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Power split in Fuel Cell Hybrid Electric Vehicles (FCHEVs) has been controlled using different strategies ranging from rule-based to optimal control. Dynamic Programming (DP) and Model Predictive Control (MPC) are two common optimal control strategies used in optimization of the power split in FCHEVs with a trade-off between global optimality of the solution and online implementation of the controller. In this paper, both control strategies are developed and tested on a FC/battery vehicle model, and the results are compared in terms of total energy consumption. In addition, the effects of the MPC prediction horizon length on the controller performance are studied. Results show that by using the DP strategy, up to 12% less total energy consumption is achieved compared to MPC for a charge sustaining mode in the Urban Dynamometer Driving Schedule (UDDS) drive cycle.

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Speed Harmonization, Eco Approach and Departure and in-situ vehicle parameter characterization.

The successful implementation of a clean, quiet, high-performance four-stroke motorcycle engine into an existing snowmobile chassis has been achieved. The snowmobile is easy to start, easy to drive, and environmentally friendly. The following paper describes the conversion process in detail with actual dynamometer and field test data. The vehicle meets the proposed 2010 EPA snowmobile emissions regulations and is quieter than a stock snowmobile. The snowmobile not only addresses environmental concerns, it is economical as well, with an approximate cost of $5874.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

Computational fluid dynamics of gas-fueled large-bore spark ignition engines with pre-chamber ignition can speed up the design process of these engines provided that 1) the reliability of the results is not affected by poor meshing and 2) the time cost of the meshing process does not negatively compensate for the advantages of running a computer simulation. In this work a flame propagation model that runs with arbitrary hybrid meshes was developed and coupled with the KIVA4-MHI CFD solver, in order to address these aims. The solver follows the G-Equation level-set method for turbulent flame propagation by Tan and Reitz, and employs improved numerics to handle meshes featuring different cell types such as hexahedra, tetrahedra, square pyramids and triangular prisms. Detailed reaction kinetics from the SpeedCHEM solver are used to compute the non-equilibrium composition evolution downstream and upstream of the flame surface, where chemical equilibrium is instead assumed.

Vehicle thermal protection is an important aspect of the overall vehicle development process. It involves optimizing the exhaust system routing and designing heat shields to protect various components that are in near proximity to the exhaust system. Reduced time to market necessitates an efficient process for thermal protection development. A robust procedure that utilizes state of the art CFD simulation techniques proactively during the design phase is described. Simulation allows for early detection of thermal issues and development of countermeasures several months before prototype vehicles are built. Physical testing is only used to verify the thermal protection package rather than to develop heat shields. The new procedure reduces the number of physical tests and results in a robust, efficient methodology.

To address the need of increasing fuel economy requirements, automotive Original Equipment Manufacturers (OEMs) are increasing the number of turbocharged engines in their powertrain line-ups. The turbine-driven technology uses a forced induction device, which increases engine performance by increasing the density of the air charge being drawn into the cylinder. Denser air allows more fuel to be introduced into the combustion chamber, thus increasing engine performance. During the inlet air compression process, the air is heated to temperatures that can result in pre-ignition resulting and reduced engine functionality. The introduction of the charge air cooler (CAC) is therefore, necessary to extract heat created during the compression process. The present research describes the physics and develops the optimized simulation method that defines the process and gives insight into the development of CACs.

Homogenous charge compression ignition (HCCI) engines offer a great potential in achieving high thermal efficiency and extremely low NOx at the same time. However, control of combustion phasing over a wide speed and load range has been a challenge, especially during transient operations. This paper describes work conducted at the National Vehicle and Fuel Emissions Laboratory, which explores the potential use of an HCCI engine as a power plant for a hybrid vehicle. A four-cylinder, 1.9 L commercial diesel engine was modified to operate with port-injected regular grade gasoline in HCCI mode. The combustion phasing is controlled by a combination of boost, EGR and thermal management as a function of engine speed and load. As a stand-alone unit, the engine has demonstrated a wide operation range with efficiency like that of a diesel engine and NOx below 0.2 g/kWh. At room temperature, the engine starts in SI mode and then transitions to HCCI in about 25 seconds.

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. The engine’s limited operating range can be improved by taking advantage of electrification in the powertrain. In this study, a multi-mode LTC-SI engine is integrated with a parallel hybrid electric configuration, where the engine operation modes include Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations.

In light-duty vehicles, there is a fundamental trade-off between fuel consumption and acceleration performance, if other vehicle attributes are held fixed. Earlier econometric studies have estimated the magnitude of this trade-off - the elasticity of fuel consumption with respect to performance - based on historical vehicle data. The majority of these studies assume, a priori, that elasticity is constant across the model year, vehicle power, and technology content. However, there is evidence that the content in the underlying powertrain technology packages is shifting in a way that reduces the value of the elasticity of fuel consumption with respect to performance, such that historical trends would not predict future behavior. This paper presents an alternative strategy for studying vehicle fuel consumption versus performance trade-off.

Increasing regulatory demand to reduce CO2 emissions has led to an industry focus on electrified vehicles while limiting the development of conventional internal combustion engine (ICE) and hybrid powertrains. Hybrid electric vehicle (HEV) powertrains rely on conventional SI mode IC engines that are optimized for a narrow operating range. Advanced combustion strategies such as Gasoline Compression Ignition (GCI) have been demonstrated by several others including the authors to improve brake thermal efficiency compared to both gasoline SI and Diesel CI modes. Soot and NOx emissions are also reduced significantly by using gasoline instead of diesel in GCI engines due to differences in composition, fuel properties, and reactivity. In this work, an HEV system was proposed utilizing a multi-mode GCI based ICE combined with a HEV components (e-motor, battery, and invertor).

Hydrogen is widely considered a promising fuel for future transportation applications for both, internal combustion engines and fuel cells. Due to their advanced stage of development and immediate availability hydrogen combustion engines could act as a bridging technology towards a wide-spread hydrogen infrastructure. Although fuel cell vehicles are expected to surpass hydrogen combustion engine vehicles in terms of efficiency, the difference in efficiency might not be as significant as widely anticipated [1]. Hydrogen combustion engines have been shown capable of achieving efficiencies of up to 45 % [2]. One of the remaining challenges is the reduction of nitric oxide emissions while achieving peak engine efficiencies. This paper summarizes research work performed on a single-cylinder hydrogen direct injection engine at Argonne National Laboratory.

This paper describes a multi-sensor data set, suitable for testing algorithms to detect and track pedestrians and cyclists, with an autonomous vehicle’s sensor suite. The data set can be used to evaluate the benefit of fused sensing algorithms, and provides ground truth trajectories of pedestrians, cyclists, and other vehicles for objective evaluation of track accuracy. One of the principal bottlenecks for sensing and perception algorithm development is the ability to evaluate tracking algorithms against ground truth data. By ground truth we mean independent knowledge of the position, size, speed, heading, and class of objects of interest in complex operational environments. Our goal was to execute a data collection campaign at an urban test track in which trajectories of moving objects of interest are measured with auxiliary instrumentation, in conjunction with several autonomous vehicles (AV) with a full sensor suite of radar, lidar, and cameras.

Mode management is an essential part of the design process for NVH performance. System resonances must be sufficiently separated to minimize interaction from source inputs and each other [1]. Such resonances are typically determined through experimental modal testing conducted in a lab environment under controlled and repeatable conditions. Global vehicle and suspension system response demonstrate soft nonlinear behavior, however. Their resonant frequencies may thus decrease under on-road input not reproducible in a lab environment. Subsequently, mode management charts derived from lab testing may not be representative of the vehicle's on-road dynamic response. This paper presents modal model determination methodologies, and examines suspension system and vehicle global dynamic response under lab modal test and operating conditions. Vehicle suspension modes measured under static and dynamic (rolling) conditions will be compared.

The growing competition of the automotive market makes more and more necessary the reduction of development time and consequently, the increase of the capacity to quickly respond to the launching of the competitors. One of the most costly phases on the vehicle development process is the field durability test, both in function of the number of prototypes employed and the time needed to its execution. More and more diffused, the fatigue life prediction methods have played an important part in the durability analysis via CAE. Nevertheless, in order they can be reliable and really being able to reduce the development time and cost, they need to be provided with load cases that can accurately represent the field durability tests. This work presents a CAE approach used for light trucks in order to get a reasonable understanding of component durability behavior due to payload increase. In general, road load data is not available for a new payload condition.

By almost any definition, technology has penetrated the U.S. light-duty vehicle fleet significantly in conjunction with the increased stringency of fuel economy and GHG emissions regulations. The physical presence of advanced technology components provides one indication of the efforts taken to reduce emissions, but that alone does not provide a complete measure of the benefits of a particular technology application. Differences in the design of components, the materials used, the presence of other technologies, and the calibration of controls can impact the performance of technologies in any particular implementation. The effectiveness of a technology for reducing emissions will also be influenced by the extent to which the technologies are applied towards changes in vehicle operating characteristics such as improved acceleration, or customer features that may offset mass reduction from the use of lightweight materials.

A three phase catalytic mathematical model was developed for analysis and optimization of the volatile reactor assembly (VRA) used on International Space Station (ISS) Water Processor. The Langmuir-Hinshelwood Hougen-Watson (L-H) expression was used to describe the surface reaction rate. Small column experiments were used to determine the L-H rate parameters. The test components used in the experiments were acetic acid, acetone, ethanol, 1-propanol, 2-propanol and propionic acid. These compounds are the most prevalent ones found in the influent to the VRA reactor. The VRA model was able to predict performance of small column data and experimental data from the VRA flight experiment.

The 45RFE is a new generation electronically controlled rear wheel drive automatic transmission. It employs real-time feedback, closed-loop modulation of shift functions to achieve outstanding shift quality and to meet demanding durability goals. It uses no shift valves; all friction element applications are effected with high-flow electro-hydraulic solenoid valves. A unique gear train arrangement of three planetary carriers allows all sun gears and annulus gears to have the same number of teeth respectively and use a common pinion gear in all carriers, resulting in significant manufacturing simplification. The three-planetary system is designed for four forward ratios of 3.00, 1.67, 1.00 and 0.75 and one reverse gear ratio equal to the low gear ratio. A fifth ratio of 1.50 is used only in certain kick-down shift sequences for highway passing. A sixth forward ratio, an additional overdrive ratio of 0.67, is available in the hardware.

The Society of Automotive Engineers' (SAE) Clean Snowmobile Challenge (CSC) 2005 was hosted by Michigan Technological University (MTU) in Houghton, Michigan during the week of March 14, 2005. The events were held at the Keweenaw Research Center (KRC), a research arm of MTU. With energy prices on the rise and pollution regulations tightening, efficient and clean modes of transportation are becoming more important. The emissions, noise, and fuel economy events are all significant portions of this year's competition. MTU has hosted the Clean Snowmobile Challenge for the past three years, during which there have been numerous modifications to the events and logistics of the competition to make the experience as beneficial as possible. This paper looks at not only the results from the 2005 competition, but also discusses trends and common design strategies that the winning teams from each year possess.

The U.S. Environmental Protection Agency, U.S. Department of Transportation's National Highway and Traffic Safety Administration, and the California Air Resources Board have recently released proposed new regulations for greenhouse gas emissions and fuel economy for light-duty vehicles and trucks in model years 2017-2025. These proposed regulations intend to significantly reduce greenhouse gas emissions and increase fleet fuel economy from current levels. At the fleet level, these rules the proposed regulations represent a 50% reduction in greenhouse gas emissions by new vehicles in 2025 compared to current fleet levels. At the same time, global growth, especially in developing economies, should continue to drive demand for crude oil and may lead to further fuel price increases. Both of these trends will therefore require light duty vehicles (LDV) to significantly improve their greenhouse gas emissions over the next 5-15 years to meet regulatory requirements and customer demand.