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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.

The National Highway Traffic Safety Administration (NHTSA) plays a crucial role in guiding the formulation of Corporate Average Fuel Economy (CAFE) standards, and at the forefront of this regulatory process stands Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy (DOE) research institution, has developed Autonomie—an advanced and comprehensive full-vehicle simulation tool that has solidified its status as an industry standard for evaluating vehicle performance, energy consumption, and the effectiveness of various technologies. Under the purview of an Inter-Agency Agreement (IAA), the DOE Argonne Site Office (ASO) and Argonne have assumed the responsibility of conducting full-vehicle simulations to support NHTSA's CAFE rulemaking initiatives. This paper introduces an innovative approach that hinges on a large-scale simulation process, encompassing standard regulatory driving cycles tailored to various vehicle classes and spanning diverse timeframes.

In response to the stipulations of the Energy Policy and Conservation Act and the global momentum toward carbon mitigation, there has been a pronounced tightening of fuel economy standards for manufacturers. This stricter regulation is coupled with an accelerated transition to electric vehicles, catalyzed by advances in electrification technology and a decline in battery cost. Improvements in the fuel economy of medium- and heavy-duty vehicles through electrification are particularly noteworthy. Estimating the magnitude of fuel economy improvements that result from technological advances in these vehicles is key to effective policymaking. In this research, we generated vehicle models based on assumptions regarding advanced transportation component technologies and powertrains to estimate potential vehicle-level fuel savings. We also developed a systematic approach to evaluating a vehicle’s fuel economy by calibrating the size of the components to satisfy performance requirements.

The National Highway Traffic Safety Administration (NHTSA) has been leading U.S. efforts related to the rulemaking process for Corporate Average Fuel Economy (CAFE) standards. Argonne National Laboratory, a U.S. Department of Energy (DOE) national laboratory, has developed a full-vehicle simulation tool called Autonomie that has become one of the industry standard tools for analyzing vehicle performance, energy consumption, and technology effectiveness. Through an Interagency Agreement, the DOE Argonne Site Office and Argonne National Laboratory have been tasked with conducting full vehicle simulation to support NHTSA CAFE rulemaking. This paper presents an innovative approach focused on large-scale simulation processes spanning standard regulatory driving cycles, diverse vehicle classes, and various timeframes. A key element of this approach is Autonomie’s capacity to integrate advanced engine technologies tailored to specific vehicle classes and powertrains.

This paper presents a comprehensive analysis of emerging powertrain technologies for a wide spectrum of vehicles, ranging from light-duty passenger vehicles to medium and heavy-duty trucks. The study focuses on the anticipated evolution of these technologies over the coming decades, assessing their potential benefits and impact on sustainability. The analysis encompasses simulations across a wide range of vehicle classes, including compact, midsize, small SUVs, midsize SUVs, and pickups, as well as various truck types, such as class 4 step vans, class 6 box trucks, and class 8 regional and long-haul trucks. It evaluates key performance metrics, including fuel consumption, estimated purchase price, and total cost of ownership, for these vehicles equipped with advanced powertrain technologies such as mild hybrid, full hybrid, plug-in hybrid, battery electric, and fuel cell powertrains.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

The establishment of Corporate Average Fuel Economy (CAFE) standards by the Energy Policy and Conservation Act (EPCA) of 1975 marked a pivotal moment in the automotive industry's pursuit of greater fuel efficiency. The responsibility for the development and enforcement of these standards was assigned to the U.S. Department of Transportation (DOT), with the National Highway Traffic Safety Administration (NHTSA) assuming a critical role in their oversight and implementation. In collaboration with Argonne National Laboratory (Argonne), supported by the U.S. Department of Energy (DOE), significant strides have been made in advancing fuel efficiency through the development of Autonomie, a leading full-vehicle simulation tool. Through an Inter-Agency Agreement between the DOE Argonne Site Office and Argonne, comprehensive full-vehicle simulations are conducted to support NHTSA's CAFE rulemaking processes.

Hybrid electric vehicles (HEVs) have seen tremendous improvements in performance, fuel economy and cost over the last two decades. As battery and motor prices decrease, HEVs are likely to be even more attractive to consumers. This study considers how HEVs can improve and whether advancements in engines and other components will play a large role in the HEV segment. Past studies have relied on a rule-based component sizing approach for hybrids to meet certain performance criteria. By going beyond this approach, we can explore the design space by varying engine power and electric drivetrain power. This can provide more insights into the fuel-saving potential of HEVs, and the trade-offs required on performance or cost characteristics to achieve those savings. In this study, we examine the fuel-saving potential of three main hybrid powertrain architectures (parallel, series, and power-split) with varying degrees of hybridization (DOH) and using various engine technologies.

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.

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase).

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.

This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.

This paper examines, for several major markets, the fuel savings achievable with advanced engine technologies as “drop-in” substitutions for existing engines, as well as from increased electric hybridization of the powertrain. Key segments of light duty vehicles in major automotive markets including the US, China, EU, Japan, India, and Saudi Arabia were examined. Representative vehicles for each market were simulated using advanced vehicle modeling tools and evaluated on the relevant local regulatory cycle or cycles. In all cases, to ensure meaningful results, the performance of a given vehicle was maintained as engine and powertrain technology was varied through appropriate resizing of powertrain components. In total, 4 engine technologies and 5 powertrain architectures were simulated for 5 different markets.

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.

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.

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.

In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory.

Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.