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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 advent of shared-mobility, vehicle electrification, connected vehicle technology, and vehicle automation. These technologies will not only affect our safety and mobility, but also our energy consumption, air pollution, and climate change. As a result, it is of unprecedented importance to understand the overall system impacts, as a result of introducing these emerging technologies and concepts. However, existing modeling tools are not able to properly capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. For example, it is quite challenging to calibrate state-of-the-art microscopic traffic simulators to properly model the behavior of automated vehicles or to address potential cyber-security issues in a Connected Vehicle (CV) environment.
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

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

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.
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).
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