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Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are a normal part of technology transformations; however, this report will provide suggestions for effective stakeholder engagement. Click here to access the full SAE EDGETM Research Report portfolio.

Nyquist plots are a classical means to visualize a complex vibration frequency response function. By graphing the real and imaginary parts of the response, the dynamic behavior in the vicinity of resonances is emphasized. This allows insight into how modes are coupling, and also provides a means to separate the modes. Mathematical models such as Nyquist analysis are often embedded in frequency analysis hardware. While this speeds data collection, it also removes this visually intuitive tool from the engineer’s consciousness. The behavior of a single degree of freedom system will be shown to be well described by a circle on its Nyquist plot. This observation allows simple visual examination of the response of a continuous system, and the determination of quantities such as modal natural frequencies, damping factors, and modes shapes. Vibration test data from an auto rickshaw chassis are used as an example application.

This SAE EDGE™ Research Report explores how the deployment of automated vehicles (AVs) will affect the insurance industry and the principles of liability that underly the structure of insurance in the US. As we trade human drivers for suites of sensors and computers, who (or what) is responsible when there is a crash? The owner of the vehicle? The automaker that built it? The programmer that wrote the code? Insurers have over 100 years of experience and data covering human drivers, but with only a few years’ worth of information on AVs – how can they properly predict the true risks associated with their deployment? Without an understanding of the nature and risks of AVs, how can the government agencies that regulate the insurance industry provide proper oversight? Do the challenges AVs present require a total reworking of our insurance and liability systems, or can our current structures be adapted to fit them with minor modifications?

This SAE EDGE Research Report explores the many legal issues raised by the advent of automated vehicles. While promised to bring major changes to our lives, there are significant legal challenges that have to be overcome before they can see widespread use. A century’s worth of law and regulation were written with only human drivers in mind, meaning they have to be amended before machines can take the wheel. Everything from key federal safety regulations down to local parking laws will have to shift in the face of AVs. This report undertakes an examination of the AV laws of Nevada, California, Michigan, and Arizona, along with two failed federal AV bills, to better understand how lawmakers have approached the technology. States have traditionally regulated a great deal of what happens on the road, but does that still make sense in a world with AVs? Would the nascent AV industry be able to survive in a world with fifty potential sets of rules?

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

Crash safety researchers have an increased concern regarding the decreased thoracic deflection and the contributing injury causation factors among the elderly population. Sternum fractures are categorized as moderate severity injuries, but can have long term effects depending on the fragility and frailty of the occupant. Current research has provided detail on rib morphology, but very little information on sternum morphology, sternum fracture locations, and mechanisms of injury. The objective of this study is two-fold (1) quantify sternum morphology and (2) document sternum fracture locations using computed tomography (CT) scans and crash data. Thoracic CT scans from the University of Michigan Hospital database were used to measure thoracic depth, manubriosternal joint, sternum thickness and bone density. The sternum fracture locations and descriptions were extracted from 63 International Center for Automotive Medicine (ICAM) crash cases, of which 22 cases had corresponding CT scans.

This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results.

The recently published Lightweight Combat Vehicle Science and Technology Campaign [1] recommended the Army develop a quantitative understanding of the operational impact that weight reduction has to the Army and create appropriate metrics that would better reflect the performance trade with regards to weight. That paper raised the question of what a ton of weight is worth in operational effectiveness and cost. This paper is an attempt to clarify this complex topic. The impact of select programmatic considerations, operational considerations, and financial considerations are discussed. Throughout, the paper provides example analyses based on vehicle weight, performance, and cost data. The paper closes with a discussion of the issues presented, research recommendations, and closing comments.

Reliability and resiliency (R&R) definitions differ depending on the system under consideration. Generally, each engineering sector defines relevant R&R metrics pertinent to their system. While this can impede cross-disciplinary engineering projects as well as research, it is a necessary strategy to capture all the relevant system characteristics. This paper highlights the difficulties associated with defining performance of such systems while using smart microgrids as an example. Further, it develops metrics and definitions that are useful in assessing their performance, based on utility theory. A microgrid must not only anticipate load conditions but also tolerate partial failures and remain optimally operating. Many of these failures happen infrequently but unexpectedly and therefore are hard to plan for. We discuss real life failure scenarios and show how the proposed definitions and metrics are beneficial.

The Powertrain Analysis and Computational Environment (PACE) is a powertrain simulation tool that provides an advanced behavioral modeling capability for the powertrain subsystems of conventional or hybrid-electric vehicles. Due to its origins in Argonne National Lab’s Autonomie, PACE benefits from the reputation of Autonomie as a validated modeling tool capable of simulating the advanced hardware and control features of modern vehicle powertrains. However, unlike Autonomie that is developed and executed in Mathwork’s MATLAB/Simulink environment, PACE is developed in C++ and is targeted for High-Performance Computing (HPC) platforms. Indeed, PACE is used as one of several actors within a comprehensive ground vehicle co-simulation system (CRES-GV MERCURY): during a single MERCURY run, thousands of concurrent PACE instances interact with other high-performance, distributed MERCURY components.

This study evaluated the ISO 5353 Seat Index Point Tool (SIPT) as an alternative to the SAE J826 H-point manikin for measuring military seats. A tool was fabricated based on the ISO specification and a custom back-angle measurement probe was designed and fitted to the SIPT. Comparisons between the two tools in a wide range of seating conditions showed that the mean SIP location was 5 mm aft of the H-point, with a standard deviation of 7.8 mm. Vertical location was not significantly different between the two tools (mean - 0.7 mm, sd 4.0 mm). A high correlation (r=0.9) was observed between the back angle measurements from the two tools. The SIPT was slightly more repeatable across installations and installers than the J826 manikin, with most of the discrepancy arising from situations with flat seat cushion angles and either unusually upright or reclined back angles that caused the J826 manikin to be unstable.

This experimental study involves optimization of the scheduling of diesel post injections to reduce soot emissions from a light-duty diesel engine. Previous work has shown that certain post injection schedules can reduce engine-out soot emissions when compared to conventional injection schedules for the same engine load. The purpose of this study is to investigate the impact of post injection scheduling for a range of engine conditions on a light duty multicylinder turbodiesel engine (1.9L GM ZDTH). For each engine operating condition, a test grid was developed so that only two variables (post injection duration and the commanded dwell time between main injection and post injection) were varied, with all other conditions held constant, in order to isolate the effects of the post injection schedule. Results have identified two distinct regimes of post injection schedules that reduce soot emissions.

Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

Dynamic vehicle loads play critical roles for automotive controls including battery management, transmission shift scheduling, distance-to-empty predictions, and various active safety systems. Accurate real-time estimation of vehicle loads such as those due to vehicle mass and road grade can thus improve safety, efficiency, and performance. While several estimation methods have been proposed in literature, none have seen widespread adoption in current vehicle technologies despite their potential to significantly improve automotive controls. To understand and bridge the gap between research development and wider adoption of real-time load estimation, this paper assesses the accuracy and performance of four estimation methods that predict vehicle mass and/or road grade.

It is of considerable interest to developers of military vehicles, in early phases of the concept design process as well as in Analysis of Alternatives (AoA) phase, to quickly predict occupant injury risk due to under-body blast loading. The most common occupant injuries in these extremely short duration events arise out of the very high vertical acceleration of vehicle due to its close proximity to hot high pressure gases from the blast. In a prior study [16], an extensive parametric study was conducted in a systematic manner so as to create look-up tables or automated software tools that decision-makers can use to quickly estimate the different injury responses for both stroking and non-stroking seat systems in terms of a suitable blast load parameter. The primary objective of this paper is to quantitatively evaluate the accuracy of using such a tool in lieu of building a detailed model for simulation and occupant injury assessment.

Ammonia, often present in exhaust gas samples, is a polar molecule gas that interacts with walls of the gas sampling and analysis equipment resulting in delayed instrument response. A set of experiments quantified various materials and process parameters of a heated sample line system for ammonia (NH3) response using a Fourier Transform infrared spectrometer (FTIR). Response attenuation rates are due to mixing and diffusion during transport as well as NH3 wall storage. Mixing/diffusion effects cause attenuation with a time constant 1-10 seconds. Wall storage attenuation has a time constant 10-200 seconds. The effects of sample line diameter and length, line temperature, line material, hydrated versus dry gas, and flow rate were examined. All of these factors are statistically significant to variation of at least one of the time constants. The NH3 storage on the sample system walls was calculated as a function of the experimental test as well.

Driving simulators offer a safe alternative to on-road driving for the evaluation of performance. In addition, simulated drives allow for controlled manipulations of traffic situations producing a more consistent and objective assessment experience and outcome measure of crash risk. Yet, few simulator protocols have been validated for their ability to assess driving performance under conditions that result in actual collisions. This paper presents results from a new Simulated Driving Assessment (SDA), a 35- to-40-minute simulated assessment delivered on a Real-Time® simulator. The SDA was developed to represent typical scenarios in which teens crash, based on analyses from the National Motor Vehicle Crash Causation Survey (NMVCCS). A new metric, failure to brake, was calculated for the 7 potential rear-end scenarios included in the SDA and examined according two constructs: experience and skill.

The U. S. Army Tank Automotive Research, Development and Engineering Center (TARDEC) National Automotive Center (NAC) owns a fleet of ten Hydrogen Hybrid Internal Combustion Engine (H2ICE) vehicles that have been demonstrated in various climates from 2008 through 2010. This included demonstrations in Michigan, Georgia, California and Hawaii. The fleet was consolidated into a single location between July 2009 and April 2010. Between July of 2009 and January of 2011, data collection was completed on the fleet of H2ICE vehicles deployed to Oahu, Hawaii for long-term duration testing. The operation of the H2ICE vehicles in Hawaii utilized standard operation of a non-tactical vehicle at a real-world military installation. The vehicles were fitted with data acquisition equipment to record the operation and performance of the H2ICE vehicles; maintenance and repair data was also recorded for the fleet of vehicles.

Digital human models (DHM) are now widely used to assess worker tasks as part of manufacturing simulation. With current DHM software, the simulation engineer or ergonomist usually makes a manual estimate of the likely worker standing location with respect to the work task. In a small number of cases, the worker standing location is determined through physical testing with one or a few workers. Motion capture technology is sometimes used to aid in quantitative analysis of the resulting posture. Previous research has demonstrated the sensitivity of work task assessment using DHM to the accuracy of the posture prediction. This paper expands on that work by demonstrating the need for a method and model to accurately predict worker standing location. The effect of standing location on work task posture and the resulting assessment is documented through three case studies using the Siemens Jack DHM software.