This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging reinforcement learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating realistic traffic signals, road gradients, and vehicle interactions. RL agents are trained to interpret vehicle states, road attributes, and traffic light information to adjust control parameters in real time. This integration enables the controller to anticipate and respond to dynamic driving scenarios, thereby improving both energy efficiency and operational robustness. Simulation experiments across multiple driving scenarios demonstrate that the RL-enhanced eco-driving controller achieves substantial energy savings without compromising travel time. On average, our approach surpasses a baseline eco-driving controller without RL by 12% and outperforms a high-fidelity human driver model by 24.2% in terms of energy consumption reduction. These results highlight the potential of continuous action space RL to advance real-time eco-driving control in CAVs. Overall, this work provides a pathway toward more intelligent, adaptive, and sustainable vehicle control systems that can accelerate the deployment of energy-efficient mobility solutions.

Analyzing large volumes of real-world driving data is essential for providing meaningful and reliable insights into real-world trips, scenarios, and human driving behaviors. To this end, we developed a multi-level data processing approach that adds new information, segments data, and extracts desired parameters. Leveraging a confidential but extensive dataset (over 1 million km), this approach leads to three levels of in-depth analysis: trip, scenario, and driving. The trip-level analysis explains representative properties observed in real-world trips, while the scenario-level analysis focuses on scenario conditions resulting from road events that reduce vehicle speed. The driving-level analysis identifies the cause of driving regimes for specific situations and characterizes typical human driving behaviors. Such analyses can support the design of both trip- and scenario-based tests, the modeling of human drivers, and the establishment of guidelines for connected and automated vehicles.

This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-$k$ vehicles star ranking system, and (2) engage in more general assignment problems where $n$ vehicles need to be deployed over $m \leq n$ trips. This new assignment system has been deployed and integrated into the POLARIS Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium

This observational study explores the effect of adaptive cruise control (ACC) on energy in electric vehicles (EVs) and contrasts the findings with prior research on internal combustion engine (ICE) vehicles. Using real-world driving data, we show that ACC engagement results in a penalty of +6.62 Wh/km, a 2.5% increase over the fleet-level average of 266 Wh/km. This penalty is smaller than that in ICE vehicles, primarily due to the superior efficiency of EV powertrains and the mitigating role of regenerative braking. On average, human drivers achieve higher regenerative braking efficiency than ACC. However, when braking conditions match, ACC marginally outperforms human drivers across most regions of the speed-deceleration map. This research provides insights into the interplay between energy-efficient technologies and driver-assistance systems, and highlights the need to optimize automation algorithms to leverage the unique characteristics of EV powertrains, maximize energy recovery, and support next-generation energy management solutions in transportation.

This paper presents AutonomieAI, a novel toolkit designed for efficient energy estimation of vehicles across diverse trip scenarios, routes, and drive cycles, applicable to a broad range of vehicle powertrain technologies. It leverages state-of-the-art Machine Learning techniques to deliver real-time energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. Benchmark results show that AutonomieAI achieves high accuracy, with an average percentage error below 2% for most powertrain types, and computational efficiency capable of processing over 10,000 trips per second. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. AutonomieAI is the result of large neural network based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics based simulation softwares.

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. What sets this methodology apart is its integration of lightweighting advancements specifically tailored to different vehicle powertrains. Moreover, beyond describing the novel methodology, this paper offers a comprehensive analysis of energy consumption reduction. It delves into the intricate dynamics across a spectrum of vehicle powertrains, providing a nuanced understanding of how advancements in lightweighting contribute to energy efficiency gains. This work not only highlights an innovative approach to vehicle simulation, but also contributes valuable insights to the ongoing discourse surrounding CAFE standards and sustainable automotive technologies.

This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of cruise control on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, cruise control engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of cruise control on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that cruise control can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of cruise control engagement to properly evaluate its effects. Efficient automated driving technology to improve upon human driving behaviour offer a promising pathway to energy and fuel savings. Here, authors analyse real-world data to determine how adaptive cruise control affects fuel consumption and identify specific driving situations where it can be optimized.

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. This integration highlights BatPaC's adaptability to emerging technologies, ensuring efficiency and accuracy in Corporate Average Fuel Economy (CAFE) rulemaking evaluations. In short, BatPaC is a robust LIB pack design and cost estimation tool, contributing to the evolving landscape of lithium-ion battery technology. Its integration with Autonomie positions it as a key player in large-scale simulations and regulatory assessments, emphasizing the tool's relevance and effectiveness in the dynamic electric vehicle and battery technology landscape. Continuous refinement will be essential to address market dynamics and ensure BatPaC's ongoing impact.

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. This paper introduces an innovative approach to CAFE standards development, emphasizing large-scale simulation processes that encompass standard regulatory driving cycles, diverse vehicle classes, and various time frames. At the core of this approach lies Autonomie's capability to integrate advanced engine technologies tailored to specific vehicle classes and powertrains, facilitating a comprehensive understanding of their impact on fuel economy. By customizing simulations to mirror real-world conditions across different vehicle types, the research provides nuanced insights crucial for effective CAFE standards development and implementation. Furthermore, the paper delves into the reductions in energy consumption across diverse vehicle powertrains, offering a comprehensive analysis of the potential for fuel efficiency improvements. Insights gained from this in-depth analysis contribute valuable knowledge to the ongoing pursuit of sustainable transportation solutions. This collaborative effort exemplifies a commitment to advancing understanding in vehicle dynamics, energy usage, and technology performance through large-scale simulations, ultimately fostering informed decision-making for a more sustainable automotive future.

This paper presents a comprehensive analysis of the impact of adaptive cruise control (ACC) on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level ACC benefit estimate that compares trips with and without ACC in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of ACC on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, ACC engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of ACC on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that ACC can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of ACC engagement to properly evaluate ACC effects.

In 1975, Congress passed the Energy Policy and Conservation Act (EPCA), requiring standards for corporate average fuel economy (CAFE), and charging the U.S. Department of Transportation (DOT) with the establishment and enforcement of these standards. The Secretary of Transportation has delegated these responsibilities to the National Highway Traffic Safety Administration (NHTSA). NHTSA has contracted the DOT Volpe National Transportation Systems Center (Volpe Center) to provide analytical support for NHTSA’s regulatory and analytical activities related to fuel economy standards. Unlike long-standing safety and criteria pollutant emissions standards, fuel economy standards apply to manufacturers’ overall fleets rather than to individual vehicle models. In developing the standards, NHTSA made use of the CAFE Compliance and Effects Modeling System (the CAFE model), which was developed by DOT’s Volpe Center for the 2005–2007 CAFE rulemaking and has been continually updated since. The model is the primary tool used by the agency to evaluate potential CAFÉ stringency levels by applying technologies incrementally to each manufacturer’s fleet until the requirements under consideration are met. The CAFE model relies on numerous technology-related and economic inputs, such as market forecasts and technology cost and effectiveness estimates. These inputs are categorized by vehicle classification, technology synergies, phase-in rates, cost learning curve adjustments, and technology decision trees. The Volpe Center assists NHTSA in the development of the engineering and economic inputs to the CAFE model by analyzing the application of potential technologies to the current automotive industry vehicle fleet to determine the feasibility of future CAFÉ standards, the associated costs, and the benefits of the standards. Part of the CAFE model’s function is to estimate CAFE improvements that a given manufacturer could achieve by applying additional technologies to specific vehicles in its product line. Because CAFÉ standards apply to the average fuel economy across manufacturers’ entire fleets of new passenger cars and light trucks, the model, when simulating manufacturers’ potential application of technology, considers the entire range of each manufacturer’s product line. This typically involves accounting for more than 1,000 distinct vehicle models and variants, many more than can be practically examined using full vehicle simulation (or the other techniques mentioned above). Instead, the model uses estimates of the effectiveness of specific technologies for a representative vehicle in each vehicle class, and arranges technologies in decision trees defining logical progressions from lower to higher levels of cost, complexity, development requirements, and/or implementation challenges. All inputs to CAFE’s decision tree model are related to the effectiveness (fuel consumption reduction) of each fuel-saving technology. Although vehicle testing could be used to estimate these factors, vehicle testing that spans many vehicle types and technology combinations could be prohibitively resource-intensive. Another alternative, either as a substitute for or as a complement to vehicle testing, is to make greater use of vehicle simulation. Full vehicle simulation tools use physics-based mathematical equations, engineering characteristics (e.g., engine maps, transmission shift points, hybrid vehicle control strategies), and explicit drive cycles to predict the effectiveness of individual fuel-saving technologies as well as their combinations. Argonne National Laboratory, a U.S. DOE national laboratory, has developed a full vehicle simulation tool, Autonomie, which has become one of the industry’s standard tools for analyzing vehicle performance, energy consumption, and technology effectiveness. Through an Inter Agency Agreement, the DOE Argonne Site Office and Argonne National Laboratory have been tasked with conducting full vehicle simulation t

The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems, while allowing for underlying complex subsystem level interactions. Particularly, we present a data-driven approach to vehicle price modeling and its component price estimation by leveraging a combination of concepts from machine learning and game theory. We show an alternative to common teardown methodologies and surveying approaches for component and vehicle price estimation at the manufacturer's suggested retail price (MSRP) level that has the advantage of bypassing the uncertainties involved in 1) the gathering of teardown data, 2) the need to perform expensive and biased surveying, and 3) the need to perform retail price equivalent (RPE) or indirect cost multiplier (ICM) adjustments to mark up direct manufacturing costs to MSRP. This novel exercise not only provides accurate pricing of the technologies at the customer level, but also shows the, a priori known, large gaps in pricing strategies between manufacturers, vehicle sizes, classes, market segments, and other factors. There is also clear synergism or interaction between the price of certain technologies and other specifications present in the same vehicle. Those (unsurprising) results are indication that old methods of manufacturer-level component costing, aggregation, and the application of a flat and rigid RPE or ICM adjustment factor should be carefully examined. The findings are based on an extensive database, developed by Argonne National Laboratory, that includes more than 64,000 vehicles covering MY1990 to MY2020 over hundreds of vehicle specs.

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) supports research and development (R&D), as well as deployment of efficient and sustainable transportation technologies, that will improve energy efficiency and fuel economy and enable America to use less petroleum. To accelerate the creation and adoption of new technologies, DOE-VTO has developed specific targets for a wide range of powertrain technologies (e.g., engine, battery, electric machine, lightweighting, etc.). This paper quantifies the impact of VTO R&D on vehicle energy consumption and cost compared to expected historical improvements across vehicle classes, powertrains, component technologies and timeframes. We have implemented a large scale simulation process to develop and simulate tens of thousands of vehicles on U.S. standard driving cycles using Autonomie, a vehicle simulation tool developed by Argonne National Laboratory. Results demonstrate significant additional reductions in both cost and energy consumption due to the existence of VTO R&D targets compared to predicted historical trends. It is observed that, over time, the fuel consumption of different electrified vehicles is expected to decrease by 40–50% and a reduction of 45–55% for vehicle manufacturing costs owing to significant improvements through various VTO R&D targets.

midsize SUV, and pickup trucks); and Fuels (i.e., gasoline, diesel, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015, 2020, 2025, 2030, and 2045. A delay of 5 years is assumed between laboratory year and model year (year technology is introduced into production). Finally, uncertainties are included for both technology performance and cost aspects by considering two cases: Low case, aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on regulations, business as usual; and High case, aligned with aggressive technology advancements based on R&D targets developed through support by VTO & HFTO. These scenarios are not intended as predictions of future performances. The energy and cost impact of different technologies were estimated using Autonomie (www.autonomie.net), Argonne vehicle system simulation tool. Autonomie is a state-of-the-art vehicle system simulation tool used to assess the energy consumption, performance and cost of multiple advanced vehicle technologies across classes (from light to heavy duty), powertrains (from conventional to HEVs, FCEVs, PHEVs and BEVs), components and control strategies. Autonomie is packaged with a complete set of vehicle models for a wide range of vehicle classes, powertrain configurations and component technologies, including vehicle level and component level controls. These controls were developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies including analyzing various component technologies, sizing powertrains components for different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route based vehicle energy control and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions and estimates the vehicle-level energy consumption benefits and associated technology costs for the various types of light duty vehicles. All details of vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

This paper presents a machine learning approach to model the electric consumption of electric vehicles at macroscopic level, i.e., in the absence of a speed profile, while preserving microscopic level accuracy. For this work, we leveraged a high-performance, agent-based transportation tool to model trips that occur in the Greater Chicago region under various scenario changes, along with physics-based modeling and simulation tools to provide high-fidelity energy consumption values. The generated results constitute a very large dataset of vehicle-route energy outcomes that capture variability in vehicle and routing setting, and in which high-fidelity time series of vehicle speed dynamics is masked. We show that although all internal dynamics that affect energy consumption are masked, it is possible to learn aggregate-level energy consumption values quite accurately with a deep learning approach. When large-scale data is available, and with carefully tailored feature engineering, a well-designed model can overcome and retrieve latent information. This model has been deployed and integrated within POLARIS Transportation System Simulation Tool to support real-time behavioral transportation models for individual charging decision-making, and rerouting of electric vehicles.

From a Total Cost of Ownership (TCO) standpoint, the lowest-cost powertrain option depends on driving conditions and number of miles driven over the lifetime of the vehicle. Studies looking at the value proposition of powertrain options traditionally assess the energy consumption based on standardized drive cycles. TCO is then calculated using average yearly Vehicle Miles Travelled (VMT). This approach which looks at TCO at the aggregate level, does not take into consideration the variations in driving patterns and the spread of VMT that exist in the vehicle population. In this study, we first determine the driving pattern of each vehicle in the Chicago metropolitan area. We then consider different powertrain options for each vehicle, calculate its associated TCO and provide a powertrain distribution based on lowest TCO. Vehicles under consideration are Privately Owned Vehicles (POV) and vehicles used by Transportation Network Companies (TNC).

In developing the standards, NHTSA made use of the CAFE Compliance and Effects Modeling System (the CAFE model), which was developed by DOT’s Volpe Center for the 2005–2007 CAFE rulemaking and has been continually updated since. The model is the primary tool used by the agency to evaluate potential CAFÉ stringency levels by applying technologies incrementally to each manufacturer’s fleet until the requirements under consideration are met. The CAFE model relies on numerous technology-related and economic inputs, such as market forecasts and technology cost and effectiveness estimates. These inputs are categorized by vehicle classification, technology synergies, phase-in rates, cost learning curve adjustments, and technology decision trees. The Volpe Center assists NHTSA in the development of the engineering and economic inputs to the CAFE model by analyzing the application of potential technologies to the current automotive industry vehicle fleet to determine the feasibility of future CAFÉ standards, the associated costs, and the benefits of the standards. Part of the CAFE model’s function is to estimate CAFE improvements that a given manufacturer could achieve by applying additional technologies to specific vehicles in its product line. Because CAFÉ standards apply to the average fuel economy across manufacturers’ entire fleets of new passenger cars and light trucks, the model, when simulating manufacturers’ potential application of technology, considers the entire range of each manufacturer’s product line. This typically involves accounting for more than 1,000 distinct vehicle models and variants, many more than can be practically examined using full vehicle simulation (or the other techniques mentioned above). Instead, the model uses estimates of the effectiveness of specific technologies for a representative vehicle in each vehicle class, and arranges technologies in decision trees defining logical progressions from lower to higher levels of cost, complexity, development requirements, and/or implementation challenges. All inputs to CAFE’s decision tree model are related to the effectiveness (fuel consumption reduction) of each fuel-saving technology. Although vehicle testing could be used to estimate these factors, vehicle testing that spans many vehicle types and technology combinations could be prohibitively resource-intensive. Another alternative, either as a substitute for or as a complement to vehicle testing, is to make greater use of vehicle simulation. Full vehicle simulation tools use physics-based mathematical equations, engineering characteristics (e.g., engine maps, transmission shift points, hybrid vehicle control strategies), and explicit drive cycles to predict the effectiveness of individual fuel-saving technologies as well as their combinations. Argonne National Laboratory, a U.S. DOE national laboratory, has developed a full vehicle simulation tool, Autonomie, which has become one of the industry’s standard tools for analyzing vehicle performance, energy consumption, and technology effectiveness. Through an Inter Agency Agreement, the DOE Argonne Site Office and Argonne National Laboratory have been tasked with conducting full vehicle simulation t

Transportation system simulation is a widely accepted approach to evaluate the impact of transport policy deployment. In developing a transportation system deployment model, the energy impact of the model is extremely valuable for sustainability and validation. It is expected that different penetration levels of Connected-Autonomous Vehicles (CAVs) will impact travel behavior due to changes in potential factors such as congestion, miles traveled, etc. Along with such impact analyses, it is also important to further quantify the regional energy impact of CAV deployment under different factors of interest. The objective of this paper is to study the energy consumption of electrified vehicles in the future for different penetration levels of CAVs deployment in the City of Chicago. The paper will further provide a statistical analysis of the results to evaluate the impact of the different penetration levels on the different electrified powertrains used in the study.

The U.S. Department of Energy’s (DOE) Hydrogen & Fuel Cell Technologies Office (HFTO) supports research, development (R&D), and deployment of efficient and sustainable transportation technologies that will improve energy efficiency, fuel economy, and enable America to use less petroleum. To accelerate the development and adoption of new technologies, both HFTO and the Vehicle Technologies Office (VTO) has developed specific targets for a wide range of powertrain technologies (e.g., fuel cell system, hydrogen storage, engine, battery, electric machine, lightweighting, etc.).

The U.S. Department of Energy, Vehicle Technologies Office (U.S. DOE-VTO) has been developing more energy-efficient and environmentally friendly highway transportation technologies that would enable the United States to burn less petroleum on the road. System simulation is an accepted approach for evaluating the fuel economy potential of advanced (future) technology targets. U.S. DOE-VTO defines the targets for advancement in powertrain technologies (e.g., engine efficiency targets, battery energy density, lightweighting, etc.) Vehicle system simulation models based on these targets have been generated in Autonomie, reflecting the different EPA classifications of vehicles for different advanced timeframes as part of the DOE Benefits and Scenario (BaSce) Analysis. It is also important to evaluate the progress of these component technical targets compared to existing technologies available in the market. This paper will present an approach based on a large-scale simulation process, where simulations are performed over standard regulatory driving cycles for different vehicle classes over a range of timeframes by implementing the technology advancement targets set by the U.S. DOE-VTO. This approach would further evaluate the potential impact of different VTO engine targets for different technologies and provide a comparison with existing engine technologies available in the market.