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DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗

DOE OSTI2021

Development of heavy-duty vehicle representative driving cycles via decision tree regression

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Using Machine Learning to Understand Electric and Hybrid Vehicles Ownership in Burdened and Nonburdened Communities

Transitioning to electric and hybrid vehicles (EHVs) for all communities is a pivotal step toward sustainable transportation and environmental conservation. This paper aims to understand the adoption of EHVs, focusing on burdened communities (BCs) in the United States. The EHV ownership-based analysis combines two datasets—behavioral data from the Puget Sound Regional Travel Survey integrated with BCs (Justice40) data covering transportation insecurity, environmental burden, social vulnerability, health vulnerability, and climate and disaster risk burden. After creating this unique database, descriptive analysis and modeling are used to analyze the data and predict EHV ownership in the future. Specifically, we use a new method that combines particle swarm optimization (PSO) with a stacking model named PSO-Stacking, which incorporates heterogeneous base learners of machine learning and deep learning. PSO applies a customized objective function to select the optimal hyperparameters for heterogeneous learners within the stacking model, effectively addressing challenges such as multicollinearity, data imbalance, nonlinearity, and overfitting. The proposed solution covers more accurate results than standard benchmark models for EHV ownership in BCs and non-BCs. In addition, the results of the PSO-Stacking method are explained using the local interpretable model-agnostic explanations technique. Results show a negative correlation between the BCs indicators, that is, higher transportation insecurity associated with lower EHV ownership. Furthermore, BCs have higher future climate risk scores, diesel particulate matter levels, and PM2.5 in the air than non-BCs because of higher conventional vehicle ownership. These communities are at higher risk and can benefit from electrification, EV infrastructure, and EV policies to address environmental challenges.

Aslam, Zeeshan [ORNL]↗

DOE OSTI2026

EV Load Forecasting Guide: A Report by the Energy Systems Integration Group’s EV Load Forecasting Task Force

Forecasting electricity usage is a foundational planning activity for utilities, underpinning billions of dollars in grid investments that ensure system reliability. Historically, forecasting relied on trends in economic and population growth; however, transportation electrification presents a new and complex planning challenge. Unlike conventional loads, electric vehicle (EV) charging has relatively limited usage history. In addition, charging is driven by complex human behaviors, is mobile, and at the same time can concentrate geographically in ways that, without proper planning, can quickly overwhelm local distribution systems.

Giraldez, Julieta [Electric Power Engineers, Austi↗

DOE OSTI2022

Impacts of Increasing Electrification on State Fleet Operations and Charging Demand

State fleets represent an enticing opportunity to explore the near-term feasibility of fleet electrification. In many instances, state fleet operations encompass a wide geographic area with fleet locations for many vehicles. Serving these wide areas will require a significant amount of energy and, in the case of electric vehicles (EVs), a significant level of charging power. The peak demand as a result of this charging demand is of interest for fleets, with impacts on both utility bills and installation costs ranking among some of the greatest concerns. The combination of a wide operational area and multiple fleet locations positions state fleets as ideal candidates to understand the impacts of vehicle charging on fleet operations. As the availability of electric drivetrains expands beyond light-duty sedans, fleets need to understand when it will be appropriate operationally and financially to start adding electric drivetrains to their fleets. Throughout this process, it will also be important to understand the charging implications of fleet electrification and the resulting impacts to facility electrical systems. To better understand these considerations, NREL contracted Sawatch Labs to analyze the role that increasing state fleet electrification may have on the charging demand at fleet parking facilities.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2023

Advanced 2030 Turboprop Aircraft Modeling for the Electrified Powertrain Flight Demonstration Program

Electrified aircraft propulsion concepts are rapidly emerging due to their huge potential in fuel saving and mitigating negative environmental impact. In order to perform a linear technology progression and fairly assess the impacts of powertrain electrification, it is important to first establish parametric state-of-the-art baseline vehicle models with advanced technologies matured by 2030. For a regional turboprop (50-passenger) size class and a thin haul (19-passenger) turboprop size class, a current state-of-the-art technology reference aircraft (TRA) is identified and modeled using a multi-disciplinary analysis and optimization environment. Viable technologies for airframe and conventional propulsion system are then identified which are expected to be available by 2030. These technologies are parametrically infused in the TRA models to create advanced technology aircraft models, which will serve as the baseline models for future studies of powertrain electrification.

epfd↗

DOE OSTI2026

Future Projections of Lifecycle Cost and Greenhouse Gas Emissions of Light-Duty Vehicles

Vehicles with electrified powertrains carry the promise of significant reductions in greenhouse gas (GHG) emissions from a lifecycle analysis (LCA) standpoint compared to conventional internal combustion engine (CICE) vehicles. However, trade-offs exist between different types of electrified powertrains in terms of cost, consumer acceptance, and GHG reduction efficacy for different operating conditions. The open-source tool CarGHG was developed with an aim to enable the exploration of a plethora of parametric study scenarios, including the cost of electrification technologies, different driving patterns and charging habits, and the cost and carbon intensity of electricity and fuel blends. This paper introduces the framework of CarGHG, then showcases total cost of ownership (TCO) and LCA GHG results for select models of light-duty vehicles. Another capability of CarGHG, which is the ability to estimate the performance of “virtual” vehicle models (perceived vehicle design specifications not yet on the market), is utilized to explore future scenarios of electrification and low-carbon fuel blends for Small Sports Utility Vehicles (SUVs), a popular light-duty vehicle segment in North America. With opportunities, but also uncertainties, in future scenarios, it is likely wise to continue pursuing multiple ways towards the reduction of LCA GHG.

Hamza, Karim↗

DOE OSTI2023

An electrified boom actuation system with energy regeneration capability driven by a novel electro-hydraulic unit

This paper presents a novel electrified solution for linear actuation systems, which has undergone a successful in-vehicle demonstration, marking a significant advancement in this field. Aligned with the growing trend towards electrification, this solution holds promise for a wide range of off-road applications. The showcased electrohydraulic actuation architecture enables efficient four-quadrant operation and incorporates energy recovery capabilities. At the core of the system lies a distinctive electro-hydraulic unit, integrating a fixed hydraulic pump and a variable-speed electric motor into a compact and powerful entity. A specialized hydraulic system is designed to efficiently transform electrical energy from the electro-hydraulic unit into boom cylinder actuation. The sizing of the system is determined by the boom operation of a commercial compact loader. The maximum power output of the electro-hydraulic system is approximately 15 kW. The components of the proposed system are integrated into the reference vehicle, and comprehensive performance tests are conducted. The results of the tests performed on the vehicle with the boom lifting and lowering cycle indicate an overall system efficiency of 60%. The electrified solution consumes only about one-third of the energy used by the conventional boom actuation system on the reference machine, while exhibiting energy savings exceeding 75% during specific work phases. The energy savings come mainly from energy regeneration and minimal loss from throttling through the specialized hydraulic circuit design. Finally, the successful demonstration on the reference vehicle underscores the practicality and effectiveness of the proposed electrified solution in current mobile hydraulic applications

42 ENGINEERING↗

DOE OSTI2024

Building Electrification Pathways for Airports: Envisioning the Future of Zero-Emissions Transportation

In Athena ZEV, the next phase of the DOE-funded Athena Project, NREL is working with Dallas-Fort Worth International (DFW) airport and other partners to develop sophisticated strategies that will accelerate the decarbonization of millions of vehicles operating at and around airports. This work has an initial focus on the critical emerging challenge of rapid electrification of rental car operations and evaluating solutions through the NREL ARIES energy system simulation platform.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

DOE OSTI2020

Fleet-wide Electrification Impacts Assessment for the Valley Transportation Authority

This report explores the long-term electrification opportunities for the Valley Transit Authority’s (VTA) transit bus fleet. The potential for transit bus electrification at VTA as well as the economic impacts of partial and complete electrification are explored. We use the Revenue Operation and Device Optimization model to determine the optimal charging, operation and lowest capital and operating cost solution to achieve different levels of electrification to meet their existing routes. This study finds that, relying on only depot charging, around 70% of the daily trips by VTA’s transit bus fleet can be replaced with battery electric buses (BEBs) today. The benefits and drawbacks of five methods for improving the electrification potential beyond that achievable with only depot charging are discussed including (1) increase charger power, (2) purchase of larger vehicle batteries, (3) en-route charging, (4) purchasing additional buses and swapping them to enable the existing routes/blocks1 to be met, and (5) route/block redesign. A strategy is developed to enable full fleet electrification by increasing charger power or allowing intraday charging as a proxy for the options mentioned above. This method allows us to develop an understanding of the impacts and trade-offs of full fleet electrification. Two charging strategies are examined. Immediate charging, when the bus is charged as soon as it arrives at a depot or en-route charging station, and smart charging, which uses a controller to determine the best times to charge to achieve the lowest charging cost, while maintaining the same trip schedules. Smart charging is effective at reducing the peak power consumption, which can be reduced by between 31% and 65% compared to immediate charging. This translates directly to lower electricity demand charges and lower costs for possible distribution system upgrades. The total lifetime net present value (NPV) costs for different scenarios are presented in Figure ES-1. Scenarios are separated into three sections. The first stacked bar on the left is the base case (business-as-usual) where all buses are diesel hybrids, the next four bars include partial and full fleet electrification utilizing only immediate charging, and the last four bars include partial and full fleet electrification utilizing smart charging. The results show that smart charging scenarios are within ±4% of the lifetime NPV cost of the diesel-hybrid only (business-as-usual) scenario. The scenarios with full fleet electrification (i.e., including intraday charging) are 4% lower cost and those with partial fleet electrification (i.e., without intraday charging) are 2%–3% higher. However, it is important to note that the intraday charging scenarios do not include any additional costs for the equipment necessary to achieve intraday charging (e.g., additional chargers, larger batteries, new route design). Additionally, it is worth noting that the Low Carbon Fuel Standard (LCFS) credit received for implementing electric buses is essential to achieving these results.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Propulsion Electrification Architecture Selection Process and Cost of Carbon Abatement Analysis for Heavy-Duty Off-Road Material Handler

The heavy-duty off-road industry continues to expand efforts to reduce fuel consumption and CO 2 e (carbon dioxide equivalent) emissions. Many manufacturers are pursuing electrification to decrease fuel consumption and emissions. Future policies will likely require electrification for CO2e savings, as seen in light-duty on-road vehicles. Electrified architectures vary widely in the heavy-duty off-road space, with parallel hybrids in some applications and series hybrids in others. The diverse applications for different types of equipment mean different electrified configurations are required. Companies must also determine the value in pursuing electrified architectures; this work analyzes a range of electrified architectures, from micro hybrids to parallel hybrids to series hybrids to a BEV, looking at the total cost, total CO 2 e, and cost per CO 2 e (cost of carbon abatement, or cost of carbon reduction) using data for the year 2021. This study is focused on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i. This machine’s specialty application, including events like unloading large oil pipes from a railcar, requires a unique electrified architecture that suits its specific needs. However, the results from this study may be extrapolated to similar machinery to inform fuel savings options across the heavy-duty off-road industry. In this study, a unique electrified architecture is determined for the Cary-Lift. This architecture is informed by multiple rounds of a Pugh matrix decision analysis to select a shortened list of desirable electrified architectures. The shortened list is modeled and simulated to determine CO 2 e, cost, and cost per CO 2 e. A final architecture is determined as a plug-in series hybrid that reduces fuel consumption by 65%, targeting the large fuel and CO 2 e savings that are likely to be required for the future of the heavy-duty off-road industry.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

The Value of Motorcycle Electrification in Mexico City

Local and city governments worldwide are looking to adopt emerging technologies and mobility approaches, such as electric vehicles, as they pursue their environmental and economic goals. Commercially available electric vehicles include buses, automobiles, motorcycles, and other two-wheelers. Electrifying transportation is critical to achieve urban sustainability, but it also raises challenges for private and public actors. Jurisdictions across countries are seeking to answer big questions, including how the transportation sector can be electrified, what impacts are priorities, and how such impacts can be obtained.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Projecting Electric Vehicle Electricity Demands and Charging Loads

After over a century of petroleum dominance, electric vehicles are rapidly disrupting the transportation energy landscape. At the same time, the electric power systems are undergoing profound changes as variable renewables replace dispatchable fossil generators. It is critically important to understand how transportation electrification will impact electricity demand, including changes in the load shapes that characterize the system, and the value of flexible electric vehicle charging to better balance electricity demand and supply. This talk focuses on methods to project electricity demand for EV charging with high spatiotemporal fidelity to enable electricity system modeling and analysis and summarizes results from recent NREL studies in this area.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗

DOE OSTI2022

Electrification and decarbonization of spent Li-ion batteries purification by using an electrochemical membrane reactor

The expanding electric vehicle market brings with it exponential growth in the use of lithium (Li)-ion batteries (LIB) for which a wave of spent LIB is expected to come within the next 5 to 10 years. Due to the economic and strategic value imbedded within the metals contained in LIB, different recycling technologies, including hydrometallurgy, pyrometallurgy and direct recycling, are under development. Being different from previous hydrometallurgical methods, which may have high chemical consumption and negative environmental impact, an electrochemical membrane reactor is designed and validated for the first time, to electrify and decarbonize the impurity removal process. This reactor electroplates copper (Cu) and electrochemically precipitates aluminum (Al) and iron (Fe) from simulated spent LIB leachates, by consuming only air, water, and electricity, and the impurities are reduced to <1 ppm. The purified leachate maintains 99.5 % of the nickel (Ni), 95.4 % of the cobalt (Co) and 99.14 % of manganese (Mn) from the original leachate solution, and then can be directly applied for cathode precursor synthesis. Additionally, the purification process doesn’t introduce extra impurity, and the reactor restoration process generates valuable by-product hydro sulfate (H2SO4). This electrochemical process can reduce the cost, because of the much less chemical consumption and the valuable by-product generation, and mitigates the waste emissions, because of no extra impurity introduced and no greenhouse gas (GHG) produced. In conclusion, the chemical precipitation method uses significant amount of NaOH, which induced GHG emission during the manufacturing process.

25 ENERGY STORAGE↗

DOE OSTI2022

Impact of Transport Electrification Demand and Charging Schedules on Electricity Markets and Nuclear Generators

As the U.S. pursues deep decarbonization targets, electric vehicles (EVs) are likely to become a major driver of demand growth and a major determinant of daily demand patterns. This study analyzes a possible future ERCOT-like electricity grid, and examines the impact of different types of EV charging schedules on grid and market outcomes. This analysis demonstrates the significant impact of EV charging patterns on capacity expansion simulations. Even without EVs, the overall daily demand profile in a market can have significant impacts on prices and grid stability in that system, especially if non-dispatchable renewable generators (e.g. wind and solar) make up a significant fraction of the generation mix. EV demand will not necessarily follow this preexisting demand profile, so its daily trends may significantly change what generation portfolio would optimally serve the system. Furthermore, the effects of EV demand can alter the profitability of different types of units, by altering the frequency of market events like extreme-demand hours or zero-price hours. These effects are explored in this study. The EV demand levels were derived from MARKAL simulations of the West-South-Central North American Electric Reliability Corporation (NERC) region for the year 2050, using a carbon tax of $100/ton. The baseline MARKAL simulation forecasted that 23% of the region’s annual electricity demand in 2050 would be attributable to EVs, and broke out demand projections for EV and non-EV end-use in that year. To model lower EV penetration into the system, an additional case was explored which assumed that EVs only achieved 75% of the demand level projected by MARKAL.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

EV Charging Infrastructure Energization: An Overview of Approaches for Simplifying Processes and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure and electricity. Growth in electricity demand from EV charging, coupled with growth in other sectors like data centers and electrification of other sectors, is impacting electricity infrastructure and load service request processes after two decades of relatively flat electricity demand. While most electric vehicle charging occurs at home, enroute and depot charging for medium and heavy-duty vehicles, both using high-powered EV charging, are critical to meet electric vehicle operational needs. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have expressed distress that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Electric Vehicle Charging Demand in the Chicago Metropolitan Area through 2030

This report outlines the collaborative efforts between Argonne National Laboratory and Exelon in advancing the Agent-Based Transportation Energy Analysis Model (ATEAM). Aligning with ComEd’s beneficial electrification plan, this study developed eight scenarios to access the temporal and spatial distribution of charging load and demand stemming from the widespread adoption of battery electric vehicles (BEV) adoption, augmented public charging infrastructure deployment, and increased multi-unit dwelling (MUD) charging availability. Enhancements to the ATEAM model encompassed the simulation of multiple days of travel behavior, estimation of total public charging infrastructure needs, user interface refinements, and output tracking at both vehicle and charging station levels. The total electricity consumption for residential and public charging to support over 800,000 BEVs in Chicago in 2029 is projected at approximately 10.2 GWh. Enhanced MUD home charging accessibility (70%) amplifies the home charging load in the study area by 1.5% compared to the baseline scenario (10%). The widespread adoption of BEVs reduces peak charging loads, owing to their inclusion across households with diverse income levels, thus fostering a more dispersed charging activity pattern. However, widespread BEV adoption increases the peak home charging load in areas with lower median household incomes, reflecting a higher BEV concentration in these locales and, subsequently, heightened peak charging demands. In the Widespread BEV adoption scenario, fewer census tracts exhibit elevated peak loads for combined home and public charging, indicating a more even distribution of charging demand across the study area. Predominantly, peak loads for combined charging—both home and public— occur between 2 p.m. and 10 p.m. across all scenarios, encompassing the majority of census tracts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗