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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Electric Vehicles, Biofuels, and Transitions in Transportation Energy

The decarbonization of power sources for light duty transportation is moving towards the use of renewable energies, biofuels, and electric vehicles. Currently there are three major light transportation fuels: gasoline (US gasoline is 98% E10), biofuels (E85), and electricity. Currently fuel ethanol in biofuels is obtained principally from corn in the US and sugarcane in Brazil with a small amount from cellulose. The transition to alternative fuels has been occurring since 1980, although light transportation still relies on gasoline. The rise of battery electric vehicles (BEV) is now beginning to offset internal combustion engines (ICE), while recent advances in biotechnology and integrated plant design have enhanced the economic feasibility of cellulosic biofuels. This paper explores the potential role of these fuels in reducing the carbon footprint associated with light transportation. We compare three different cases: Gasoline, E85 and full electric based on the Argonne AFLEET tool to calculate generation of greenhouse gases due to manufacturing and operation of BEV and ICE cars, respectively, in the US assuming an average useful automobile life of 200,000 miles or 12 years. We concluded that significant liquid fuel use (both renewable and non-renewable) will continue for the next 50 years. Here, renewable liquid fuels have an important role to play as the transportation sector transitions from petroleum to other energy sources.

09 BIOMASS FUELS↗
DOE OSTI2023

DP-Based Optimization of BESS to Substitute RICE Reserves for Improved Economic Benefits

In regions with a significant proportion of renewable energy sources, reciprocating internal combustion engines (RICE or IC) are frequently employed as grid reserve solutions. This study suggests a scheduling technique for battery energy storage systems (BESS) that utilizes dynamic programming (DP) to replace IC units, adding versatility to the total reserve capacity and resulting in higher BESS earnings compared to traditional scheduling approaches. The capacity sweeping approach to optimize BESS’s revenue sometimes leads to operation which is counter intuitive to conventional battery sizing approaches.

25 ENERGY STORAGE↗
DOE OSTI2023

Model Based Validation of Intelligent Powertrain Strategies for Connected and Automated Vehicles

Systems incorporating Vehicle to Everything (V2X) and conventional cellular based communication in vehicles can significantly help improve energy consumption via a combination of intelligent powertrain control strategies, smarter routing algorithms and driving in such a way as to minimize fuel economy and the emission of carbon dioxide, known as "eco-driving." In projects led by the Southwest Research Institute (SwRI), large-scale traffic simulations are created to model real-world scenarios with dynamic behavior that is reactive to imposed changes. Coupled with high fidelity powertrain models, the closed loop framework enables research and development of such Connected and Automated Vehicle (CAV) enabled technologies at scale. This paper will discuss a traffic system simulation environment that was built based on the High Street urban corridor in Columbus, Ohio. Eco-driving strategies were tested at scale on a variety of powertrain platforms – internal combustion engines, hybrid electric and fully electric vehicles. Furthermore, the paper will focus on hybrid electric powertrain modeling along with details on how the powertrain model was leveraged to develop a sophisticated clustering scheme to help down-select speed traces from large scale simulation studies for validation on vehicle dynamometer. Nominal energy consumption improvement around 12% was observed with good match between simulation studies and vehicle testing.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Trends and Heterogeneity in Electric Vehicle Economics: A French Case Study

The total cost of ownership of electric vehicles depends on both the behavior and economic context of the driver as well as the level of government support offered for electrified vehicles. In this work, we study both of these effects as they shift private vehicle costs and emissions benefits, focused on a case study of the faculty and staff commuters to the Cite Scientifique campus of the University of Lille. The modeling approach considers the net costs and emissions from switching each driver from an internal combustion engine vehicle to three electrified options: a hybrid electric, a plug-in hybrid, and a pure battery electric. Increasing government support or technology-induced cost declines of electric vehicles both improve the economics of electrified options and shift more drivers towards battery electrics as the preferred alternative to thermal vehicles.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2020

Circular economy framework for automobiles: Closing energy and material loops

Abstract Corporations, including automotive manufacturers, are increasingly exploring extended circular economy strategies as a means to enhance the sustainability of their products. The circular economy paradigm focuses on reducing nonrenewable materials and energy, promoting renewable feedstocks and energy, and keeping products/materials in use across the life cycle of a system. As such, life cycle environmental burdens associated with vehicle manufacturing, use, and disposal could potentially be reduced through circular economy strategies; however, no such comprehensive circular economy framework currently exists for the automotive industry. We develop the first circular economy schematic of automobiles, derived from the Ellen MacArthur Foundation's framework. Further, we characterize the current automotive circular economy using metrics of renewable energy and recycled materials. Specifically, for current U.S. average sedans, we find that internal combustion engine vehicles (ICEVs) use ∼6% renewable life cycle primary energy and 27% recycled materials; for battery electric vehicles (BEVs), these measures are ∼8% and 21%, respectively. On a vehicle‐miles‐traveled basis, BEVs use ∼47% less nonrenewable life cycle primary energy than ICEVs, highlighting the importance of electrification as a strategy for automotive manufacturers to reduce environmental burdens. Our proposed circular economy framework is then applied to Ford Motor Company's sustainability programs and initiatives as an example. This schematic aims to provide a starting point for the automotive industry to operationalize circular economy strategies, the application of which could advance its overall sustainability performance.

Aguilar Esteva, Laura C.↗
DOE OSTI2024

Well‐to‐wheels analysis of greenhouse gas emissions for passenger vehicles in Middle East and North Africa

Battery electric vehicles (BEVs) are widely considered a pathway to achieve low carbon mobility. BEVs emit zero emissions from the tailpipe, but their life cycle carbon reduction compared to gasoline vehicles varies based on primary energy sources, electricity generation, and use efficiency. The Middle East and North Africa (MENA) region is an area rich in fossil fuels, meriting a detailed comparison between the emissions from BEV and other powertrains. We developed a MENA‐specific life cycle model that estimates well‐to‐wheel (WTW) greenhouse gas (GHG) emissions from passenger transport with internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), plug‐in hybrid electric vehicles, and BEVs. MENA's average WTW GHG emissions for all supply chain steps including combustion emissions from vehicle operation are 767 g/kWh and 84 g CO 2 eq/MJ for electricity and gasoline, respectively, but are highly variable due to heterogeneity in upstream supply chains. The use of hybrid gasoline ICEVs provides the largest emission reduction opportunity for existing vehicle fleets in 9 of the 16 MENA countries. For these nine countries, replacing gasoline ICEVs with HEVs could, on average, reduce country‐level life cycle GHG emissions by 47%. There is a similar emission reduction opportunity for 14 of the 16 MENA countries when normalizing vehicle efficiencies irrespective of the powertrain shares and other trends in existing vehicle fleets. Future scenario analysis shows that BEVs would have the lowest WTW GHG emissions among all powertrains in most MENA countries only if significantly reduced electricity transmission losses and cleaner grid mix are realized, although a high cost of infrastructure developments is expected.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Modeling Nanosecond-Pulsed Spark Discharge and Flame Kernel Evolution

Dilute combustion, either using exhaust gas recirculation or with excess air, is considered a promising strategy to improve the thermal efficiency of internal combustion engines. However, the dilute air-fuel mixture, especially under intensified turbulence and high-pressure conditions, poses significant challenges for ignitability and combustion stability, which may limit the attainable efficiency benefits. In-depth knowledge of the flame kernel evolution to stabilize ignition and combustion in a challenging environment is crucial for effective engine development and optimization. To date, a comprehensive understanding of ignition processes that result in the development of fully predictive ignition models usable by the automotive industry does not yet exist. Spark-ignition consists of a wide range of physics that includes electrical discharge, plasma evolution, joule-heating of gas, and flame kernel initiation and growth into a self-sustainable flame. In this study, an advanced approach is proposed to model spark-ignition energy deposition and flame kernel growth. To decouple the flame kernel growth from the electrical discharge, a nanosecond-pulsed high-voltage discharge is used to trigger spark-ignition in an optically accessible small ignition test vessel with a quiescent mixture of air and methane. Initial conditions for the flame kernel, including its thermodynamic state and species composition, are derived from a plasma-chemical equilibrium calculation. The geometric shape and dimension of the kernel are characterized using a multi-dimensional thermal plasma solver. Here, the proposed modeling approach is evaluated using a high-fidelity computational fluid dynamics procedure to compare the simulated flame kernel evolution against flame boundaries from companion Schlieren images.

42 ENGINEERING↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2023

An Experimental Comparison of Cyclic Variations in Diesel–Natural Gas and POMDME–Natural Gas Dual Fuel Combustion

Abstract Cyclic variations in internal combustion engines are caused by various factors, including combustion mixture stratification, in-cylinder flows, local fluctuations in air-fuel ratio, etc. Cyclic variations have a profound impact on engine performance and emissions. In this study, cyclic variations in dual fuel combustion are analyzed, comparing diesel–natural gas (NG) and polyoxymethylene dimethyl ether (POMDME)-NG dual fuel combustion. Cyclic variability was initially quantified using the coefficient of variation of gross indicated mean effective pressure (IMEPg) computed from experimental cylinder pressure data. The cases analyzed in this study had a coefficient of variation (COV) of IMEPg greater than or around 5%, which was the lower limit of onset of instability for this engine. Experiments were performed at two fixed start of injection (SOI) of high-cetane fuel: 310 CAD and 350 CAD. For all experiments, a constant load of 5 bar IMEPg was maintained, and the intake boost pressure and rail pressure were fixed at 1.5 bar and 500 bar, respectively. For each case, 1000 cycles of cylinder pressure data were recorded, filtered, and processed using an in-house heat release analysis code for each cycle. A comparison between individual cycles and the “ensemble averaged cycle” was made for both diesel–NG and POMDME–NG combustion. For the early SOI of 310 CAD, the peak cylinder pressure fluctuations of individual cycle were found to be ± 15 bar for both fuel combinations, compared to the ensemble averaged cycle, and < 1/10th of the cycles had an IMEPg lower than 0.05 bar of the ensemble averaged cycle. However, the peak pressure fluctuations were found to be lower for POMDME–NG (±3 bar) than diesel–NG dual fuel combustion at 350 CAD SOI, indicating lower cyclic variations. The higher reactivity of POMDME helped reduce fluctuations in combustion phasing at the retarded SOI. The presence of cycles of deterioration and cycles of recovery were also observed with diesel–NG combustion for 310 CAD SOI, and the scatter in the IMEPg return map was similar for both fuel combinations. The IMEPg return map for POMDME–NG combustion was less scattered at the 350 CAD SOI.

Engineering↗
DOE OSTI2022

Predicting the Cetane Number, Sooting Tendency, and Energy Density of Terpene Fuel Additives

Abstract Discovering renewable fuels and fuel additives is paramount in reducing carbon emissions from internal combustion engines. Terpenes, a group of compounds that can be synthesized from plant matter and microorganisms, have gained significant interest in recent years as promising candidates for fuels/additives. Terpenes are a diverse class of compounds that contain rings and methyl branches, resulting in high energy densities and optimal cold weather behavior. Their variation in bond order, carbon chains, and functional groups lead to varying degrees of soot formation and performance in existing engines. The present work leverages predictive models, namely artificial neural networks, to predict the cetane number (CN), sooting tendency (quantified with yield sooting index, YSI), and energy density (quantified with lower heating value, LHV) of terpenes and hydrogenated terpenes whose sooting propensities were previously determined through experimental means. Predicted sooting propensities of these terpenes are compared with experimental values, and predicted cetane numbers and energy densities are used to comment on the compounds’ ability to act as fuels/additives. Expected prediction errors for CN, YSI, and LHV, defined by blind test set median absolute error, are within 5.56 cetane units, 3.63 yield sooting index units, and 0.77 MJ/kg respectively. Additionally, the present work investigates a variety of correlation/dependence metrics for property-property relationships, furthering our understanding of how combustion-relevant properties are related.

09 BIOMASS FUELS↗
DOE OSTI2024

Performance and Emissions of an SI Engine Fueled With DME-Propane Blends

Dimethyl Ether (DME) is an alternative fuel that can be produced renewably and has the potential for lower CO and NOx emissions than conventional petroleum-based fuels. Blending DME with another gaseous fuel such as propane, which has a lower knock tendency than gasoline, can allow this fuel to be leveraged on SI engines. In this study, the use of DME-propane blends on a spark ignition (SI) engine was studied via computer simulations in order to understand the impact on engine performance and emissions and to identify the knock limitations of using such fuel blends. A 2L Hyundai SI engine was modeled in GT Power and the model was validated by comparing it with computational fluid dynamics (CFD) simulation results. Starting from pure propane, DME was added incrementally until knock was observed in the engine. Results indicate that it is feasible to run propane with DME percentages up to 35% before severe knock impacts were observed. The BTE was higher, but the BSFC also increased for DME-propane blends as compared to gasoline. Here, an increase in NOx emissions was detected along with a significant decrease in CO emissions. CO 2 emissions declined for propane as compared to gasoline but increased with the addition of DME.

alternative fuels↗
DOE OSTI2025

Battery charging goes quantum

Rechargeable lithium-ion batteries power consumer electronics and electric vehicles, making them an essential component of the modern economy. Although lithium-ion battery technology has improved continuously over the past decades, widespread adoption of electrified transportation requires charging in less than 15 min to be competitive with internal combustion engines. As a battery charges and discharges, lithium ions travel across the electrode-electrolyte interface. The rate at which lithium ions transfer is dictated by the structure and physical properties of electrolytes and lithium-storing electrodes. Yet, the exact chemical reaction mechanism underlying the insertion of lithium ions at the electrode-electrolyte interface remains elusive. On page 46 of this issue, Zhang et al. (1) report experimental evidence that shows that lithium-ion battery charge and discharge occur through a coupled ion-electron transfer mechanism. Furthermore, this could establish an experimental and theoretical platform to extract key parameters for optimizing charge transfer rates in lithium-ion batteries.

Warburton, Robert E. [Case Western Reserve Univers↗
DOE OSTI2021

Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

Engineering↗
DOE OSTI2022

Evaluating an Eco-Cooperative Automated Control System

The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.

Engineering↗
DOE OSTI2021

Determination of a most representative cycle from cylinder pressure ensembles via statistical method using distribution skewness

In internal combustion engine research, cylinder pressure measurements provide valuable information about the underlying thermodynamic and combustion processes, and are typically collected in ensembles of several 100 traces. Although in some particular fields of combustion research all traces are analyzed, in most cases only one trace is studied because analyzing all the traces is impractical due to the large number of collected samples. Instead, an ensemble-averaged pressure trace is commonly calculated and used for analysis. However, this pressure trace is highly smoothed and dynamic information is lost during the averaging process. With the average trace, pressure rise rates are lower and pressure oscillations such as the ones resulting from combustion knock are lost. In this work, a statistical method was developed to determine the “most representative cycle,” which is the cycle from the ensemble that has the pressure trace most representative of the engine operating condition. Eleven characteristic parameters are computed from each pressure trace and probabilistic distributions are obtained for each of the parameters using all the traces in the ensemble. Finally, the most representative cycle is selected by means of a cost function minimization. The benefits of this method are illustrated using experimental data from four very different engine platforms, under four different combustion modes and over a range of operating conditions.

42 ENGINEERING↗
DOE OSTI2023

Development and evaluation of a skeletal mechanism for EHN additized gasoline mixtures in large Eddy simulations of HCCI combustion

Advanced Low Temperature Combustion modes, such as the Sandia proposed Additive-Mixing Fuel Injection (AMFI), can unlock significant potential to boost fuel conversion efficiency and ultimately improve the energy conversion of internal combustion engines. This is a novel improved combustion process that is enabled by supplying small (<5%) variable amounts of autoignition improver to the fuel to enhance the engine operation and control. Common, diesel-fuel ignition-quality enhancing additive, 2-ethylexyl nitrate (EHN), is doped into gasoline to enable Sandia LTGC + AMFI combustion. This manuscript focuses on the development of a reduced sub-mechanism for EHN chemical kinetics at engine relevant conditions that is implemented into a skeletal mechanism for chemical kinetic studies of gasoline surrogate fuels. The mechanism validation utilized zero-dimensional numerical simulations and comparison to shock tube ignition-delay data of pure and EHN-doped n-heptane. Additional validation is presented with Homogeneous Charge Compression-Ignition (HCCI) engine data of pure and EHN-doped research-grade E10 gasoline. Then, the mechanism was deployed in a 3-D computational fluid dynamics (CFD) using Large Eddy Simulations (LES) to model the HCCI engine experiments of 0.4% vol EHN additized E10 gasoline at several equivalence ratios. Simulations showed a very good performance of the mechanism, and the model accurately reproduced (a) the ignition point, (b) combustion phasing, (c) combustion duration, and (d) the peak of the heat release rates of the engine experiments. The results show that EHN promotes Low-Temperature Heat Release, ultimately driving the gasoline to autoignite at thermodynamic conditions where the fuel would not otherwise ignite. Overall, this work demonstrates a viable reduced chemical-kinetic mechanism for EHN and shows that it can be combined with a skeletal gasoline mechanism for CFD-LES analysis of well-mixed LTGC that matches well with experimental results. The CFD-LES analysis also shows the spatial distribution of EHN-fuel interactions that control the autoignition throughout the combustion chamber.

Guleria, Gaurav↗