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The Battery Performance and Cost Model (BatPaC), developed by Argonne National Laboratory, is a versatile tool designed for lithium-ion battery (LIB) pack engineering. It accommodates user-defined specifications, generating detailed bill-of-materials calculations and insights into cell dimensions and pack characteristics. Pre-loaded with default data sets, BatPaC aids in estimating production costs for battery packs produced at scale (5 to 50 GWh annually). Acknowledging inherent uncertainties in parameters, the tool remains accessible and valuable for designers and engineers. BatPaC plays a crucial role in National Highway Transportation Traffic Safety Administration (NHTSA) regulatory assessments, providing estimated battery pack manufacturing costs and weight metrics for electric vehicles. Integrated with Argonne's Autonomie simulations, BatPaC streamlines large-scale processes, replacing traditional models with lookup tables.

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints.

Gasoline compression ignition shows great potential in reducing NOx and soot emissions with competitive thermal efficiency by leveraging the properties of gasoline fuels and the high compression ratio of compression ignition engines operating air-dilute. Meanwhile, its control becomes challenging due to not only the properties of different gasoline-type fuels but also the impacts of injection strategies on the in-cylinder reactivity. As such, a computationally efficient zero-dimension combustion model can significantly reduce the cost of control development. In this study, a previously developed zero-dimension combustion model for gasoline compression ignition was extended to multiple gasoline-type fuel blends and a port fuel injection/direct fuel injection strategy. Tests were conducted on a 12.4-liter heavy-duty engine with five fuel blends.

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance.

Gasoline compression ignition using a single gasoline-type fuel has been shown as a method to achieve low-temperature combustion with low engine-out NOx and soot emissions and high indicated thermal efficiency. However, key technical barriers to achieving low temperature combustion on multi-cylinder engines include the air handling system (limited amount of exhaust gas recirculation) as well as mechanical engine limitations (e.g. peak pressure rise rate). In light of these limitations, high temperature combustion with reduced amounts of exhaust gas recirculation appears more practical. Furthermore, for high temperature Gasoline compression ignition, an effective aftertreatment system allows high thermal efficiency with low tailpipe-out emissions. In this work, experimental testing was conducted on a 12.4 L multi-cylinder heavy-duty diesel engine operating with high temperature gasoline compression ignition combustion using EEE gasoline.

Diesel engines are one of the most popular combustion systems used in different types of heavy-duty applications because of higher efficiencies compared to the spark ignition engines. Combustion phasing and the rate of heat release in diesel engines are controlled by the rate at which the fuel is injected into the combustion chamber near top dead center. In this work, computational fluid dynamics (CFD) was employed to simulate the combustion behavior of a heavy-duty diesel engine equipped with a 16-hole injector, in which the nozzles were arranged in two individual rows. The two rows of nozzles have differential flow rate due to the geometrical construction of the injector. Combustion and performance characteristics of the engine were compared with and without considering the differential flow rate of the nozzle rows at a range of injection timing values.

Improvements in vehicle technology impact the purchase price of a vehicle and its operating cost. In this study, the monetary benefit of a technology improvement includes the potential reduction in vehicle price from using cheaper or smaller components, as well as the discounted value of the fuel cost savings. As technology progresses over time, the value and benefit of improving technology varies as well. In this study, the value of improving a few selected technologies (battery energy density, electric drive efficiency, tire rolling resistance, aerodynamics, light weighting) is studied and the value of the associated cost saving is quantified. The change in saving as a function of time, powertrain selection and vehicle type is also quantified. For example, a 10% reduction in aerodynamic losses is worth $24,222 today but only $8,810 in 2030 in an electric long haul truck. The decrease in value is primarily due to expected battery cost reduction over time.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

Gasoline compression ignition is a promising strategy to achieve high thermal efficiency and low emissions with limited modifications to the conventional diesel engine hardware. It is a partially premixed concept which derives its superiority from higher volatility and longer ignition delay of gasoline-like fuels combined with higher compression ratio typical of diesel engines. The present study investigates the combustion process in a gasoline compression ignition engine using computational fluid dynamics. Simulations are carried out on a single cylinder of a multi cylinder heavy-duty compression ignition engine which operates at a compression ratio of 17:1 and an engine speed of 1038 rev/min. In this study, a late fuel injection strategy is used because it is less sensitive to combustion kinetics compared to early injection strategies, which in turn is a better choice to assess the performance of the spray model.

Gasoline compression ignition (GCI) has been shown to offer benefits in the NOx-soot tradeoff over conventional diesel combustion while still achieving high fuel efficiency. However, due to gasoline’s low reactivity, it is challenging for GCI to attain robust ignition and stable combustion under cold operating conditions. Building on previous work to evaluate glow plug-assisted GCI combustion at cold idle, this work evaluates the use of a spark plug to assist combustion. The closed-cycle 3-D CFD model was validated against GCI test results at a compression ratio of 17.3 during extended cold idle operation under laboratory-controlled conditions. A market representative, ethanol-free, gasoline (RON92, E0) was used in both the experiment and the numerical analysis. Spark-assisted simulations were performed by incorporating an ignition model with the spark energy required for stable combustion at cold start.

In this last decade, non-destructive X-ray measurement techniques have provided unique insights into the internal surface and flow characteristics of automotive injectors. This has in turn contributed to enhancing the accuracy of Computational Fluid Dynamics (CFD) models of these critical injection system components. By employing realistic injector geometries in CFD simulations, designers and modelers have identified ways to modify the injectors’ design to improve their performance. In recent work, the authors investigated the occurrence of cavitation in a heavy-duty multi-hole diesel injector operating with a high-volatility gasoline-like fuel for gasoline compression ignition applications. They proposed a comprehensive numerical study in which the original diesel injector design would be modified with the goal of suppressing the in-nozzle cavitation that occurs when gasoline fuels are used.

Connected and automated technologies poised to change the way vehicles operate are starting to enter the mainstream market. Methods to accurately evaluate these technologies, in particular for their impact on safety and energy, are complex due to the influence of static and environmental factors, such as road environment and traffic scenarios. Therefore, it is important to develop modeling and testing frameworks that can support the development of complex vehicle functionalities in a realistic environment. Microscopic traffic simulations have been increasingly used to assess the performance of connected and automated vehicle technologies in traffic networks. In this paper, we propose and apply an evaluation method based on a combination of microscopic traffic simulation (AIMSUN) and a chassis dynamometer-based vehicle-in-the-loop environment, developed at Argonne National Laboratory.

The combustion system of a heavy-duty diesel engine operated in a gasoline compression ignition mode was optimized using a CFD-based response surface methodology and a machine learning genetic algorithm. One common dataset obtained from a CFD design of experiment campaign was used to construct response surfaces and train machine learning models. 128 designs were included in the campaign and were evaluated across three engine load conditions using the CONVERGE CFD solver. The design variables included piston bowl geometry, injector specifications, and swirl ratio, and the objective variables were fuel consumption, criteria emissions, and mechanical design constraints. In this study, the two approaches were extensively investigated and applied to a common dataset. The response surface-based approach utilized a combination of three modeling techniques to construct response surfaces to enhance the performance of predictions.

The objective of this study was to evaluate the fuel saving potential of various hybrid powertrain architectures for medium and heavy duty vehicles. The relative benefit of each powertrain was analyzed, and the observed fuel savings was explained in terms of operational efficiency gains, regenerative braking benefits from powertrain electrification and differences in vehicle curb weight. Vehicles designed for various purposes, namely urban delivery, utility, transit, refuse, drayage, regional and long haul were included in this work. Fuel consumption was measured in regulatory cycles and various real world representative cycles. A diesel-powered conventional powertrain variant was first developed for each case, based on vehicle technical specifications for each type of truck. Autonomie, a simulation tool developed by Argonne National Laboratory, was used for carrying out the vehicle modeling, sizing and fuel economy evaluation.

This work focuses on reducing the computational effort of a 0-dimensional combustion model developed for compression ignition engines operating on gasoline-like fuels. As in-cylinder stratification significantly contributes to the ignition delay, which in turn substantially influences the entire gasoline compression ignition combustion process, previous modeling efforts relied on the results of a 1-dimensional spray model to estimate the in-cylinder fuel stratification. Insights obtained from the detailed spray model are leveraged within this approach and applied to a reduced order model describing the spray propagation. Using this computationally efficient combustion model showed a reduction in simulation time by three orders of magnitude for an entire engine cycle over the combustion model with the 1-dimensional spray model.

In this study, the combustion system of a light-duty compression ignition engine running on a market gasoline fuel with Research Octane Number (RON) of 91 was optimized using computational fluid dynamics (CFD) and Machine Learning (ML). This work was focused on optimizing the piston bowl geometry at two compression ratios (CR) (17 and 18:1) and this exercise was carried out at full-load conditions (20 bar indicated mean effective pressure, IMEP). First, a limited manual piston design optimization was performed for CR 17:1, where a couple of pistons were designed and tested. Thereafter, a CFD design of experiments (DoE) optimization was performed where CAESES, a commercial software tool, was used to automatically perturb key bowl design parameters and CONVERGE software was utilized to perform the CFD simulations. At each compression ratio, 128 piston bowl designs were evaluated.

Achieving robust ignitability for compression ignition of diesel engines at cold conditions is traditionally challenging due to insufficient fuel vaporization, heavy wall impingement, and thick wall films. Gasoline compression ignition (GCI) has shown the potential to offer an enhanced NOx-particulate matter tradeoff with diesel-like fuel efficiency, but it is unknown how the volatility and reactivity of the fuel will affect ignition under very cold conditions. Therefore, it is important to investigate the impact of fuel physical and chemical properties on ignition under pressures and temperatures relevant to practical engine operating conditions during cold weather. In this paper, 0-D and 3-D computational fluid dynamics (CFD) simulations of GCI combustion at cold conditions were performed.

An accurate prediction of internal nozzle flow in fuel injector offers the potential to improve predictions of spray computational fluid dynamics (CFD) in an engine, providing a coupled internal-external calculation or by defining better rate of injection (ROI) profile and spray angle information for Lagrangian parcel computations. Previous research has addressed experiments and computations in transparent nozzles, but less is known about realistic multi-hole diesel injectors compared to single axial-hole fuel injectors. In this study, the transient injector opening and closing is characterized using a transparent multi-hole diesel injector, and compared to that of a single axial hole nozzle (ECN Spray D shape). A real-size five-hole acrylic transparent nozzle was mounted in a high-pressure, constant-flow chamber. Internal nozzle phenomena such as cavitation and gas exchange were visualized by high-speed long-distance microscopy.

A zero-dimensional heat release model was developed for compression ignition engines. This type of model can be utilized for parametric studies, off-line optimization to reduce experimental efforts as well as model-based control strategies. In this particular case, the combustion model, in a simpler form, will be used in future efforts to control the combustion in compression ignition engines operating on gasoline-like fuels. To allow for a realistic representation of the in-cylinder combustion process, a spray model has been employed to allow for the quantification of fuel distribution as well as turbulent kinetic energy within the injection spray. The combustion model framework is capable of reflecting premixed as well as mixing controlled combustion. Fuel is assigned to various combustion events based on the air-fuel mixture within the spray.

Gasoline compression ignition (GCI) using a single gasoline-type fuel for direct/port injection has been shown as a method to achieve low-temperature combustion with low engine-out NOx and soot emissions and high indicated thermal efficiency. However, key technical barriers to achieving low temperature combustion on multi-cylinder engines include the air handling system (limited amount of exhaust gas recirculation (EGR)) as well as mechanical engine limitations (e.g. peak pressure rise rate). In light of these limitations, high temperature combustion with reduced amounts of EGR appears more practical. Previous studies with 93 AKI gasoline demonstrated that the port and direct injection strategy exhibited the best performance, but the premature combustion event prevented further increase in the premixed gasoline fraction and efficiency.