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Conventional methods of vehicle operation for Plug-in Hybrid Vehicles first discharge the battery to a minimum State of Charge (SOC) before switching to charge sustaining operation. This is very demanding on the battery, maximizing the number of trips ending with a depleted battery and maximizing the distance driven on a depleted battery over the vehicle's life. Several methods have been proposed to reduce the number of trips ending with a deeply discharged battery and also eliminate the need for extended driving on a depleted battery. An optimum SOC can be maintained for long battery life before discharging the battery so that the vehicle reaches an electric plug-in destination just as the battery reaches the minimum operating SOC. These “Just-in-Time” methods provide maximum effective battery life while getting virtually the same electricity from the grid.

The question of whether increasing the fuel economy of light-duty natural gas fueled vehicles can improve their economic competitiveness in the U.S. market, and help the US Department of Energy meet stated goals for such vehicles is explored. Key trade-offs concerning costs, exhaust emissions and other issues are presented for a number of possible advanced engine designs. Projections of fuel economy improvements for a wide range of lean-burn engine technologies have been developed. It appears that compression ignition technologies can give the best potential fuel economy, but are less competitive for light-duty vehicles due to high engine cost. Lean-burn spark ignition technologies are more applicable to light-duty vehicles due to lower overall cost. Meeting Ultra-Low Emission Vehicle standards with efficient lean-burn natural gas engines is a key challenge.

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.

A GT-SUITE vehicle-aftertreatment model has been developed to examine the cold-start emissions reduction capabilities of a Vacuum Insulated Catalytic Converter (VICC). This converter features a thermal management system to maintain the catalyst monolith above its light-off temperature between trips so that most of a vehicle’s cold-start exhaust emissions are avoided. The VICC thermal management system uses vacuum insulation around the monoliths. To further boost its heat retention capacity, a metal phase-change material (PCM) is packaged between the monoliths and vacuum insulation. To prevent overheating of the converter during periods of long, heavy engine use, a few grams of metal hydride charged with hydrogen are attached to the hot side of the vacuum insulation. The GT-SUITE model successfully incorporated the transient heat transfer effects of the PCM using the effective heat capacity method.

There is a pressing need to develop accurate and robust approaches for predicting vehicle speed to enhance fuel economy/energy efficiency, drivability and safety of automotive vehicles. This paper details outcomes of research into various methods for the prediction of vehicle velocity. The focus is on short-term predictions over 1 to 10 second prediction horizon. Such short-term predictions can be integrated into a hybrid electric vehicle energy management strategy and have the potential to improve HEV energy efficiency. Several deterministic and stochastic models are considered in this paper for prediction of future vehicle velocity. Deterministic models include an Auto-Regressive Moving Average (ARMA) model, a Nonlinear Auto-Regressive with eXternal input (NARX) shallow neural network and a Long Short-Term Memory (LSTM) deep neural network. Stochastic models include a Markov Chain (MC) model and a Conditional Linear Gaussian (CLG) model.

This paper proposes a vehicle performance/safety method using combined active steering and differential braking to achieve yaw stability and rollover avoidance. The advantages and disadvantages of active steering and differential braking control methods are identified under a variety of input signals, such as J-turn, sinusoidal, and fishhook inputs by using the implemented linear 4 DOF model. Also, the nonlinear model of the vehicle is evaluated and verified through individual and integrated controller. Each controller gives the correction steering angle and correction moment to the simplified steering and braking actuators. The integrated active steering and differential braking control are shown to be most efficient in achieving yaw stability and rollover avoidance, while active steering and differential braking control has been shown to improve the vehicle performance and safety only in yaw stability and rollover avoidance, respectively.

Vital to the effectiveness of simulation-based design is having a model of known quality of the system being designed. The purpose of this paper is to validate a simplified dynamic model of an FMTV (Family of Medium Tactical Vehicles) for a range of system parameters using a previously developed technique for determining model robustness and accuracy within a design space. The literature provides an algorithm called AVASIM (Accuracy and Validity Algorithm for Simulation) for assessing model validity systematically and quantitatively. AVASIM assess the validity of a model based on a specific input and set of system parameters. The literature also defines a procedure for evaluating the robustness and accuracy of a model with respect to input and system parameter variations based on the AVASIM algorithm.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

A major driving force for change in light-duty vehicle design and technology is the National Highway Traffic Safety Administration (NHTSA) and the U.S. Environmental Protection Agency (EPA) joint final rules concerning Corporate Average Fuel Economy (CAFE) and greenhouse gas (GHG) emissions for model years 2017 (MY17) through 2025 (MY25) passenger cars and light trucks. The chief goal of this current study is to compare the already rapid pace of fuel economy improvement and technological change over the previous decade to the required rate of change to meet regulations over the next decade. EPA and NHTSA comparisons of the model year 2005 (MY05) US light-duty vehicle fleet to the model year 2015 (MY15) fleet shows improved fuel economy (FE) of approximately 26% using the same FE estimating method mandated for CAFE regulations. Future predictions by EPA and NHTSA concerning ensemble fleet fuel economy are examined as an indicator of required vehicle rate-of-change.

Growing environmental concerns and stringent vehicle emissions regulations has created an urge in the automotive industry to move towards electrified propulsion systems. Reducing and eliminating the emission from public transportation vehicles plays a major role in contributing towards lowering the emission level. Battery electric buses are regarded as a type of promising green mass transportation as they provide the advantage of less greenhouse gas emissions per passenger. However, the electric bus faces a problem of limited range and is not able to drive throughout the day without being recharged. This research studies a public bus transit system example which servicing the city of Ann Arbor in Michigan and investigates the impact of different electrification levels on the final CO2 reduction. Utilizing models of a conventional diesel, hybrid electric, and battery electric bus, the CO2 emission for each type of transportation bus is estimated.

In support of the Federal Motor Carrier Safety Administration’s (FMCSA’s) ongoing interest in connected and automated commercial vehicles, this report summarizes analyses conducted to quantify variability in stopping distance tests conducted on commercial truck tractors. The data used were retrieved from tests performed under the controlled conditions specified for FMVSS-121 air brake system compliance testing. The report explores factors affecting the variability of the service brake stopping distance as defined by 49 CFR 571.121, S5.3.1 Stopping Distance—trucks and buses stopping distance. Variables examined in this analysis include brake type, weight, wheelbase, and tractor antilock braking system (ABS). This analysis uses existing test data collected between 2010 and 2019. Several of the examined parameters affected both tractor stopping distance and stopping distance variability.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

The US EPA and the California Air Resources Board (CARB) require electric vehicle range to be determined according to the Society of Automotive Engineers (SAE) surface vehicle recommended practice J1634 - Battery Electric Vehicle Energy Consumption and Range Test Procedure. In the 2021 revision of the SAE J1634, the Short Multi-Cycle Test (SMCT) was introduced. The proposed testing protocol eases the chassis dynamometer test burden by performing a 2.1-hour drive cycle on the dynamometer, followed by discharging the remaining battery energy into a battery cycler to determine the Useable Battery Energy (UBE). Opting for a cycler-based discharge is financially advantageous due to the extended operating time required to fully deplete a 70-100kWh battery commonly found in Battery Electric Vehicles (BEVs).

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?

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?

The emission of excessive quantities of NOx when the automobile air conditioner is turned on has received a fair amount of attention in recent years. Since NOx is a smog precursor, it is important to understand the reasons for this jump in emissions especially on hot sunny days when air conditioner usage is at a maximum. A simple thermodynamic model is used to demonstrate how the torque from a typical air conditioner compressor is mainly related to the ambient temperature. The compressor's on-off cycling patterns are also characterized. Since the compressor significantly loads the engine, it affects fuel economy and emissions. The key independent variable that we employ to represent engine load is fuel rate. The correlations between engine-out NOx emissions and fuel rate are shown for a number of light duty vehicles and trucks. From these, a physical model for engine-out NOx emissions (with and without air conditioning) is presented.

Developing a complete understanding of the structure and behavior of the near-wall region (NWR) in reciprocating, internal combustion (IC) engines and of its interaction with the core flow is needed to support the implementation of advanced combustion and engine operation strategies, as well as predictive computational models. The NWR in IC engines is fundamentally different from the canonical steady-state turbulent boundary layers (BL), whose structure, similarity and dynamics have been thoroughly documented in the technical literature. Motivated by this need, this paper presents results from the analysis of two-component velocity data measured with particle image velocimetry near the head of a single-cylinder, optical engine. The interaction between the NWR and the core flow was quantified via statistical moments and two-point velocity correlations, determined at multiple distances from the wall and piston positions.

Naturally aspirated HCCI operation is typically limited to medium load operation (∼ 5 bar net IMEP) by excessive pressure rise rate. Boosting can provide the means to extend the HCCI range to higher loads. Recently, it has been shown that HCCI can achieve loads of up to 16.3 bar of gross IMEP by boosting the intake pressure to more than 3 bar, using externally driven compressors. However, investigating HCCI performance over the entire speed-load range with real turbocharger systems still remains an open topic for research. A 1 - D simulation of a 4 - cylinder 2.0 liter engine model operated in HCCI mode was used to match it with off-the-shelf turbocharger systems. The engine and turbocharger system was simulated to identify maximum load limits over a range of engine speeds. Low exhaust enthalpy due to the low temperatures that are characteristic of HCCI combustion caused increased back-pressure and high pumping losses and demanded the use of a small and more efficient turbocharger.

The friction and wear characteristics of three commercially-available, electrolytic coatings for aluminum engine cylinder bores were compared to those of cast iron liners. A Ni/SiC electrocomposite, a hard anodized treatment, and a Plasma Electrolytic Oxidation (PEO) coating were investigated. ASTM standard test method G133-95, non-firing test method, for linearly reciprocating sliding wear was modified to use segments of piston rings and cylinder liners. Tests were conducted using Mr. Goodwrench™ 5W30 as a lubricant at room temperature. The normal force was 150N, the reciprocating frequency was 15Hz, the stroke length was 8mm, and the test duration was 60 minutes. Kinetic friction coefficients ranged from 0.1 to 0.22, typical of boundary lubrication. The Ni/SiC and cast iron samples exhibited the lowest friction. The wear resistance of the Ni/SiC coating was superior to that of cast iron.