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Islam, Ehsan

Publications and source records attributed to Islam, Ehsan.

15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Annual Technology Baseline (ATB): The 2024 Transportation Update

The Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates for vehicle technologies as well as fuels, and it details the assumptions used to calculate those costs, such as gas and electricity prices, discount rates, and vehicle miles traveled. The 2024 update added more biofuels pathways to align with pathways used in the Biomass Scenario Model.

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗
DOE OSTI2024

A real-time energy and cost efficient vehicle route assignment neural recommender system

Here, 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 (m ≤ 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 (SMART, 2024).

Energy consumption↗
DOE OSTI2024

Annual Technology Baseline: The 2022 Transportation Update [Slides]

The Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates for vehicle technologies as well as conventional and alternative fuels, and it details the assumptions used to calculate those costs, such as gas and electricity prices, discount rates, and vehicle miles traveled. The 2022 update added more classes of light-duty vehicles, medium and heavy-duty vehicles, an aviation page, and it aligned diesel pathways with aviation pathways.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Cradle-to-Grave Lifecycle Analysis of U.S. Light-Duty Vehicle-Fuel Pathways: A Greenhouse Gas Emissions and Economic Assessment of Current (2020) and Future (2030-2035) Technologies

This study provides a comprehensive life cycle analysis (LCA), or cradle-to-grave (C2G) analysis, of the cost and greenhouse gas (GHG) emissions of a variety of vehicle-fuel pathways, the levelized cost of driving (LCD) and cost of avoided GHG emissions. The C2G analysis assesses light duty midsize sedans and small sport utility vehicles (SUVs) across a variety of vehicle-fuel technology pathways, including conventional internal combustion engine vehicles (ICEVs), flexible hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs) with varying vehicle ranges, and fuel cell electric vehicles (FCEVs). In evaluating the vehicle-fuel combinations, this study considers both low-volume and high-volume “CURRENT TECHNOLOGY” cases (nominally 2020) and a high-volume “FUTURE TECHNOLOGY” lower-carbon case (nominally 2030–2035).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Parametric Study of Lithium-Ion Batteries using BatPaC (Final Report)

The Battery Performance and Cost Model (BatPaC) is a tool developed by Argonne National Laboratory to design lithium-ion battery (LIB) packs for automotive applications. The material demand and design specifications are used to estimate the cost of manufacturing the battery pack in a large volume production plant (5-50 GWh/year). With the option to select from various electrode chemistries and materials with their associated properties and prices, the resultant battery pack mass, volume, and cost can vary considerably. This report is the result of a parametric sensitivity study that helps identify the key parameters and prices that affect the battery metrics – the specific energy (Wh/kg) and the pack cost ($/kWh). The study was conducted with LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) and graphite (G) electrodes as the baseline materials used in a battery pack for a 300-mile range battery electric vehicle (BEV300) and a 50-mile range plug-in hybrid electric vehicle (PHEV50).

25 ENERGY STORAGE↗
DOE OSTI2022

Cradle-to-Grave Lifecycle Analysis of U.S. Light-Duty Vehicle-Fuel Pathways: A Greenhouse Gas Emissions and Economic Assessment of Current (2020) and Future (2030-2035) Technologies

This study provides a comprehensive life cycle analysis (LCA), or cradle-to-grave (C2G) analysis, of the cost and greenhouse gas (GHG) emissions of a variety of vehicle-fuel pathways, the levelized cost of driving (LCD) and cost of avoided GHG emissions. The C2G analysis assesses light duty midsize sedans and small sport utility vehicles (SUVs) across a variety of vehicle-fuel technology pathways, including conventional internal combustion engine vehicles (ICEVs), flexible hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs) with varying vehicle ranges, and fuel cell electric vehicles (FCEVs). Coming at a timely manner, given the marked increase, since 2016, in climate aspirations announced by governmental institutions and private firms both in the US and across the globe, this analysis builds on a previous comprehensive life cycle analysis, updating that study’s 2016 assumptions and methods (Elgowainy et al. 2016). These updates incorporate technological advances and changes in energy supply sources that have emerged during the intervening period. Utilizing these updated assumptions and methods, alongside more recent data, the present report accounts for a broader range of vehicle technologies and considers both current (2020) and expected future (2030-2035) conditions. Reflecting increased research interest in synthetic liquid fuels produced using renewable low-carbon electricity and CO 2 sources, electro-fuels (a.k.a. e-fuels) were added to the potential future fuel technologies that are evaluated. This study takes a “pathway” approach rather than a “scenario” approach; hence distinct, technically feasible, routes or sequences of processes starting with one or more feedstocks and ending with an intermediate or a final product are examined, not necessarily constrained by practical feedstock, economic, policy, and market considerations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Comparing total cost of ownership of battery electric vehicles and internal combustion engine vehicles

The technological advance of electrochemical energy storage and the electric powertrain has led to rapid growth in the deployment of electric vehicles. The high cost and the added weight of the batteries have limited the size (energy storage capacity) and, therefore, the driving range of these vehicles. However, consumers are steadily purchasing these vehicles because of the fast acceleration, quiet ride, and high energy efficiency. The higher pack-to-wheel efficiency and the lower energy cost per mile, as well as the lower expense for maintenance and repair, translate to operating savings over conventional vehicles. Here we compare battery electric vehicles with internal combustion engine vehicles based on the total cost of ownership. It is seen that the higher initial cost of electric vehicles can be recovered in as little as 5 years. This is especially true for electric vehicles with shorter driving ranges. Specifically, a vehicle with an electric driving range under 200 miles may achieve cost parity with an equivalent internal combustion engine vehicle in 8 years or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach

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 the use of this framework on a very specific (but generalizable) application, interesting for a more general AI audience. Here, we detail 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 uncertainties involved in gathering teardown data, the need to perform expensive and biased surveying, and the need to perform retail price equivalent or indirect cost multiplier 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 classes, market segments, etc. There is also clear interaction between the price of technologies and other specifications present in vehicles. Those results are indication that old methods of manufacturer-level component costing, aggregation, and application of flat and rigid adjustment factors should be carefully examined. The findings are based on a database developed by Argonne, which includes over 64,000 vehicles covering MY1990 to MY2020 with hundreds of vehicle specs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

2020 Annual Technology Baseline (ATB) Cost and Performance Data for Transportation Technologies

The 2020 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. The Transportation ATB includes current and projected estimates through 2050 for light-duty vehicle technologies as well as conventional and alternative fuels. This excel files include vehicle, fuel, and joined vehicle and fuel data with calculated levelized cost and emission values associated with the 2020 Transportation ATB. NREL has also provided a Tableau workbook to further explore the data. A website documents this data at https://atb.nlr.gov .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Annual Technology Baseline: The 2020 Transportation Update

Consistent cost and performance data for various transportation technologies can be difficult to find and may change frequently for certain technologies. With the Annual Technology Baseline (ATB), the National Renewable Energy Laboratory annually provides an organized and centralized set of such cost and performance data. The ATB uses the best information from the Department of Energy national laboratories' transportation analysts. The ATB has been reviewed by experts and it includes powertrains such as gasoline, diesel, natural gas, hybrid, plug-in hybrid, battery electric, and fuel cell, as well as fuels such as gasoline, ethanol, diesel, biodiesel, natural gas, electricity, and hydrogen.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

SMART Mobility. Connected and Automated Vehicles Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Connected and Automated Vehicles (CAVs) Pillar. This Pillar investigated the energy, technology, and usage implications of vehicle connectivity and automation and identified efficient CAV solutions. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗