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Technical Paper

Deep Learning-based Queue-aware Eco-Approach and Departure system for Plug-in Hybrid Electric Bus at signalized intersections: a simulation study

2020-04-14
2020-01-0584
Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where signal phase and timing (SPaT) and geometric intersection description (GID) information are well utilized to guide the vehicles passing through the intersection in a most energy efficient manner. Previous studies by the authors formulated the optimal trajectory planning problem as finding the shortest path on a graph model where the nodes define the reachable states of the host vehicle (e.g., speed, location) at each time step, the links govern the state reachability from previous time step, and the link costs represent the energy consumption rate due to state transition. This method is effective in energy saving, but its computation efficiency can be enhanced by machine learning techniques.
Technical Paper

Dyno-in-the-Loop: An Innovative Hardware-in-the-Loop Development and Testing Platform for Emerging Mobility Technologies

2020-04-14
2020-01-1057
Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems.
Technical Paper

Engine-Aftertreatment in Closed-Loop Modeling for Heavy Duty Truck Emissions Control

2019-04-02
2019-01-0986
An engine-aftertreatment computational model was developed to support in-loop performance simulations of tailpipe emissions and fuel consumption associated with a range of heavy-duty (HD) truck drive cycles. For purposes of this study, the engine-out exhaust dynamics were simulated with a combination of steady-state engine maps and dynamic correction factors that accounted for recent engine operating history. The engine correction factors were approximated as dynamic first-order lags associated with the thermal inertia of the major engine components and the rate at which engine-out exhaust temperature and composition vary as combustion heat is absorbed or lost to the surroundings. The aftertreatment model included catalytic monolith components for diesel exhaust oxidation, particulate filtration, and selective catalytic reduction of nitrogen oxides (NOx) with urea.
Technical Paper

Continuous Particulate Filter State of Health Monitoring Using Radio Frequency Sensing

2018-04-03
2018-01-1260
Reliable means for on-board detection of particulate filter failures or malfunctions are needed to meet diagnostics (OBD) requirements. Detecting these failures, which result in tailpipe particulate matter (PM) emissions exceeding the OBD limit, over all operating conditions is challenging. Current approaches employ differential pressure sensors and downstream PM sensors, in combination with particulate filter and engine-out soot models. These conventional monitors typically operate over narrowly-defined time windows and do not provide a direct measure of the filter’s state of health. In contrast, radio frequency (RF) sensors, which transmit a wireless signal through the filter substrate provide a direct means for interrogating the condition of the filter itself.
Technical Paper

Integration and Validation of a Thermal Energy Storage System for Electric Vehicle Cabin Heating

2017-03-28
2017-01-0183
It is widely recognized in the automotive industry that, in very cold climatic conditions, the driving range of an Electric Vehicle (EV) can be reduced by 50% or more. In an effort to minimize the EV range penalty, a novel thermal energy storage system has been designed to provide cabin heating in EVs and Plug-in Hybrid Electric Vehicles (PHEVs) by using an advanced phase change material (PCM). This system is known as the Electrical PCM-based Thermal Heating System (ePATHS) [1, 2]. When the EV is connected to the electric grid to charge its traction battery, the ePATHS system is also “charged” with thermal energy. The stored heat is subsequently deployed for cabin comfort heating during driving, for example during commuting to and from work. The ePATHS system, especially the PCM heat exchanger component, has gone through substantial redesign in order to meet functionality and commercialization requirements.
Journal Article

On-Board Particulate Filter Failure Prevention and Failure Diagnostics Using Radio Frequency Sensing

2017-03-28
2017-01-0950
The increasing use of diesel and gasoline particulate filters requires advanced on-board diagnostics (OBD) to prevent and detect filter failures and malfunctions. Early detection of upstream (engine-out) malfunctions is paramount to preventing irreversible damage to downstream aftertreatment system components. Such early detection can mitigate the failure of the particulate filter resulting in the escape of emissions exceeding permissible limits and extend the component life. However, despite best efforts at early detection and filter failure prevention, the OBD system must also be able to detect filter failures when they occur. In this study, radio frequency (RF) sensors were used to directly monitor the particulate filter state of health for both gasoline particulate filter (GPF) and diesel particulate filter (DPF) applications.
Journal Article

Electric Drive Transient Behavior Modeling: Comparison of Steady State Map Based Offline Simulation and Hardware-in-the-Loop Testing

2017-03-28
2017-01-1605
Electric drives, whether in battery electric vehicles (BEVs) or various other applications, are an important part of modern transportation. Traditionally, physics-based models based on steady-state mapping of electric drives have been used to evaluate their behavior under transient conditions. Hardware-in-the-Loop (HIL) testing seeks to provide a more accurate representation of a component’s behavior under transient load conditions that are more representative of real world conditions it will operate under, without requiring a full vehicle installation. Oak Ridge National Laboratory (ORNL) developed such a HIL test platform capable of subjecting electric drives to both conventional steady-state test procedures as well as transient experiments such as vehicle drive cycles.
Technical Paper

Thermal Storage System for Electric Vehicle Cabin Heating - Component and System Analysis

2016-04-05
2016-01-0244
Cabin heating of current electric vehicle (EV) designs is typically provided using electrical energy from the traction battery, since waste heat is not available from an engine as in the case of a conventional automobile. In very cold climatic conditions, the power required for space heating of an EV can be of a similar magnitude to that required for propulsion of the vehicle. As a result, its driving range can be reduced very significantly during the winter season, which limits consumer acceptance of EVs and results in increased battery costs to achieve a minimum range while ensuring comfort to the EV driver. To minimize the range penalty associated with EV cabin heating, a novel climate control system that includes thermal energy storage from an advanced phase change material (PCM) has been designed for use in EVs and plug-in hybrid electric vehicles (PHEVs).
Journal Article

PHEV Cold Start Emissions Management

2013-04-08
2013-01-0358
Plug-in hybrid electric vehicles (PHEV) operate predominantly as electric vehicles (EV) with intermittent assist from the engine. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current conventional vehicles, the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts. ORNL, in collaboration with the University of Tennessee, developed an Engine-In-the-Loop (EIL) test platform to investigate cold start emissions on a 2.0l Gasoline Turbocharged Direct Injection (GTDI) Ecotec engine coupled to a virtual series hybrid electric vehicle.
Technical Paper

In-Cylinder Fuel Blending of Gasoline/Diesel for Improved Efficiency and Lowest Possible Emissions on a Multi-Cylinder Light-Duty Diesel Engine

2010-10-25
2010-01-2206
In-cylinder fuel blending of gasoline with diesel fuel is investigated on a multi-cylinder light-duty diesel engine as a strategy to control in-cylinder fuel reactivity for improved efficiency and lowest possible emissions. This approach was developed and demonstrated at the University of Wisconsin through modeling and single-cylinder engine experiments. The objective of this study is to better understand the potential and challenges of this method on a multi-cylinder engine. More specifically, the effect of cylinder-to-cylinder imbalances and in-cylinder charge motion as well as the potential limitations imposed by real-world turbo-machinery were investigated on a 1.9-liter four-cylinder engine. This investigation focused on one engine condition, 2300 rpm, 5.5 bar net mean effective pressure (NMEP). Gasoline was introduced with a port-fuel-injection system.
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