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Heat exchangers with Phase Change Material (PCM) are finding more energy storage applications for both Internal Combustion Engine Vehicles (ICEVs) and Electric Vehicles (EVs). These applications include cold storage evaporators for stop-start cars, thermal storage system for EV cabin heating and cooling, and other Heating, Ventilation, and Air Conditioning (HVAC) and Power Train Cooling (PTC) peak load shaving applications. The energy stored in a PCM heat exchanger is typically charged/discharged using refrigerant, coolant, or air, depending on the system design of different applications. Due to the low thermal conductivity of state-of-art PCM, the PCM heat exchangers generally rely on aluminum fins to enhance the speed of charging and discharging of the stored energy. Different fin shape, height and density will result in different PCM freezing/melting rate.

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.

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency.

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.

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.

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).

In-cylinder blending of gasoline and diesel to achieve reactivity controlled compression ignition (RCCI) has been shown to reduce NOX and PM emissions while maintaining or improving brake thermal efficiency as compared to conventional diesel combustion (CDC). The RCCI concept has an advantage over many advanced combustion strategies in that the fuel reactivity can be tailored to the engine speed and load allowing stable low-temperature combustion to be extended over more of the light-duty drive cycle load range. However, the current range of the experimental RCCI engine map investigated here does not allow for RCCI operation over the entirety of some drive cycles and may require a multi-mode strategy where the engine switches from RCCI to CDC when speed and load fall outside of the RCCI range.

Spot, or distributed, cooling and heating is an energy efficient way of delivering comfort to an occupant in the car. This paper describes an approach to distributed cooling in the vehicle. A two passenger CFD model of an SUV cabin was developed to obtain the solar and convective thermal loads on the vehicle, characterize the interior thermal environment and accurately evaluate the fluid-thermal environment around the occupants. The present paper focuses on the design and CFD analysis of the energy efficient HVAC system with spot cooling. The CFD model was validated with wind tunnel data for its overall accuracy. A baseline system with conventional HVAC air was first analyzed at mid and high ambient conditions. The airflow and cooling delivered to the driver and the passenger was calculated. Subsequently, spot cooling was analyzed in conjunction with a much lower conventional HVAC airflow.

We describe novel results of ongoing research at 3DX-RAY Ltd and Oak Ridge National Laboratory using new, commercially available, nondestructive x-ray techniques to make engineering measurements of diesel particulate filters (DPF). Nondestructive x-ray imaging and data-analysis techniques were developed to detect and visualize the small density changes corresponding to the addition of substances such as soot and ash to DPF monoliths. The usefulness of this technique was explored through the analysis of field-aged samples, accelerated-aged samples, and the synthetic addition of ash and soot to clean DPF samples. We demonstrate the ability to visualize and measure flaws in substrates and quantify the distribution of ash and soot within the DPF. We also show that the technology is sensitive enough for evaluations of soot and ash distribution and thermal damage without removing the DPF from its metal casing.

A computational study using multi-dimensional CFD modeling was performed to investigate the effects of physical properties on diesel engine combustion characteristics with bio-diesel fuels. Properties of typical bio-diesel fuels that were either calculated or measured are used in the study and the simulation results are compared with those of conventional diesel fuels. The sensitivity of the computational results to individual physical properties is also investigated, and the results provide information about the desirable characteristics of the blended fuels. The properties considered in the study include liquid density, vapor pressure, surface tension, liquid viscosity, liquid thermal conductivity, liquid specific heat, latent heat, vapor specific heat, vapor diffusion coefficient, vapor viscosity and vapor thermal conductivity. The results show significant effects of the fuel physical properties on ignition delay and burning rates at various engine operating conditions.

We report experimental observations of cyclic combustion variability during the transition between propagating flame combustion and homogeneous charge compression ignition (HCCI) in a single-cylinder, stoichiometrically fueled, spark-assisted gasoline engine. The level of internal EGR was controlled with variable valve actuation (VVA), and HCCI combustion was achieved at high levels of internal EGR using the VVA system. Spark-ignition was used for conventional combustion and was optionally available during HCCI. The transition region between purely propagating combustion and HCCI was mapped at multiple engine speeds and loads by incrementally adjusting the internal EGR level and capturing data for 2800 sequential cycles. These measurements revealed a complex sequence of high COV, cyclic combustion variations when operating between the propagating flame and HCCI limits.