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Review

Adoption and Use of Battery Electric Vehicles Among Older Drivers: A Review and Research Recommendations

by
David W. Eby
*,
Renée M. St. Louis
,
Jennifer S. Zakrajsek
and
Nicole Zanier
Behavioral Sciences Group, University of Michigan Transportation Research Institute (UMTRI), College of Engineering, University of Michigan, 2901 Baxter Rd., Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(7), 2810; https://doi.org/10.3390/su17072810
Submission received: 21 January 2025 / Revised: 12 March 2025 / Accepted: 20 March 2025 / Published: 21 March 2025
(This article belongs to the Special Issue Sustainable Transportation and Traffic Psychology)
Versions Notes

Abstract

In the United States, transportation is the largest contributor to greenhouse gas emissions, with passenger vehicles accounting for the majority. Battery electric vehicles (BEVs) offer a significant opportunity to reduce emissions, as they have fewer emissions related to electricity generation compared to gasoline-powered vehicles. However, the benefits of BEVs are limited by their low adoption rates, particularly among older adults. In 2023, only 9.3% of vehicles on US roads were electric, and older adults (age 65 and above) have the lowest ownership and least interest in purchasing electric vehicles. This review aimed to understand the empirical data on the adoption and use of BEVs among older drivers, identify research gaps, and provide a research agenda to promote BEV use among this demographic for a more sustainable future. The review found that older drivers possess unique perceptions, often seeing more environmental benefits and fewer cost-related barriers than younger drivers, but concerns about charging infrastructure remain a significant obstacle. Notably, there is limited detailed research specific to older adults’ use patterns, charging behaviors, and the potential influence of socioeconomic factors. Future research should consider more nuanced age definitions, mixed-method approaches, and real-world behavioral studies over extended periods. A concerted effort toward understanding and addressing these barriers can inform strategies to increase BEV adoption among older adults, contributing to broader environmental goals. The review proposes a research agenda focused on understanding older adults’ adoption decisions, driving and charging behaviors, and effective training methods to facilitate BEV use.

1. Introduction

An international panel of climate scientists recently concluded that “Human activities, principally through emissions of greenhouse gases, have unequivocally caused global warming” [1] (p. 2). While vital, transportation is the largest contributor of greenhouse gases (GHGs) in the US. In 2020, transportation accounted for about 27% of all GHG emissions, and within the transportation sector, 57% of GHG emissions were from light-duty vehicles such as passenger vehicles [2]. A typical gasoline-powered passenger vehicle releases 4.6 metric tons of carbon dioxide (a GHG) from its tailpipe annually. Even though a battery electric vehicle (BEV) has no tailpipe emissions, there are emissions released in generating the electricity used to charge the battery, but these emissions, depending on the vehicle make/model and where it is located, are about half the amount of emissions from gasoline-powered vehicles [3].
While BEVs represent an opportunity to significantly reduce GHGs, the benefits to the environment and society are limited by the penetration and use of BEVs among the general driving population. Ownership of BEVs in the US is low. In 2023, an estimated 9.3% of vehicles on US roadways were electric [4]. Further, about a third of those who have purchased a BEV will discontinue BEV ownership on their next vehicle purchase [5,6].
Adoption is even lower among older adults. According to a recent Gallup [7] poll, ownership of BEVs in the US is lowest (3%) among older adults (age 65 and older) while other research reports that older adults are the least likely to seriously consider buying a BEV (3%) and 63% of older adults report that they would not buy a BEV [8,9,10,11,12]. The lack of interest in BEVs among older adults represents a significant impediment for increasing the penetration of BEVs in the light duty vehicle market. Older adults currently comprise about 19% of the US population and, in the coming decades, this proportion is projected to increase to more than 25% [13].
There is a significant amount of literature on so-called “green vehicles” or vehicles that produce less GHGs than combustion-engine vehicles (CVs). Table 1 presents a brief explanation for each type of vehicle (definitions are based on [14]). For this paper, we focus on BEVs and PEVs (collectively called BEVs in this paper), which are powered entirely by batteries that are charged by plugging into the electric grid.
The contributions of this review are as follows:
  • To gain a better understanding of the empirical data on the adoption and use of BEVs among older drivers;
  • To identify gaps in the research on the adoption and use of BEVs by older drivers;
  • To provide a research agenda to help promote BEV use among older drivers.

2. Methods

2.1. Selection Criteria

A search was conducted for BEV literature on the adoption and use of BEVs among older drivers. The literature search was bounded by English-language articles published in the last ten years (2014 to present). The following terms were used to search databases:
  • Terms for older driver: Older driver, elderly driver, aging driver, senior driver, older adult driver.
  • Terms for the technology: Electric vehicle, EV, plug-in hybrid electric vehicle, PHEV, hybrid electric vehicle, HEV, plug-in electric vehicle, PEV, battery electric vehicle, BEV, light-duty e-vehicle, LDEV, zero-emission vehicle, advanced transportation technologies.
  • Terms for topics: Barriers to use, facilitators to use, intent to use, prevalence, use of, driving patterns, preferences, perceived safety, adoption, acceptance, attitudes.
The search terms were combined into search strings (using AND, OR, etc.) and truncation and wildcard symbols applied to maximize relevant results.

2.2. Document Searching

The search strings were entered into the following databases: TRID, PsycINFO, ScienceDirect, Google Scholar, Web of Science, ProQuest, and PubMed. The results of each database search were reviewed by a member of the study team. Articles deemed relevant to the research topic were collected and organized using Zotero (n = 128), an online bibliographic tool, and any duplicate articles were subsequently removed.

2.3. Document Review and Synthesis

Final article review was conducted by the primary author following guidelines published by JBI Global and The University of Adelaide [15], which allowed the judgment of article quality within the variety of methodological approaches used in the articles. Documents of sufficient quality were synthesized by the senior authors.

3. Results

3.1. Barriers and Facilitators of BEVs Adoption

Given the low national adoption percentage, and the even lower penetration of BEVs among older adults, it is useful to understand the barriers and facilitators for BEV adoption. Here, we first synthesize the literature specific to older adults and then for the general population.

3.1.1. Older Drivers

There is a significant amount of literature on drivers’ perceptions of BEVs, which clearly play a role in decisions to purchase and use a BEV. However, only a few articles have looked at this topic specifically with regard to older drivers.
The perceptions of barriers and facilitators of BEV adoption among older drivers generally differ from younger drivers, but, in many cases, the effects are complex. For example, a questionnaire study in Germany of 208 drivers assessed the perceived benefits of EVs and CVs [16]. The respondents were divided into three age groups: young (18–40); middle-aged (41–60); and older (61–75). In addition to reporting self-knowledge about BEVs, each respondent answered barrier and benefit questions about both types of powertrain vehicles in five areas: environment, costs, comfort, trust, and technology. The questions were structured as statements that respondents indicated their level of agreement with on a four-point scale (1 = I do not agree at all, 4 = I completely agree). For example, the environmental benefits of BEVs were assessed with the following statement: “I use/would use an e-car, because electric mobility safes [sic] the environment”. The study reported that the older respondents had a similar level of BEV knowledge as the other two age groups. When reporting on the environmental benefits of BEVs, older women and older men reported the highest level of agreement as compared to men and women of the other two age groups. Combining across sex, the respondents in the older age group were similar in their judgments of the environmental benefits as the youngest age group and both reported higher agreement with the environmental benefits of BEVs than the middle-age group. Older respondents also reported higher levels of agreement with statements about the comfort and technology benefits of BEVs compared to the youngest age group. The analysis of barriers found that the only significant difference in agreements on BEV barrier statements among age groups was the cost of BEVs, with the older respondents agreeing less with the statement about BEV cost than younger respondents. No differences in barriers and benefits were found for trust in BEVs.
A study in Sweden of 247 early BEV adopters investigated the main reasons for purchasing a BEV [17]. The respondents were aged 26–85 and were analyzed by 11 age groups—three of which were age 66 or older (66–70, 71–75, and 81–85). The top reason for purchasing a BEV among older adults was the lower impact on the environment, followed by the cost-efficiency of BEVs. Among those aged 66–75, the third most frequently mentioned reason was incentives, whereas among respondents age 81–85, none mentioned incentives, safety, or the vehicle design. About 20% of respondents in the 71–75 age group reported that the design was their main reason for purchasing a BEV, a higher percentage than any other age group. No other results were presented by age group.
Finally, a questionnaire study conducted by Chinese researchers investigated 19 barriers against adopting a BEV among older adults (defined as age 50 and older; [18]). Two populations were included in the study, both selected as convenience samples: 133 Chinese older adults and 119 Russian older adults. The respondents were further divided by sex and analyses were conducted separately for the four groups. The top five barriers for both Chinese males and females were mostly related to costs:
  • Lack of integration of charging station locations in maps and BEV navigation systems.
  • The initial cost of the BEV.
  • The expense associated with the cost of replacing the battery.
  • Maintenance costs associated with routine upkeep.
  • Costs to charge the battery.
The Russian respondents’ top barriers to purchasing a BEV, for both males and females, were all related to the infrastructure:
  • The lack of accessibility and functionality of charging facilities within residential communities.
  • The low quantity and poor coverage area of public charging facilities.
  • The lack of accessibility and functionality of charging facilities at highway service stations.
  • The lack of accessibility and functionality of charging facilities at employment environments, including office buildings.
  • The lack of integration of charging station locations in maps and BEV navigation systems.

3.1.2. General Population

A vast majority of the literature on the perceived barriers and facilitators to the adoption of BEVs focuses on the general population, which often includes older adults, but the results for older adults are not reported separately, e.g., [19,20,21]. A comprehensive review of the worldwide literature on the barriers and facilitators to the adoption of BEVs has been published recently [9]. This review included 537 articles from more than 15 countries published between 2011 and 2022. The synthesis of the results on the barriers and facilitators for BEV adoption were presented in four categories: contextual/infrastructure factors; situational/environmental factors; psychological factors; and demographic factors.
The contextual factor results were subdivided by the charging infrastructure and policy incentives to purchase a BEV [9]. The study found that the charging infrastructure was an overall barrier, with a lack of charging stations identified in 22% of the articles and inadequate staffing of stations mentioned in about 4% of articles. Policy incentives were identified as being overall motivators for the adoption of BEVs, with purchase incentives reported in 18% of articles, followed by tax exemptions (14%), electricity subsidies (10%), reduced charging costs (10%), and free public parking (7%). The situational/environmental factors were presented based on environmental, technological, economic, and marketing categories. For environmental factors, both barriers (reduced air pollution, 23%, reduced energy consumption, 15%) and facilitators (pollution from battery production, 12%, lack of battery recycling facilities, 7%) were identified. The top four technological barriers identified were limited driving range (20%), long charging time (16%), less safety (8%), and low reliability (8%). The top technological facilitators were fast initial acceleration (10%), high speeds (9%), comfort (7%), and less maintenance (7%). There were both economic barriers (high purchase cost, 16%; high battery replacement cost, 8%) and facilitators (low fuel costs, 12%; low maintenance costs, 12%). Marketing-related factors were mostly barriers and included a low-quality aftersales service, lack of BEV dealerships, and lack of consumer knowledge, all of which were identified in 3–4% of articles. Informational campaigns were identified as a facilitator in about 7% of articles.
The review also identified 20 psychological facilitators for the adoption of BEVs [9]. The following eight psychological facilitators were identified in at least 10% of the articles reviewed: previous experience using a BEV, daily experience driving a BEV, attitudes toward environmental benefits of BEVs, the social norm influence of family and friends, peer pressure, a person’s openness to new experiences, and the value of the BEV in being a symbol of better self-image. Finally, several demographic factors related to BEV adoption were identified. The adoption of BEVs was higher among the following:
  • Males.
  • Young and middle-aged drivers.
  • People with high education and income.
  • In large households.
  • In multi-car households.
  • Those with higher driving distances and frequencies.
  • Those with a greater number of years licensed to drive.
  • Homeowners.
  • In households with more licensed drivers.

3.2. Use of BEVs

This review showed that there is a paucity of literature on the use of BEVs among the general population of people who have adopted BEVs. There is also a significant gap in the literature on understanding how older adults use BEVs, including patterns of BEV driving and charging behavior. Here, we review what is known about use of BEVs among general populations of drivers to provide a background for the development of recommendations for older-driver BEV use research.

3.2.1. Driving Patterns

A handful of studies have investigated how much BEVs are driven as compared to CVs, e.g., [8,22,23,24]. Using a variety of approaches to estimate mileage, collectively, these studies suggest that BEVs are driven for fewer miles than CVs. For example, in a large-scale study in the US, researchers analyzed used vehicle sale listings (which included odometer readings) of more than 12 million passenger cars (176,104 were BEVs) and 11 million sport utility vehicles (13,243 were BEVs) across the US from January 2016 to February 2022 [24]. The study found that passenger car CV annual vehicle miles traveled (VMT) averaged 11,642 miles (18,736 km) compared to 7165 miles (11,531 km) for non-Telsa BEVs. The average annual VMT for Teslas (8786 miles; 14,140 km) was higher than for non-Teslas, but considerably lower than for CVs. The results for sport utility vehicles showed similar patterns. The authors provided two potential explanations for the lower VMT for BEVs. The first was that additional analysis suggested that newer model BEVs may be driven more than older models, but the used-vehicle dataset they analyzed did not include new vehicles, potentially lowering the VMT estimates for BEVs. The second explanation was that BEVs might tend to be purchased as secondary vehicles in a household, leading to lower use of the BEVs. There is evidence that, in multi-vehicle households that include a BEV, the BEVs are preferred over CVs for discretionary, shorter-distance trips [25].
Other research has further compared the characteristics of driving trips between BEVs and CVs [26,27,28]. These studies show some important differences. For example, Li, Liu, and Jia [27] examined 2017 trip-level data from the US National Household Travel Survey. They reported that the mean trip distance for CVs (9.01 miles; 14.5 km) was slightly higher than for BEVs (7.95 miles; 12.8 km), but the average trip durations were about the same (19.8 min for CVs; 19.1 min for BEVs). The 85th percentile trip mean distances and durations were nearly identical for CVs and BEVs. The largest differences in trip distance and duration were found for the maximums (1174 miles/1889 km and 1200 min for CVs and 151 miles/243 km and 180 min for BEVs). This result is most likely related to the range limitation for BEVs and the lack of charging station availability. Analyses also compared BEVs and CVs on average daily travel patterns and similar results were found. The average daily distance for CVs (35.4 miles; 57.0 km) was higher than for BEVs (31.9 miles/51.3 km), the average daily durations were about the same (77.7 min for CVs; 76.9 min for BEVs), and the frequency of trips was nearly the same (3.9 for CVs and 4.0 for BEVs). The 85th percentile daily distance was slightly higher for CVs (58.1 miles/93.5 km compared to 50.6 miles/81.4 km), and the daily durations (125 min) and frequencies (6) were identical. Again, the biggest differences were found in the analysis of maximums (1173 miles/1888 km and 1217 min for CVs and 250 miles/402 km and 400 min for BEVs). Finally, an analysis of the trip start hour showed that, compared to CVs, trips using BEVs started more often during the hours of 7–8 a.m. and 5–6 p.m. (typical commuting start times) and were less common between 8 a.m. and 4 p.m. Similar BEV trip start results have been reported among a large sample of Chinese drivers [28].

3.2.2. Charging

Inexorably intertwined with BEV adoption, perceptions, and use is the fact that these vehicles have a limited range and need to be charged by plugging them into the electrical grid. For example, Ashkrof et al. [26] reported that among BEV drivers in the Netherlands, trip route choice was influenced by the availability and characteristics of charging stations and range anxiety (concerns about the BEV battery being depleted before reaching a charging station). As discussed by Ashkrof et al. [26], charging stations can be private/semi-private (e.g., home or workplace) or public (e.g., parking lots) and are also classified as either slow or fast charging. A full charge for a BEV can take 6–8 h at slow-charging stations as compared to 20–40 min at fast-charging stations [29]. None of the literature reviewed on charging behavior focused specifically on older adults, but several articles included general population samples [26,28,30,31].
The time of day and location of when people choose to charge their BEVs shows that charging is related to commuting behavior [28,31]. For example, Meng et al. [28] found that charging start times peaked around 7 am, 1 pm, 5 pm, and after 10 pm—similar to the trip end times for commuting trips. In an extensive review of the literature, Hardman et al. [31] drew a similar conclusion, noting that “…consumers are likely to charge their PEVs when they arrive at work, in public locations in the evening, and when they arrive home in the evening or night-time” (p. 517). Other work has assessed the frequency of using various locations of charging stations [26,30,31]. These studies report the following locations roughly in order of frequency: at home, at or near the work/commute location, public parking areas, highways or other travel corridors with long-distance travel, and public transportation locations (e.g., bus/train stations).
BEV charging style has also been investigated [30,32,33]. The phrase “charging style” refers to the motivations, attitudes, and behavioral styles associated with charging a BEV in normal use [33]. In a study in Germany, the researchers asked 70 participants (mean age 49) to use a BEV provided to them as part of the study [30]. The participants maintained a charging diary over a six-month period and completed periodic questionnaires and in-person interviews [30]. The study found the following:
  • 87% of the participants thought that the charging activity was easy.
  • 78% of the participants were not bothered by the charging times.
  • 71% of the participants preferred home charging over refueling at a petrol station.
  • On average, the participants charged 3.1 times per week.
  • The participants usually charged the BEV when there was significant charge left in the battery, with 66%, on average, charging the BEV when it had at least a 40% state of charge.
More recent work in Australia investigated EV charging styles in a questionnaire administered to 994 drivers in three categories: current EV owners, those considering an EV purchase in the next 5 years, and those considering a purchase in the next 10 years or never [33]. The questionnaire included 10 indicators of charging preferences related to charging attributes (e.g., regular charging versus a variable schedule), battery resource coping strategies (e.g., charging based on battery charge level versus opportunity), and risk propensity (e.g., planning trips versus deciding on the go). EV adoption group, travel-related factors, and demographics (including an older group defined as age 55 and older) were also considered as covariates. Latent class cluster analyses were conducted to differentiate five classes or charging styles. For two of these classes, the percentages of older adults were substantially different to those of the middle-aged or young age groups. Older adults comprised 44% of the cost-oriented deliberators class compared to 20% for the 18–34 year olds and 36% for the 35–54 year olds. This class is defined by people whose charging behavior is motivated primarily by cost savings, investing considerable effort in advanced planning for when and where to charge the BEV. Members of the cost-oriented deliberators class are the highest percentage of homeowners, travel the least distance, and are most likely to be retired males. Alternatively, the older respondents were less commonly found to be in the flexibility seekers class as compared to the other age groups (22% versus 36% and 42%). The respondents in this class charge based on the battery charge level and prioritize convenience over speed and cost. People in this group prefer to charge at home, but they are also likely to seek out fast-charging stations. Flexibility seekers have high incomes and education, are working, and are more likely to have children.

4. Discussion

This review considered the literature on the adoption and use of BEVs among older drivers. The review found that this topic is significantly, and perhaps surprisingly, understudied. Older adults comprise a large proportion of the population of many countries and both the number and proportions are expected to continue to increase in the coming decades [33]. It is also well established that older adult drivers of CVs have different travel patterns, behaviors, and mobility needs than younger drivers (see, e.g., [34,35]). The minimal research reviewed here, specifically focused on older drivers and BEVs, suggests that the adoption and use of BEVs may be different in comparison to younger drivers.
The review found some evidence that older drivers were more greatly motivated by the environmental benefits and less concerned about the cost of BEVs than younger drivers. We also found that some older drivers were motivated to adopt BEVs because of the design, comfort, and technology benefits. Other work found that cost and charging infrastructure were barriers for adoption among older drivers. These findings, however, need to be confirmed with additional research.
Future research on older drivers and BEVs, however, needs to be better designed. Most of the studies reviewed defined age 50 or 60 and older as the older driver group. However, based on travel patterns, functional abilities, and traffic crashes, contemporary aging and transportation researchers define the older age group as starting at age 65 or 70, e.g., [36,37,38,39]. Future studies should also use a mixed-method approach where questionnaire, observational, or other objective data are combined with qualitative methods (e.g., structured interviews or focus groups) to not only determine use and charging patterns, but also to understand the underlying motivations for these behaviors. Finally, as has been found in previous studies of older adults’ use of advanced driver assistance technologies, e.g., [36], it is important to study patterns of use in real-world settings over a relatively long period where natural driving and charging behaviors can occur and change as older adults learn to use the technology [37,40,41].

5. Research Recommendations

Given the scarcity of studies and the limitation of the current research on BEVs and older drivers, a comprehensive research program in this area is warranted. This review suggests several topics in which more and better information is needed. Here, we present a research agenda.

5.1. Adoption of BEVs

As discussed previously, the decision to purchase a BEV is influenced by several perceived barriers and facilitators. Research into this topic among older drivers should be guided by theories of technology adoption such as the extended technology acceptance model [42] that has been used extensively in relation to advanced automotive technology [43,44] and more recently in EVs [45]. There are various versions of this theory, but it posits that use of a new technology is influenced by peoples’ attitudes toward the technology, which, in turn, is influenced by their trust, perceived usefulness, perceived ease of use, perceived safety, and other factors (such as cost and convenience). This framework can be applied to help understand most of the barriers and facilitators for BEV adoption discussed in this review and to increase awareness and acceptance of BEVs among older adults.
The reviewed literature showed that among the general population, BEV adoption was higher among those in large, multi-car households. According to US Census Bureau data [46], 42% of older adults live in single-person households with another 44% living with just a spouse. It is likely that many of these households would have just a single vehicle. How does household composition impact adoption among older adults? Research shows that a higher household income and incentives are related to BEV adoption. While many older adults have had a long time to accumulate wealth, 28% are economically insecure and this percentage is higher among older adults in underserved communities [47]. Research is needed to better understand the transportation needs of economically insecure older adults and how BEVs could best accommodate these needs. Research is also needed to investigate the feasibility of options to appropriately incentivize the economically insecure so that BEV adoption is more equitable. Finally, research is needed to understand how BEV purchase subsidies can be optimized for older drivers.

5.2. BEV Driving Patterns

Research on older CV drivers shows that they tend to drive fewer miles, fewer minutes, and make fewer trips than drivers under the age of 65 [34]. Older CV drivers also begin to change their driving patterns in response to perceived declines in abilities needed for safe driving—an adjustment known as self-regulation (see [48] for a review). Examples of self-regulation include avoiding driving at night, driving in rush hour traffic, driving in bad weather, unprotected left turns, and travel to unknown areas as well as reducing overall travel and traveling closer to home [48]. Will these patterns change when using a BEV, and if so, how and why?
The analyses of trip making patterns reviewed earlier showed that BEV use for general populations was dominated by driver commute patterns. According to the Pew Research Center [49], only about 19% of older adults in the US are employed, and of those, only 62% are employed full-time. How will the lack of regular employment affect both the adoption and use of BEVs? Research should also consider how current CV use patterns among older adults can best be accommodated by BEVs, including an analysis of range anxiety and personal safety while traveling.

5.3. Charging Behaviors

The literature suggests that peoples’ BEV charging behaviors might be characterized by several styles and that older adults may be more likely to seek out less expensive charging options and are more willing to plan their charging behaviors. Charging styles among older adult BEV users should be investigated in more detail, including the influence of employment and economic status. Many public charging stations are privately owned and operated networks, requiring users to have an account to access the network. Further, there are a number of smartphone apps on the market that can be used to locate EV charging stations and obtain other information about the station. While smartphone ownership among older adults has continued to increase over the past decade, currently, about 61% of older adults own a smartphone in the US and only 8% are regular internet users [50]. Because BEVs have a low penetration in the US population, particularly among older adults, the past research on BEV charging behaviors is likely biased toward early technology adopters who would be more likely to own and use smartphone technology. Future research on public BEV charging among older adults needs to address the digital divide [51] experienced by this age group. Research is also needed on how BEV charging infrastructure accessibility can be improved for older drivers.

5.4. Training

It is well established that older drivers have more difficulty learning and using advanced transportation technologies [37,52,53,54]. Research also shows that one of the most frequent methods for learning about automotive technologies among older adults is trial-and-error, and as much as a third of older adults do not use the technologies that are in their vehicles [37,55]. As new and advanced types of vehicles, BEVs have distinct dashboard controls and other operations that will be unfamiliar to older adults. There is urgent need for research on how best to train older adults on how to use and charge BEVs. There has been some success in combining both face-to-face, classroom-type instruction with hands-on demonstration to train older adults about the use of advanced transportation technologies (see, e.g., [56]) and a similar approach may be fruitful for BEVs. Nevertheless, without older adults having good knowledge about the various aspects of BEV ownership, convincing older adults to adopt and use BEVs will remain challenging.

6. Conclusions

This review highlights the significant gap in research regarding the adoption and use of BEVs among older drivers. The growing proportion of older adults, along with distinct travel patterns, mobility needs, and challenges in adapting to new technology, necessitate targeted research to better understand their relationship with BEVs. While initial findings indicate that older drivers are motivated by environmental benefits and certain design features, barriers such as cost, charging infrastructure, and a potential digital divide pose significant challenges. Given the evolving transportation landscape and the complexity of factors influencing BEV adoption, a comprehensive research program tailored to older adults is essential. This program should employ mixed-method approaches (combining both quantitative data, such as questionnaires, and qualitative data, such as structured interviews), longitudinal real-world studies (naturalistic BEV driving studies with older drivers that track changes in use over several months), and training interventions designed specifically for older drivers to ensure that the unique needs and barriers faced by older adults are addressed. Understanding their specific motivations, driving patterns, and training needs will be critical to facilitating successful BEV adoption among this demographic, ultimately contributing to more equitable and sustainable transportation solutions.

7. Study Limitations

The conclusions of this study should be considered within the context of the limitations of the scoping review methodology. While the document search included a wide range of databases, it did not include the gray literature or other non-indexed documents. The review found very few articles specifically on BEVs and older drivers, so some conclusions are based on the data across age groups and what is known about the older driver population in general. Finally, the studies used a wide variety of approaches with differing levels of precision, potentially affecting the strength of the syntheses across these studies.

Author Contributions

Conceptualization, D.W.E. and R.M.S.L.; Methodology, D.W.E., R.M.S.L., J.S.Z. and N.Z.; Validation, D.W.E., R.M.S.L. and J.S.Z.; Formal Analysis, D.W.E., R.M.S.L. and J.S.Z.; Investigation, D.W.E. and R.M.S.L.; Data Curation, N.Z.; Writing—Original Draft Preparation, D.W.E. and R.M.S.L.; Writing—Review and Editing, R.M.S.L., J.S.Z. and N.Z.; Supervision, D.W.E. and R.M.S.L.; Project Administration, D.W.E. and R.M.S.L.; Funding Acquisition, D.W.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a gift from the Luo Pan Foundation (UM # G027109).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BEVBattery electric vehicle
CVCombustion engine vehicle
E-REVExtended-range battery electric vehicle
GHGGreenhouse gas
HEVHybrid electric vehicle
PEVPlug-in electric vehicle
PHEVPlug-in hybrid electric vehicle
TRIDTransport Research International Documentation
UMTRIUniversity of Michigan Transportation Research Institute
USUnited States

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Table 1. Types of electric vehicles.
Table 1. Types of electric vehicles.
NameAbbreviationDescription
Electric vehicleEVAny vehicle that uses electricity for propulsion.
Hybrid electric vehicleHEVA vehicle with both an internal combustion engine and an electric motor powered by a battery. The battery is charged by recovering energy from braking and the combustion engine powertrain.
Plug-in hybrid electric vehiclePHEVA vehicle with both an internal combustion engine and an electric motor powered by a battery. The battery is charged by plugging into the electric grid.
Extended-range battery electric vehicleE-REVA vehicle with a primary electric motor and a secondary internal combustion engine. The secondary powertrain is used to extend the range of the vehicle in case the battery runs out of charge.
Battery electric vehicle/Plug-in electric vehicleBEV/PEVThe powertrain is all-electric powered by a battery that is charged by plugging into the electric grid.
Extended-range battery electric vehicleE-REVA vehicle with a primary electric motor and a secondary internal combustion engine. The secondary powertrain is used to extend the range of the vehicle in case the battery runs out of charge.
Battery electric vehicle/Plug-in electric vehicleBEV/PEVThe powertrain is all-electric powered by a battery that is charged by plugging into the electric grid.
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Eby, D.W.; St. Louis, R.M.; Zakrajsek, J.S.; Zanier, N. Adoption and Use of Battery Electric Vehicles Among Older Drivers: A Review and Research Recommendations. Sustainability 2025, 17, 2810. https://doi.org/10.3390/su17072810

AMA Style

Eby DW, St. Louis RM, Zakrajsek JS, Zanier N. Adoption and Use of Battery Electric Vehicles Among Older Drivers: A Review and Research Recommendations. Sustainability. 2025; 17(7):2810. https://doi.org/10.3390/su17072810

Chicago/Turabian Style

Eby, David W., Renée M. St. Louis, Jennifer S. Zakrajsek, and Nicole Zanier. 2025. "Adoption and Use of Battery Electric Vehicles Among Older Drivers: A Review and Research Recommendations" Sustainability 17, no. 7: 2810. https://doi.org/10.3390/su17072810

APA Style

Eby, D. W., St. Louis, R. M., Zakrajsek, J. S., & Zanier, N. (2025). Adoption and Use of Battery Electric Vehicles Among Older Drivers: A Review and Research Recommendations. Sustainability, 17(7), 2810. https://doi.org/10.3390/su17072810

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