As electric vehicle (EV) adoption continues to grow in the United States and worldwide, many have concerns that the grid may not be modernized fast enough to cope with the rapid electricity demand from the transportation sector. This study helps address these concerns by analyzing deidentified real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019. These data are used to develop interpretable models that characterize charging behavior and quantify factors most associated with how drivers use charging infrastructure. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into the factors that influence charging decisions such as next trip distance and state of charge (SOC). The dataset, encompassing over three million park-trip pairs from 1,997 vehicles, reveals that driving behavior significantly influences charging patterns, while infrastructure and regional factors exert a lesser impact. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

Many efforts have been made to simulate energy consumption of battery electric buses (BEBs) to optimize their deployment into existing fleets. The models produced, however, are rarely validated against real-world consumption data, limiting their generalizability and widespread application to fleets. Furthermore, a major concern specific to BEBs is the effects of harsh climates on their performance. We build upon the state-of-the-art energy consumption modeling techniques developed for BEBs and apply them to a unique geographic context and a unique electrified vehicle. This geography, climate, and vehicle further the existing understanding of the factors affecting medium- and heavy-duty electric vehicles (MHDEVs) by allowing for new relationships to be tested and by assessing the generalizability of known relationships to new contexts. We find that temperature is quite predictive of energy consumption for the battery electric motorcoach (BEM) in the case study environment, similar to what has been observed in other studies. Our model also incorporates wind speed and direction relative to travel, which is a novel contribution of our methodology. Results from our study are helpful for transit service planners, fleet operators, and logistics firms for improving their ability to predict performance of potential deployments of MHDEVs into existing operations.

Current and potential electric vehicle (EV) owners express concerns about the charging infrastructure, mentioning non-functional chargers, prolonged charging times, inconvenient charger locations, long wait times, and high costs as major barriers. Addressing these issues often requires analyzing actual vehicle charging data, which is typically proprietary and inconsistent due to diverse standards and protocols. To understand and improve the EV charging experience, customer reviews are typically used to identify common customer pain points (CPPs). However, there is not a comprehensive method to map customer reviews to a standardized set of CPPs. In collaboration with the National Charging Experience (ChargeX) Consortium, this study bridges these gaps by proposing a Systematic Categorization and Analysis of Large-scale EV-charging Reviews (SCALER) framework. SCALER is an integrated, deep learning framework that segments, actively labels, analyzes, and classifies EV charging customer reviews into six CPP categories. To test its effectiveness, we used SCALER to analyze over 72,000 reviews from customers charging various EV models on different networks across the United States. SCALER achieves a classification accuracy of 92.5%, with an F1 score exceeding 85.7%. By demonstrating real-world applications of SCALER, we enhance the industry’s ability to understand and address CPPs to improve the EV charging experience.

As governments and the automotive industry push towards electrification, it becomes increasingly critical to address the factors which influence individual car buying decisions. Evidence suggests that operational inconvenience or the perception thereof plays a large role in consumer decisions concerning battery electric vehicles (BEVs). BEV ownership inconvenience and its causal factors have been relatively understudied, rendering efforts to mitigate the issues insufficiently informed. This paper presents a method of producing an empirical equation which relates operational inconvenience to a small number of housing and local electric vehicle supply equipment (EVSE) infrastructure factors. The paper then further provides a method of applying the equation in a geo-spatial context allowing for the evaluation of the effects of policies in a geographical manner. this method enables future quantitative analyses concerning investment in EVSE infrastructure to be directly sensitive to BEV operational inconvenience due to charging.

Future work is needed to quantify the benefits of these proposed solutions to help

This study uses a hybrid meta-analysis and literature-review approach to understand the current state of knowledge regarding the costs of electric vehicle supply equipment (EVSE). We present a novel way to consider, categorize, and label measures of cost and show cost measure estimates from a sample of 13 recent studies. We find that in general, there is too much variation and too few commonly represented EVSE cost measures to reasonably provide aggregate figures for these measures. We propose a convention for presenting EVSE cost measures that includes the application (commercial or residential), the power level (Level 1, Level 2, DCFC [with further distinction based on rated power capacity]), and the type of cost measure (hardware, installation, operation, and total cost). We contend that providing researchers with standard cost measures will help to advance our knowledge of EVSE costs by ensuring that future work will use common metrics. Establishing common metrics will enable conventional meta-analyses that will make assessments of EVSE costs even more accessible. Additionally, common metrics will make tracking costs more reliable as the technology continues to evolve and become more ubiquitous.

As governments and the automotive industry push toward electrification, it becomes increasingly critical to address the broad set of factors influence individual car buying decisions. Evidence suggests that operational inconvenience or the perception thereof plays a large role in consumer decisions concerning Battery Electric Vehicles (BEVs). BEV ownership inconvenience and its causal factors have been relatively understudied, rendering efforts to mitigate the issues insufficiently informed. This paper presents an empirical equation, derived using a novel data-based method, which relates operational inconvenience to a small number of housing and local Electric Vehicle Supply Equimpent (EVSE) infrastructure factors. The equation and method provided can be used to conduct quantitative analyses on the inconvenience impacts of current and proposed EVSE infrastructure. Ultimately such a quantitative approach is needed to understand and mitigate large inequities of BEV experience and adoption which might emerge from electrification.

Long, lightly loaded feeders serving residential loads may begin to experience voltage excursions as plug-in electric vehicle (PEV) penetration increases. Residential PEV charging tends to occur during peak-load hours on residential feeders, leading to increased peak loads and potential voltage excursions. To avoid voltage excursions, two PEV charging control strategies were investigated using the IEEE 34-bus feeder. The first strategy shifts PEV charging energy from peak hours to off-peak hours; the other strategy allows PEVs to provide reactive power support. Undervoltage excursions seen in a simulation of uncontrolled charging of 200 PEVs were improved dramatically when these two control strategies were used. The minimum voltage on the feeder improved from 0.855 pu when PEV charging was uncontrolled to 0.959 pu when both control strategies were applied together.

The plug-in electric vehicles (PEVs) market is receiving help from the current political climate, incentives at the federal and state levels, excessive cost of petroleum fuel, growing focus on climate solutions, increasing investment and direction by automobile manufacturers and increased awareness through media reports and advertising. Increasingly, the transportation industry, in both the United States and many other countries, is aimed at electric motive energy where practical. Increased investment in research and development have led to increasing vehicle range and lower battery costs; both of which have been deterrents in the past. The increasing demand for PEVs (consisting of the battery electric vehicle [BEV] and plug-in hybrid electric vehicle [PHEV], is challenged by the need for charging infrastructure to support these vehicles. The BEV relies totally on the on-board battery to supply the motive energy while the PHEV utilizes its battery and an installed internal combustion engine (ICE). The maximum benefit is achieved by using the battery power as much as possible. This arrangement requires the use of battery charging equipment, known as electric vehicle supply equipment (EVSE).

The convergence of electrification and automated driving will introduce opportunities to improve the operation and energy-efficiency of transportation systems. This paper discusses the challenges of dispatching autonomous electric vehicles (AEVs) in a ride-hailing fleet and their interactions with charging infrastructure. An integrated decision-making framework for dispatching and charging has been proposed using system optimization approaches. An agent-based platform has been developed for simulating and testing the proposed methods. A case study using New York City taxi data has been performed with different fleet sizes, dispatching strategies, and charging networks. Advantages of optimization-based approaches for AEV fleet management have been studied and demonstrated, for example, for a fleet of 1,750 AEVs to meet 100,000 daily requests, optimization-based centralized fleet management would result in 14% more ride requests satisfied and 43% fewer zero-occupancy miles traveled than if AEVs make independent decisions based on heuristic strategy. Benefits on reducing fleet size and charging downtime from optimization approaches are also comprehensively illustrated.

This report presents pre-conceptual design scenarios for a potential multiphase demonstration program for innovative uses of nuclear energy with the National Reactor Innovation Center (NRIC) and the Crosscutting Technology Development Integrated Energy Systems (CTD IES) program in the U.S. Department of Energy’s Office of Nuclear Energy. The demonstration program would address the need for low-carbon energy sources among industry stakeholders by identifying and implementing high-impact advanced nuclear projects within a holistic systems perspective. Battelle Energy Alliance, LLC, the managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory (INL) in Idaho Falls, Idaho, is seeking Expressions of Interest (EOI) for industry stakeholder participation in the potential demonstration program. Funding sources have not yet been identified for the demonstration program. Responses to the EOI will shape the development and funding requirements for the potential program and inform the down-selection of project designs for further planning and analysis from the wide set of pre-conceptual design scenarios shown in this report. This introductory section summarizes the need for low-carbon energy sources, describes the phases envisioned for the demonstration program, and outlines the organization of this 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 Advanced Fueling Infrastructure Pillar. This Pillar investigated the charging infrastructure needs of electric ride-hailing and car-sharing vehicles, automated shuttle buses, and freight-delivery truck fleets. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.

During fiscal year 2019 (FY 2019), the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) funded early stage research & development (R&D) projects that address Batteries and Electrification of the U.S. transportation sector. The VTO Electrification Sub-Program is composed of Electric Drive Technologies, and Grid Integration activities. The Electric Drive Technologies group conducts R&D projects that advance electric motors and power electronics technologies. The Grid and Charging Infrastructure group conducts R&D projects that advance grid modernization and electric vehicle charging technologies. This document presents a brief overview of the Electrification Sub-Program and progress reports for its R&D projects. Each of the progress reports provide a project overview and highlights of the technical results that were accomplished in FY 2019.