fuel Cell stack along with development of the scalable power split strategy for small a PackBot robot. We will apply scalable physics-based models of the fuel cell stack and balance of plant that includes a realistic and scalable parasitic load from cooling integrated with existing scalable models of the lithium ion battery. This model allows the combined optimization that captures the dominant trends relevant to component sizing and system performance.

Analysis and design of total temperature probes for accurate measurements in hot, high-speed flows remains a topic of great interest in aerospace propulsion and a number of other engineering areas. Despite an extensive prior literature on the subject, prediction of error sources from convection, conduction and radiation is still an area of great concern. For hot-flow conditions, the probe is normally mounted in a cooled support, leading to substantial axial conduction along the length of the probe. Also, radiation plays a very important role in most hot, high-speed conditions. One can apply detailed computational methods for simultaneous convection, conduction and radiation heat transfer, but such approaches are not suitable for rapid, routine analysis and design studies. So, there is still a place for low-order approximate methods, and that is the subject of this paper.

Mercury is a high-fidelity, physics-based object-oriented software for conducting simulations of vehicle performance evaluations for requirements and engineering metrics. Integrating cutting-edge, massively parallel modeling techniques for soft, cohesive and dry granular soil that will integrate state-of-the-art soil simulation with high-fidelity multi-body dynamics and powertrain modeling to provide a comprehensive mobility simulator for ground vehicles. The Mercury implements the Chrono::Vehicle dynamics library for vehicle dynamics, which provides multi-body dynamic simulation of wheeled and tracked vehicles. The powertrain is modeled using the Powertrain Analysis Computational Environment (PACE), a behavior-based powertrain analysis based on the U.S. Department of Energy’s Autonomie software. Vehicle -terrain interaction (VTI) is simulated with the Ground Contact Element (GCE), which provides forces to the Chrono-vehicle solver.

This paper considers optimal power management during the establishment of an expeditionary outpost using battery and vehicle assets for electrical generation. The first step in creating a new outpost is implementing the physical protection and barrier system. Afterwards, facilities that provide communications, fires, meals, and moral boosts are implemented that steadily increase the electrical load while dynamic events, such as patrols, can cause abrupt changes in the electrical load profile. Being able to create a fully functioning outpost within 72 hours is a typical objective where the electrical power generation starts with batteries, transitions to gasoline generators and is eventually replaced by diesel generators as the outpost matures. Vehicles with power export capability are an attractive supplement to this electrical power evolution since they are usually on site, would reduce the amount of material for outpost creation, and provide a modular approach to outpost build-up.