Advances and Challenges in Vehicle Electrification

Abstract

In the US, light- to heavy-duty vehicles account for the largest portion of total greenhouse gas emissions. A rapid transition to electric vehicles (EVs) is critical in achieving the Net Zero goal of cutting carbon emissions. Approximately 11% of new car sales are EVs but adoption of this technology is still slow. The panel will present some of the EV adoption challenges, including charging infrastructure, charging behavior, changes in driving behavior and risk outcomes, crashworthiness, and other unintended consequences. Different research methods are used to address these challenges. The benefits of the approaches and findings for EV infrastructure stakeholders, regulators and policymakers, and researchers will be discussed. Lastly, the panel will review existing research gaps and emerging topics that still need to be addressed in order to increase EVs adoption while also supporting EVs’ safe operation.

Introduction

In the US, light-duty to heavy-duty vehicles account for the largest portion (29%) of total greenhouse gas (GHG) emissions. The rapid transition to electric vehicles (EVs) is critical in achieving the Net Zero goal of cutting carbon emissions. While the adoption of EVs has begun, approximately 11% of new car sales are EVs, challenges are emerging that may slow adoption (Carey, 2023). This panel will discuss some of those challenges, including charging infrastructure and charging behavior, changes in driving behavior and risk outcomes, crashworthiness, and other unintended consequences of EVs. The panel will review recent research findings, discuss research gaps, and the needed policies to address these challenges.

The public EV charging infrastructure is expanding rapidly, with stations emerging along transportation corridors. However, charging deserts exist in rural and low-income urban areas, and the overall density of chargers is inadequate to address demands. An emerging complication is that EV owners report non-functioning public chargers, accessibility concerns (e.g., charging cable weight and curbs near charging stations), and usability issues, such as slow charging speeds (J.D. Power, 2023). The most extensive charging system, Tesla, has a better reputation than the public systems but has yet to be widely available to non-Tesla EVs. This difference in reliability has led most EV manufacturers to announce a switch from the Combined Charging System (CCS) to the North American Charging Standard (NACS). The National Electric Vehicle Infrastructure (NEVI) program, a joint program of the US DOT and US DOE, is distributing funding to states to expand the public charging infrastructure and has published criteria for a high level of reliability (97% uptime). The ChargeX consortium, organized by NEVI, is developing additional reliability criteria. As the debate continues on how to efficiently distribute and deploy charging stations and expand the EV infrastructure, advanced computational models are attempting to quantify the future placement of these charging stations. However, these models do not necessarily incorporate user variability and assume that users are homogenous. Access to existing EV infrastructure charging stations differs among users and leads them to form distinct charging usage and behavioral patterns. However, as the industry increases battery sizes and users’ familiarity increases with EVs’ capabilities, the effect of range anxiety—the fear of running empty—among users could shift as other patterns emerge.

To date, the general discussion about the benefits of EV adoption is primarily focused on GHG emissions, while the implications of EVs on road safety have received less consideration. Multiple aspects of driving EVs may impact driver behavior and subsequent risk outcomes. Investigating these human factors of EVs provides insights into how users manage their range mobility, reliability, various driving and charging needs, as well as changes in driver behavior.

This panel brings together studies that utilize a wide range of research methods, including surveys, interviews, natural language processing, simulator studies, laboratory testing, and naturalistic methods. The approaches presented by the panelists are beneficial to EV infrastructure stakeholders while encouraging future human factors researchers to get involved as we work together to reduce EV adoption thresholds and support their safe operation.

Reliability of Public EV Chargers

Introduction of the Panelist

David Rempel, MD, CPE, is Professor Emeritus in the College of Engineering at the University of California at Berkeley and in the Department of Medicine at the University of California at San Francisco. He was the director of the Ergonomics Graduate Training Program at UC Berkeley from 1990 to 2014. In retirement, his activities are focused on renewable energy and the usability of the EV charge infrastructure. He is a Fellow of HFES and chair of the HFES Sustainability Technical Group.

Abstract/Background

Reliable and functional public EV DC fast chargers are critical to the successful rapid adoption of EVs. Unfortunately, in recent surveys, EV owners report frequent problems with public charging stations (CARB, 2022). The purpose of this study was to systematically evaluate the availability and usability of all public EV DC fast chargers in one region of Northern California.

Six hundred fifty-five Public EV DC fast chargers were tested in 2022 in the nine counties of the San Francisco Bay Area (Rempel et al., 2023). Of the 655 chargers tested, 23.5% were not functional and 3.2% had cables too short to reach the EV inlet. The causes of failure were payment errors (7.6%), charge initiation (6.1%), screen error messages (4.6%), and blank or unresponsive screens (2.7%). Two months later, all chargers in two of the counties were tested again; there was little change in charger state, indicating a poor maintenance system. There were minor differences between the equipment providers. High levels of reliability of public EV chargers are critical to the adoption of EVs. The current high failure rate is due to poor system designs and poor maintenance, due in part to limitations on parts supply and an inadequate number of experienced repair technicians. States distribute federal funds for public EV chargers and should adopt the federal reliability criteria of 97% uptime and enforce the criteria with financial consequences (FHWA, 2023).

Charging Behavioral Patterns of Current EV Users

Introduction of the Panelist

Gretchen A. Macht, PhD, is an Associate Professor of Industrial & Systems Engineering at The University of Rhode Island, where she directs the Sustainable Innovative Solutions Lab and Engineering for Democracy Institute. As a computational community ergonomist, she specializes in methods to quantify and understand patterns and the performance of end-users at a community level in an environment of public service and government. Dr. Macht received the PBN News 40 Under Forty Award (2019) and URI’s Research & Scholarship Excellence Award for Advanced Careers (2024) and serves as an HFES Science Policy Fellow (2023).

Abstract/Background

To provide quantitative insights into the unexplored behavior among current users, this research (i) discusses the differences between urban-rural users in Canada and (ii) discovers a user group exhibiting procrastinate-like charging in a statewide network.

This work analyzes charging session data to investigate two perspectives: charging station usage and the users themselves. The first study explores the Innovation Diffusion Theory—urban-rural divide—in charging behavior. It finds notable differences in 1,285,610 charging events across provinces and varying urbanity levels in Canada. Weekly repetitive charging demand patterns are evidence, while high-demand periods diverge among charging networks, displaying variations in direction and shape. The results indicate that stakeholders should recognize the necessity of developing individualized and area-specific charging networks to bolster utilization and enhance usability (Jonas & Macht, 2024). Secondly, through the robust estimation of Bayesian Mixture Modeling, the Deadline Rush Model theory examines a statewide public charging network’s frequent users’ profiles representing 70,611 charging events. With the selection of an informative prior, the Bayesian Mixture Model estimates that almost one-third of frequent users demonstrate procrastination-like charging behaviors. Although procrastination-like users need to charge when they arrive at a location, that might not necessarily be true for a plug-in hybrid. From a systems perspective, hybrid vehicles can clog the system for other users whose needs are more pressing (Khaleghikarahodi & Macht, 2024). Quantifying these users’ unique patterns highlights and supports the human variability knowledge required for more comprehensive and holistic electric vehicle charging station placement models.

Driver Behavior and Risk Outcomes with EVs

Introduction of the Panelist

Pnina Gershon, PhD, is a Research Scientist at MIT AgeLab, Center for Transportation and Logistics, where she co-directs the Advanced Vehicle Technology (AVT) consortium and leads the naturalistic driving research. Dr. Gershon’s research provides the theoretical and applied insights needed to develop solutions to next-generation transportation challenges and advance the future of safe, efficient, accessible, and sustainable mobility. Her research focuses on the impact of emerging technologies, automated and electrified mobility, driver distraction, impairment, and behaviors of high-risk driver populations on driving safety. Dr. Gershon received the NIH Fellows Award for Research Excellence (2018) and the NICHD Collaboration Award (2017).

Abstract/Background

Though there are clear emissions benefits to EVs, other aspects of these vehicles have yet to be explored. There are multiple aspects of driving EVs that introduce changes that may impact driver behavior and driving kinematics. A series of studies evaluated how driving EVs with one-pedal driving, regenerative braking, and differences in their settings (rate of regenerative braking based on charge, brake lights, brake-to-hold, and brake-to-creep) is associated with changes in driving kinematic patterns and risk behaviors (e.g., speeding, hard brakes, rapid acceleration). These studies also examined the learning and adaptation to one-pedal driving and how it evolved over time.

Analysis of driving kinematics indicated that drivers using EVs spend more time and miles in lower deceleration categories than drivers of internal combustion engine (ICE) vehicles. While the rate of hard braking events did not differ between vehicle types, driving EVs was characterized by higher rates of extreme acceleration events compared to ICE vehicles. There was significantly lower use of the brake pedal to decelerate as drivers learned to modulate the EV speed. While speeding prevalence of EVs was higher, EVs speeding magnitude was lower compared to ICE vehicles. EVs with one-pedal driving and regenerative braking are changing driving kinematic behavior within a relatively short period. Despite the initial higher rates of rapid acceleration events, driving EVs demonstrated ancillary benefits related to driving more miles coasting, and having lower speeding magnitude. As more drivers purchase EVs, there is an increasing need to understand how drivers may adapt to new vehicle technologies within the first months of interaction. Assessing the impact of electrification on driver behavior may support the development of in-vehicle technologies that can shape behavior to promote driving safety and facilitate efficient public education.

Potential Human Factors Consequences of EVs

Introduction of the Panelist

Justin Mason, PhD, is an Assistant Research Scientist at the Driving Safety Research Institute at the University of Iowa. His current research focuses on disentangling drowsy driving from fatigued driving, understanding the benefits and consequences of electric vehicles, validating measurement tools to assess drivers’ understanding of advanced driver assistance systems (ADAS), and developing a framework for ADAS consumer education. Justin is the chair of the HFES Surface Transportation Technical Group.

Abstract/Background

EVs differ in construction, performance, and operation compared to ICE vehicles. Known differences between EVs and ICE vehicles include noise (Cocron & Krems, 2013), range anxiety (Abid et al., 2022), regenerative braking systems (Labeye et al., 2016), and other vehicle dynamics. An environmental scan (Literature review, crash analysis, and web scraping of forums, video comments, and social media) and structured interviews with individuals who recently (≤1 year) purchased EVs, dealership employees, and emergency responders were conducted to determine differences between EVs and ICE vehicles and identify potential human factors consequences. The positive and negative potential human factors consequences were ranked based on the potential benefits, risks, probability of occurrence, and severity associated with each consequence. Methods to address these potential consequences and facilitate EV adoption and acceptance will be discussed. As EVs become more prevalent on the roads, it is important to understand different perspectives on EVs and develop educational materials to inform the general public about EVs.

What Do We Know About EV Safety?

Introduction of the Panelist

Raul Arbelaez is vice president of the Insurance Institute for Highway Safety’s Vehicle Research Center. He joined the Institute in 1999 as a research engineer and research interests include occupant injury biomechanics, crash test dummies, vehicle event data recorders, and electric vehicle safety. Raul has been involved in the development of advanced dummy seating procedures, real-world crash investigation studies, and development of several of the Institute’s crashworthiness evaluation programs.

Abstract/Background

An ongoing question about EVs relates to their safety, with issues ranging from crashworthiness to potential fire hazards associated with batteries and the unintended outcomes on roadway infrastructure and partner vehicle protection that will result from a heavier EV fleet. Addressing these concerns requires a comprehensive examination utilizing data from various sources. Insurance loss databases offer insights into the frequency and severity of accidents involving EVs compared to conventional vehicles. Full-vehicle crash tests provide crucial data on EV performance in controlled scenarios, and real-world crash data further enriches this analysis by capturing the complexities of diverse driving conditions and accident scenarios. Results from published and ongoing studies by the Insurance Institute for Highway Safety (IIHS) and other organizations related to vehicle safety will be shared in this panel.