Exploring electric vehicle charging patterns: Mixed usage of charging infrastructure

Transportation Research Part D 79 (2020) 102249

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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Exploring electric vehicle charging patterns: Mixed usage of charging infrastructure

T

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Jae Hyun Leea, , Debapriya Chakrabortyb, Scott J. Hardmanb, Gil Talb a b

Korea Research Institute for Human Settlements, South Korea Plug-In Hybrid & Electric Vehicle Research Center, Institute of Transportation Studies, University of California, Davis, United States

A R T IC LE I N F O

ABS TRA CT

Keywords: Charging behavior Charging locations Electric vehicles Plug-in vehicles

This paper examines the charging behavior of 7,979 plug-in electric vehicle (PEV) owners in California. The study investigates where people charge be it at home, at work, or at public location, and the level of charging they use including level 1, level 2, or DC fast charging. While plug-in behavior can differ among PEV owners based on their travel patterns, preferences, and access to infrastructure studies often make generalizations about charging behavior. In this study, we explore differences in charging behavior among different types of PEV owners based on their use of charging locations and levels, we then identify factors associated with PEV owner’s choice of charging location and charging level. We identified socio-demographic (gender and age), vehicle characteristics, commute behavior, and workplace charging availability as significant factors related to the choice of charging location.

1. Introduction Plug-in-electric vehicles (PEVs), which include Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) are being adopted rapidly as an alternative to internal combustion engine (ICE) vehicles. In contrast to ICE vehicles, PEVs can be refueled (charged) anywhere if an electrical outlet is available. Charging in the U.S. is grouped into three categories based on charging power: Level 1 (L1), Level 2 (L2), and DC Fast. Level 1 provides charging through a 120-volt plug adding 2 to 5 miles of range per hour of charging (1.2–1.8 kW AC), Level 2 charges a PEV through a 240- or 208-volt point adding 10 to 20 miles of range per hour of charging (3.6–22 kW AC), and a DC Fast point can supply power up to 480-volt allowing 50 to 70 miles to be added per 20 min of charging (at 50 kW or more). Although a substantial amount of research has investigated PEV charging behavior and electricity consumption, most of the studies focused on time-of-day dynamics of PEV charging demand like start time, plug-in type, charging time, idle time, or charging location (Hardman et al., 2018). Travel patterns and vehicle driving ranges primarily impact PEV owners’ charging needs. Past studies have identified four main locations at which charging occurs - overnight charging at or near home, at workplaces, at publicly accessible locations like those near grocery stores, shopping malls, and in parking lots; and on travel corridors where drivers stop between their trip origin and destination points (Idaho National Laboratory, 2015; Ji et al., 2015; M. Nicholas et al., 2017; Nicholas and Tal, 2015). Though, multiple studies have tried to identify the optimal location for building infrastructure for PEVs, depending on the source and nature of data (stated or revealed) results can vary substantially (Dong et al., 2014; Ji et al., 2015; Santini et al., 2014; Tal and Dunckley, 2016; Weiller, 2011). Combining stated preference survey with Global Positioning System (GPS) data, Nicholas et al. (2017) found that the ⁎

Corresponding author. E-mail addresses: [email protected] (J.H. Lee), [email protected] (D. Chakraborty), [email protected] (S.J. Hardman), [email protected] (G. Tal). https://doi.org/10.1016/j.trd.2020.102249

1361-9209/ © 2020 Elsevier Ltd. All rights reserved.

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desired location stated by households is usually significantly further away from their home than the actual charging location they use. Studies that investigate the temporal trends in charging behavior also indicate home location charging at the nighttime is the most frequently used charging type. Studies using questionnaire surveys find that consumers are most likely to plug their vehicles in when they arrive at work in the morning, and when they return home in the evening (Axsen et al., 2011; Schäuble et al., 2017). Easy access and flexibility of charging time are the major drivers of home charging which also encourage people to adopt PEVs (Bailey et al., 2015; Nicholas and Tal, 2017; Plötz and Funke, 2017; Skippon and Garwood, 2011; Tal and Dunckley, 2016). In addition to convenience, one factor that drives overnight home charging is the use of time-of-use (TOU) electricity tariffs. A 2016 study in California found that these tariffs, which provide cheaper electricity during the nighttime, encourage consumers to charge their vehicles at home overnight (Tal and Dunckley, 2016). Overall, overnight home charging has the highest share of charging events among all locations- 50% to 80% of charging events for PEVs occur at home (California’s Advanced Clean Cars Midterm Review Appendix G: Plug-in Electric Vehicle In-Use and Charging Data Analysis, 2017; Franke and Krems, 2013). However, this dominance of home charging differs in situations where consumers do not have access to home charging. Workplace or commute location charging is the next most favorable option among PEV owners (Björnsson and Karlsson, 2015; Figenbaum and Kolbenstvedt, 2016; Nicholas and Tal, 2015; Skippon and Garwood, 2011). According to Tal et al. (Nicholas and Tal, 2013) a significant driver for charging at work was employers providing charging for free. Also, workplace charging is more important for BEV owners than PHEV owners who can rely on the ICE driving mode of their car when the battery is depleted (Nicholas and Tal, 2015). Approximately 15–20% of the charging events occur at the workplace for BEV owners. Publicly accessible charging locations in shopping malls or parking lots and those located on travel corridors are the least used charging locations at present. Only approximately 5% of the charging events occur at these locations. In spite of the low share of charging events at these locations, public infrastructure is still required to encourage adoption of the PEVs as it offers a safety net to BEV owners on longer trips(Dong et al., 2014; Morrissey et al., 2016; M. Nicholas et al., 2017; Plötz and Funke, 2017; Tal et al., 2014). In recent years, DC fast charging options are being offered in public charging stations. These chargers are generally more important for long-range BEV owners since they are more likely to use their car for longer trips (Figenbaum and Kolbenstvedt, 2016; Ji et al., 2015; Neaimeh et al., 2017; Nicholas and Tal, 2017; Nicholas et al., 2013). Publicly-available fast charging options can reduce range anxiety and encourage adoption of PEVs. Research on charging levels typically finds that L1 or L2 charging is preferable in locations with long dwell times, such as home or at work and that DC fast charging is used for long distance trips (Dong et al., 2014; Figenbaum and Kolbenstvedt, 2016). L1 charging results in slow charging times, which may not be an issue with small battery BEVs and PHEVs, however it may not be sufficient for longer range BEVs when charging at home, depending on their daily travel patterns (M. A. Nicholas et al., 2017b). Consumers can charge at home, work, and in public locations, they could charge at only one of these locations, some combination of two locations, or all three locations. In this study. we aim to investigating why consumers charge their vehicles at any of the potential locational combinations. Along with the choice to use any combination of charging locations, PEV owners also have a choice of charging levels at some of these locations. To accurately model the effect of statewide or nationwide PEV charging demands on future infrastructure needs and on the power grid, it is important to understand the usage pattern of L1, L2, and DC Fast chargers at relevant locations like home, work, and public. In addition, it is critical to understand the factors driving this charging behavior and choice of charging location. The literature related to charging behavior and use of chargers have often considered the importance of public, workplace, and home infrastructure in isolation. However, in reality the infrastructure is often used in an integrated way with PEV owners plugging in at multiple locations to satisfy their charging needs. We were unable to identify any studies that investigate the combined choice of charging locations. Better understanding of how the charging infrastructure is used by PEV owners and the factors characterizing this behavior will be particularly important when we develop policies for future PEV buyers. It will be possible to forecast better their usage of charging infrastructure based on the charging environment, their demographic characteristics, and travel behavior. Therefore, the primary objective of this paper is to identify the different locational charging patterns of PEV owners, focusing on the usage of different types of chargers at different locations. Subsequently, using a multinomial logit model we explore the effects of socio-demographic, vehicle characteristics, and access to charging facilities on the choice of charging infrastructure. The multinomial model despite the restrictive Independence of Irrelevant Alternatives (IIA) assumption allows us to obtain an approximate estimate of the factors that drive charging behavior among PEV owners. 2. Data The data used in this study is drawn from a cohort survey of PEV owners in California conducted in the years 2016 and 2017. Participants who owned at least one PEV were recruited based on Department of Motor Vehicles (DMV) registration data and the Clean Vehicle Rebate Program (CVRP) database using a random sampling procedure. The response rate for the completed survey was about 15%. There were six categories of questions on: travel behavior, driving behavior, vehicle performance, vehicle characteristics, response to PEV related incentives, and charging behavior. For charging behavior, we asked the respondent to provide 7-days of charging history and answer if their PEV was charged at the following locations with the given types of chargers:

• Level 1 Home • Level 2 Home • Level 1 Work • Level 2 Work • DC Fast charger Work 2

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Table 1 Descriptive statistics of the survey dataset. Income

Age

Education

Number of Vehicles

Types of PEV

Model

Battery Plug-in Hybrid Purchase or Lease Purchased Leased

3,812 4,167

Less than 50 K 50–99 K 100–149 K

208 1,024 1,616

10–19 years old 20–29 years old 30–39 years old

10 321 1,718

High school College Post-graduate

992 3,089 3,867

1 2 3

961 4,131 1,961

4,230 3,749

150–199 K 200–249 K

1,469 973

40–49 years old 50–59 years old

2,067 1,842

Gender Male

5,920

4 5+

652 274

250–299 K

637

60–69 years old

1,344

Female

1,982

590

348 196

533 71

Decline to state Household size

77

Own houses Rent or others

6,707 1,272

400–449 K

148

70–79 years old More than 80 years old Missing

Housing types 1,047 5,472

i3

300–350 K 350–399 K

Number of drivers 1 2

73

1 person

829

3

922

450–499 K More than 500

100 341

2 persons 3 persons 4 persons 5+

3,090 1,454 1,930 675

4 5+

457 80

Detached housing Detached Others

6,479 1,500

500e Bolt EV C-Max Energi e-Golf Fusion Energi

160 748 480 472 377

Leaf Prius Plugin Tesla

1,175 792

Volt Others

1,442 359

1,384

• Level 1 Public, referring to non-home and non-work locations (e.g. public chargers, highway corridor chargers, Tesla superchargers) • Level 2 Public • DC Fast charger Public For each day, a respondent must indicate which of the above option they used. In addition, we also asked them to record whether charging at work was paid or free, the type of electricity plan, and what they estimate is the cost of charging at home, membership of charging network companies, and the characteristics of their workplace and home charging infrastructure. In this study, from the pool of survey respondents we analyze a sub-sample of 7,979 PEV owners who charge at least once during the 7-days for which the charging history has been reported. Considering the market penetration of PEVs, many current owners are still early adopters of the technology. As observed in the case of other technologies, early adopters may have some unique characteristics – age- group, education level, household income, technology awareness among others. Descriptive statistics of the sample of PEV owners analyzed here are presented in Table 1. More than 80% of households in the sample have income higher than the median income in California ($77,359 according to the Census American Community Survey 1-year survey 2017) and the percentage of people with graduate or professional degrees is 48.7% (California statewide 12.3%). Our dataset contains more males who are the primary user of the PEV, and the sample has slightly more BEV owners than PHEV owners. More than 80% of respondents owned homes that are detached units. The sample is dominated by a few PEV models; about 50% of respondents with BEVs have the Chevrolet Bolt, Tesla Model S or the Nissan Leaf, and a considerable number of the PHEV households own the Prius plug-in hybrid or the Chevrolet Volt. Fig. 1 shows the difference in charging behavior between BEV and PHEV users as observed in the data. Overall, BEV owners use Level 2 chargers at home (about 40% of charging events) slightly more than PHEV owners (less than 30%). The frequency of home charging behavior does not appear to be affected by the day of the week. Work charging was significantly reduced during the weekend for both BEV and PHEV users, presumably because few respondents work on the weekends. The number of BEV users who did not charge during a particular day was twice that of the PHEV users. This difference is perhaps due to the longer electric driving ranges of BEVs allowing for multiple days of travel to be completed on one charge. Whereas for PHEV owners, if they wish to drive their vehicle using electric propulsion they will need to charge everyday assuming they have typical commute distances. 3. Methodology First, we classify respondents in our sample into distinct groups based on their choice of charging location in the 7-day period. Second, we explore the relationship between choice of charging location and different PEV models during weekdays and weekends as well as investigate any spatial difference in the choice of charging location. Finally, we use a regression model to identify the characteristics of the charging location groups. At all the stages of analysis, BEV and PHEV owners are studied separately. This is done due to the difference in vehicle technology between BEVs and PHEVs and their charging needs. Also, most BEVs in the market can use DC fast chargers whereas only one PHEV (Mitsubishi Outlander PHEV) can use DC fast charge. This restricts the choice of charging location for PHEV drivers and will impact their charging behavior. 3.1. Locational charging patterns Using information on the choice of charging locations (Home, Work, and Public) over a period of 7- days, the following seven 3

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Fig. 1. Daily charging behavior of BEV and PHEV users - share of charging events reported at home, work, and public locations using level 1, level 2, or DC fast chargers.

groups were identified:

• Home-only: those that only charge at home. • Work-only: those that only charge at work. • Public-only: those that only charge at non-home and non-work locations (e.g. public chargers at charging stations, highway corridor chargers, Tesla superchargers). • Home-work: those that charge at home and at work. • Home-public: those that charge at home and at non-home/non-work locations. • Work-public: those that charge at work and at non-home/non-work locations. • All: those that charge at home, work, and public. 4

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We consider the location of chargers to be based on the perception of the place of the charging event by the survey taker. A home charging event may not in all cases be a private charger in a driveway, though this is likely as most PEV owners’ live-in single-family homes. Other forms of home charging include chargers in private parking lots of multi-unit dwellings or chargers on the street side. Workplace chargers are where PEV buyers park their vehicle when they commute, typically at their workplace, but this could also be at a transit location or nearby parking garage. Public charging is perhaps the difficult to conceptualize. A public charger/charging event for survey takers is one when the vehicle was not plugged-in at home and not at their work. A public charger in a parking garage could be one person’s home charger, another’s work charger, and another’s public charger. This is especially true in urban area with homes, workplaces, shops, restaurants, cafes etc. in close proximity. For the purpose of analysis, public charging events are defined as those that happen at non-home and non-work location/time. If a household reported that they only charged at home on all 7- days, then it is classified as Home-only. But if a household charged at home on 6 days and reported a public charging event on 1 day, it is classified as Home-public. A household that charged their vehicle at home, work, and public locations (even once) during the 7-day period is considered in the All group.

3.2. Descriptive analysis of charging pattern The choice of charging location can differ by the type of PEV, primarily due to vehicle characteristics like electric range. For instance, long-range BEV owners (e.g. Tesla and Chevrolet Bolt) may not need to charge at their workplace or at public locations if they can fully charge the vehicle overnight at home and have a short commute. The spatial characteristics of a PEV owner’s residential location can also play a major role in determining the pattern of use as commute distance and access to public chargers can differ between urban, sub-urban, and rural neighborhoods. For example, households residing and commuting within urban neighborhoods may have greater charging opportunities than households residing in suburban and non-urban areas. Households in suburban and non-urban areas may have to depend more on home-charging. Thus, we use cross-classification tables to examine the relationships between charging behavior and PEV characteristics and the relation between charging patterns and spatial characteristics of the PEV owners’ residential locations.

3.3. Model for choice of charging infrastructure use pattern We use a multinomial logit model to understand the factors driving the group classifications. We divide the sample into two groups (BEV and PHEV owners) and estimate the logit model separately for the two groups. We use effect coding for the nominal dependent variable, 7 types of charging behavior, so that we can estimate parameters in terms of differences from the average and not from a reference category (Vermunt and Magidson, 2015). With effect coding, the probability that a household belongs to category m is given as:

P (y = m|z i ) ⎛ ⎞ ηm | zi = log ⎜ = βm0 + M ' 1/ M ⎟ [ P ( y = m | z )] ∏ i m' = 1 ⎝ ⎠

P

∑ βmp ∗ z ip (1)

p=1

where m denotes a charging group like Home-only or Work-public, p denotes the categories in the dependent variable, M denotes the total number of charging behavior groups such that 1 ≤ m ≤ M, z i are covariates and y is the outcome variable observed in the data. M Note, ∑m = 1 βmp = 0 for 0 ≤ p ≤ P, implying the sum of estimated parameters for each independent variable will be zero when summed over all the categories. As the denominator of the log function in equation (1) shows, with effect coding, the probability of belonging to category m is now compared with the average (geometric mean) of the probabilities of all M categories and not to a reference category. In this way, it is possible to identify the driving factors for all the charging behavior groups. A major drawback of the multinomial logit model is the assumption of Independence of Irrelevant Alternatives (IIA). The IIA assumption is problematic particularly if the model estimates are used to make predictions or explain substitution behavior. Since the objective of this study is to only understand the effects of sociodemographic characteristics, vehicle characteristics, and workplace charging facilities on the pattern of infrastructure use, the multinomial logit model using effect coding is well-suited and the IIA assumption should not have a significant impact on the results i.e. the direction of effect of the factors (Train, 2002). We use the following 25 explanatory variables: household income, education, age, and gender (Female: 1) of the primary driver, usage of PEV within household, detached house ownership (Own detached home: 1, other: 0), ownership of solar panels, number of vehicles in the household, household size, number of drivers in the household, presence of multiple PEVs in the household, vehicle fleet, age of PEV, workplace charger availability (Yes: 1, No: 0), electric range of PEV, free workplace charging (Yes:1, No: 0), having time limitation on workplace charging (Yes: 1, No: 0), number of workplace chargers, frequency of change in parking spots for charging in a month, cost of electricity at home (cents per kWh), charging network membership, commute distance, availability of L1 public chargers within 300 m of residence for PHEV owners, availability of L2 public chargers within 300 m of residence for both samples, availability of DC Fast public chargers within 300 m of residence for BEV owners sample, urban neighborhood (Urban:1, other 0), and Tesla ownership (Yes: 1, No 0). The Tesla ownership variable was only used for the BEV model.

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Fig. 2. Proportion of charging behavior groups by different PEV models.

4. Results 4.1. Descriptive analysis of charging behavior Fig. 2 shows the proportion of respondents in each charging behavior group and their proportion by different PEV models. Overall, the proportion of people who rely only on home charging is more than half of the sample (53%). The second and third largest groups are the people who utilized workplace charging only and those who used public charging facilities together with home charging. These groups account for 16% and 13% of total PEV owners, respectively. In total, 86% of respondents (including Homeonly, Home-work, Home-public, and All) charged from home indicating that home is the most common charging location chosen by PEV owners. The right-hand side of Fig. 2 shows the proportion of charging behavior groups by different PEV models. About half of the respondents only rely on home charging regardless of the PEV model. The proportion of Work-only and Home-work are comparable across most short-range (less than100 miles) BEV owners but there were a greater number of Nissan Leaf and BMW i3 BEV users in Home-public and All groups than other short range BEVs (e.g. Fiat 500e, Volkswagen e-Golf). The next most common location is workplace charging. About 30–40% of BEV owners use workplace charging facilities, with most of them using the latter along with home charging. Less than 30% of PHEV owners use workplace charging, again most of them belonging to the Home-work group. After Home-work the next largest group of workplace charging users is the group that uses all three locations. A smaller group uses a combination of work and other chargers, and another uses only work charging. The public charging groups are less prominent. The most prominent group that uses public is the Home-public group especially for BEV households. The most unique charging behavior was found in Tesla users. More than half of Tesla users belonged to the Home-only group, a rate that is higher than short range BEVs, but comparable to the Chevrolet Bolt which is also a long-range BEV. Another area in which Tesla differed from other BEVs was the Home-public group which was the largest in comparison to all other models of BEVs and PHEVs. This

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Fig. 3. Proportion of usage of different level of chargers in within charging behavior group.

pattern could be a result of the access to free supercharger network enjoyed by Tesla owners (at the time of the survey) or the longer driving ranges of these vehicles which increases the likelihood of long-distance travel requiring public charging. As the survey may have an over-representation of certain groups of vehicle owners, we re-estimate the proportions using weighted data. The weights are calculated using data from the CVRP records. The CVRP dataset contains information of about 200,000 plug-in vehicles that were sold between 2010 and 2017 in California. The purchase year and make of PEVs were used to calculate weights because model information is not available from CVRP dataset. The bottom chart of Fig. 2 shows the weighted proportion of charging behavior groups by different PEV models. They are not significantly different from the unweighted one except the proportion of Home-only group for short-range BEVs (Leaf and i3). A greater number of Leaf users are in the Home-only group of the weighted sample while there are fewer i3 BEV users in the same group. It is a possibility that the charging pattern observed in the weighted sample of BMW i3 users is related to the incentives and free charging provided by BMW in North America (BMW, 2016). Along with charging location we also investigate choice of charging levels – L1, L2, or DC fast charging by different charging behavior groups. Fig. 3 illustrates the average number of PEV charging days using different levels of charging at home, work, and public locations during the weekdays and weekends by different charging behavior groups. Regardless of BEV owners’ charging behavior group L2 is the most frequently used charger at home and workplace while DC fast charger is used when charging in public locations. BEV users’ average number of charging days at home using L1 charger is 0.8 and L2 is 2.7 (Fig. 3a). These values can be interpreted as the proportion of L1 and L2 home chargers at BEV owners home locations because there is typically not more than one type of charger at PEV owners’ homes. Despite BEV owners mostly using L2 chargers, there is still a considerable number of BEV owners who use L1 chargers at home. BEV owners in the Work-only group use mostly L2 charger, but they also use L1 and DC fast chargers. People who rely only on public charging locations mostly use DC fast chargers (about 1.5 days in a week) or L2 chargers (about 0.8 times per week). The Home-work group seems to utilize both charging locations in equal proportion, mainly a L1 charger at home and a L2 charger at work. On the other hand, Home-public or Work-public groups seem to be more dependent on home and workplace chargers (more than 2 days at these chargers) respectively, primarily using L2 chargers at home or work and DC Fast at public locations. The All group primarily utilize L2 chargers at home and workplace and are less dependent on charging at public locations using L2 or DC Fast chargers. Fig. 3b show weekend charging behavior of BEV owners, and they are very similar to the weekdays’ pattern except for the workplace charging. PHEV owners’ charging behavior is illustrated in Fig. 3c and d. PHEV owners tend to charge more often than BEV owners. More than 60% of PHEV owners used L1 chargers at home while their main chargers at a workplace or public locations were L2 chargers. Like BEV users, people in Home-work group utilize both home and workplace charging infrastructure equally, mainly using L1 charger at home and L2 charger at work. PHEV owners in Home-public, Work-public, and All groups tend to mainly use the L1 chargers at home and L2 chargers at work. Like BEV owners, there are no significant differences between weekdays and weekend charging patterns of 7

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Fig. 4. Land use characteristics and charging behavior by different types of land use.

PHEV owners except for less workplace charging. Fig. 4 shows the distribution of the seven charging behavior groups for five different types of built environment (urban core, urban district, urban neighborhood, suburban neighborhood, and non-urban) (Clifton, 2016). This typology of built environments was developed with multiple land use indicators including community design measures (densities of people, jobs, and housings) and regional accessibility measures (level of transit services and job accessibilities). More details can be found in the technical memorandum in 2017 National Household Survey California Add-on. While approximately 50% of the PEV owners in urban neighborhoods only charge at home, the dependence on home charging is more in suburban and non-urban locations. Further, the Work-only and Work-public group is significantly smaller in suburban and non-urban residential areas compared to urban neighborhoods. Here, we only consider residential location information to map the distribution. The build environment of the commute location can also influence the choice of charging location. However, as the median commute distance is 11 miles in the sample, we assume that the urban density of the commute location is not significantly different from residential density and present only the spatial distribution of charging behavior groups by the type of build environment of the residential locations. Fig. 5 shows the distribution of the different behavioral groups in the state of California. Analogous to the location choice pattern based on build environment (Fig. 4), the share of the Home-work, Home-other, Work-only, and Work-other group is closely related to the proximity of the PEV owner’s residential location to major urban/employment centers in the state. Focusing on the Bay area and Southern California, we observe that the share of the Work-only and Work-public group is high in dense urban locations of the Bay area and Southern California like San Francisco, San Jose, and Los Angeles where PEV owners may have higher access to workplace and public charging opportunities. On the other hand, PEV owners in the sub-urban and non-urban areas of the state like the Mendocino county in Northern California or Kern county in the south mostly belonged to the Home-only and Home-public group. 4.2. Logistic regression results 4.2.1. BEV model estimation Table 2 shows the estimated parameters from the multinomial logit model for BEV owners. The model found that the Home-only group are more likely to be high-income, older, and owners of detached houses with solar cells, and have L2 charging facility at home. This group of BEV owners usually don’t have access to workplace charging or membership with any network like ChargePoint or EVgo. They are more likely to have a special EV rate or TOU rate allowing them to pay a lower electricity rate per kWh. The Workonly group tends to have BEVs with shorter range, they are likely to have unlimited free workplace charging, and are more likely to need to swap their vehicles’ parking location while at work. Swapping parking spaces at work allows one charge point to support more than one PEV throughout the day, thus reducing the issues of congestion and increasing the availability of workplace chargers to PEV owners. Unlike the Home-only group, BEV owners in this group are mostly apartment dwellers, renters, or residents of condominiums. However, like the Home-only group, the Work-only group are less likely to have a membership with any charging network. The Public-only group of BEV owners are more likely to be renters, apartment, or condominium residents and have a lower likelihood of having charging facility available at their workplace. The results however show that this group of BEV owners have a higher likelihood of owning a Tesla. Due to their access to Tesla’s supercharger network, this group of BEV owners can use public chargers to satisfy their charging needs. Other potential reasons for belonging to the Public-only charging group are, being renters or residents of condominiums/apartments meaning they are less likely to have chargers at home. The logit model results also show that the BEV owners in this group on average have lower household incomes, though their average annual income of $163,000 is not low 8

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Fig. 5. Distribution of charging behavior groups in California by county.

when compared to the average Californian household. Among the households using multiple locations for vehicle charging, BEV owners in the Home-work group are more likely to be owners of detached houses and have older BEVs with shorter driving ranges. Due to shorter driving ranges, drivers may be more motivated to utilize both L2 chargers at home and chargers available at work. This group of BEV owners is likely to revise their home electricity rate plan as well as use free workplace chargers to lower the operating cost of their vehicle. As the results indicate, they are 9

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Table 2 Multinomial logistic regression results for BEV owners.

Note: the coefficients colored with blue were positively significant at the level of 0.05, and red were negatively related at the level of 0.05.

less likely to be Tesla owners. Also, BEV owners in this group are less likely to have a membership of charging networks restricting them to home and workplace charging. BEV owners in the Home-public group are more likely to have lower income than the average BEV owner. However, they are more likely to be Tesla owners, have solar, and have L2 chargers at home. They are more likely to have membership with a charging network. Workplace charging is less likely to be available to this group. BEV owners in the Work-public group and the Work-only group are similar to each other except for two characteristics. The former is more likely to have membership to charging networks and have a higher likelihood of being apartment dwellers with public charger availability within 300 m of their multi-unit dwelling. Lastly, people who utilized all charging location are more likely to be young, own older BEVs, and have access to chargers at the workplace.

4.2.2. PHEV model estimation Table 3 shows the estimated parameters from the multinomial logit model for PHEV owners. Overall, fewer parameters were significant compared to the BEV model, though the characteristics that were significant are similar to the BEV model. The Home-only group for PHEV owners are more likely to be older, owners of detached house, and who own short range PHEVs (e.g. Toyota Prius Plug-in Hybrid). This group of PHEV owners do not have access to workplace charging facilities and if available it is not free, has time limits, and may have a lower guarantee of finding an available charger. Like BEV owners, if the primary driver of the PHEV is a female then they are more likely to be in the Home-only group. PHEV owners in the Work-only group are also similar to the corresponding group among BEV owners (renters, apartment, and condominium dwellers with access to free or paid workplace charging and no charging limits). However, unlike BEV users, commute distance matters and PHEV owners in this group tend to commute shorter distance than other charging behavior groups. The Public-only group in PHEV owners are less likely to have access to free workplace charging or home L2 chargers. They also tend to use older PHEVs and have a lower number of vehicles in households. As a result, this group of PHEV owners may be more reliant on their PHEV for short- as well as long-trips and thereby are more incentivized to use public chargers to make the maximum use of the electric range of their vehicle. Among PHEV owners who chose to charge their vehicle at multiple locations during the 7-day span, the PHEV owners in the Home-work group are like their BEV counterparts except they tend to use their vehicle for longer commutes. The PHEV owners in the Home-public group are more likely to have L2 chargers at home and have lower electricity rate than the average PHEV owner. This group of PHEV owners are less likely to have access to workplace charging. The Work- public group of PHEV owners are similar to the comparable group of BEV owners. Lastly, the All group is more likely to have young PHEV drivers with access to L2 charger at home as well as chargers in their workplace. In terms of household characteristics, this group of PHEV drivers are more likely to have a larger household size and more vehicles, but a lower number of drivers in the households. Along with L2 charger at home this group of PHEV drivers are more likely to pay lower rate electricity at home. Overall, the results of the two multinomial logit models show that infrastructure location choices are characterized by a range of 10

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Table 3 Multinomial logistic regression for PHEV owners.

Note: the coefficients colored with blue were positively significant at the level of 0.05, and red were negatively related at the level of 0.05.

factors including socio-demographic characteristics like gender, income, and home ownership; vehicle characteristics like range and age of the vehicle; commute distance; electricity rate paid at home; and access to charging facility at home and workplace. Availability of workplace chargers plays an important role in determining the infrastructure use pattern, particularly when free. As the results of the logistic regression show, renters, apartment/condominium dwellers with limited or nonexistent charging at home do use their BEV for commuting when workplace charging is available. Other PEV users with charging facilities at home also use workplace charging infrastructure especially when the residential electricity rate is high or workplace charging is free. Interestingly, commute distance is a significant factor only for PHEV owners. As observed in Table 3, longer commute distance in PHEVs has a positive effect on the mixed use of charging infrastructure for PHEV owners, like Home-Work and Work- Public and a negative effect on Work only charging group. Assuming PHEV owners want to increase their proportion of electric miles, reduce the vehicle operating cost, and reduce tailpipe emissions, they may want to charge their vehicles more often. Consequently, if drivers commute distance is longer they seem more motivated to seek out additional charging opportunities. Multiple case studies have discussed the importance of public infrastructure for residents of multi-unit dwellings (Peterson, 2011)). To control for this effect, we interact dwelling type of respondents i.e. whether the respondent resides in an apartment complex with the availability of L2 public chargers within 300 m of residence for PHEV owners. For BEV owners, we explore the interaction between dwelling type and availability of L2 and DC Fast public chargers within 300 m of residence. We observe that there is no significant effect on the choice of charging location for most of the charging behavior groups. It is significant and positive for only the Work-Public group among BEV owners. Since this group is characterized by BEV owners living in apartments with lower likelihood of access to L2 charger at home but free workplace charging, public chargers near the multi-unit dwelling can play an important role in satisfying their charging needs. Among PHEV owners, availability of L2 chargers within 300 m have a negligible but significant effect for the Work-only (positive effect) and the Home-work group (negative effect). One potential reason for the overall lack of significant effect of public charger availability near multi-unit dwelling can be that BEV and PHEV owners may self-select into apartment complexes where charging infrastructure is available in the complex parking facilities and therefore don’t have to rely on public infrastructure for vehicle charging. We also perform sensitivity analysis to consider the impact of public chargers within 500 m of residence and 1 mile of a residence. We do not find any impact of public infrastructure on the choice of charging location by households. Though the exploratory analysis involving only the spatial distribution of charging location indicated an impact of urban density, when we controlled for all the other factors influencing charging behavior, we did not find a significant effect of urban density on BEV or PHEV owners.1

1

Urban density is controlled using a dummy variable. A more detailed analysis of urban density may change the results. 11

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5. Conclusion In this paper, we used results from a 7-day charging behavior survey to capture variation in charging patterns among PEV owners. Based on the use of different levels of chargers at different locations, we could analyze week-long patterns of charging behavior for BEV and PHEV owners. Although there is negligible daily variation in the charging during weekdays, different charging patterns for weekdays and weekends among BEV and PHEV owners were identified. Using the charging data, we classified survey takers into seven different groups of PEV owners based on their charging patterns. 37.8% of the BEVs and 30.5% of the PHEVs use more than one location and, in many cases, different charging powers over the week. However, both BEV and PHEV owners relied heavily on home charging, with more than half of them only using home chargers. This highlights the findings of previous studies that found home location charging to be the most important and most frequently used location. In terms of choice of the charging infrastructure, PHEV owners were found to be dependent on L1 chargers at home about three times more than BEV owners. This may be due to the current vehicles having smaller capacity batteries that can charge fully overnight on level 1. Using a set of multinomial logit models, we analyzed potential factors that may characterize different charging patterns. Sociodemographic characteristics like household income, home ownership, and gender of the driver, PEV characteristics like electric range and age of the vehicle, travel behavior, electricity cost at home, workplace charging availability and accessibility to L2 charger at home were found to be the key factors influencing the infrastructure use patterns. Commute distance is a crucial factor for mixed usage of charging infrastructure among PHEV owners, indicating that PHEV drivers who reside far from their workplace are more likely to use workplace charging along with other options, allowing them to maximize the use of the electric range of the vehicle. Current PEV infrastructure models are often developed to investigate home, work, or public charging events in isolation. The results here show that the use of these three infrastructure types is interconnected, some owners display more mixed-use of the charging infrastructure than others. This highlights the importance of having an integrated infrastructure investment plan that will account for different locational charging patterns among PEV owners. 5.1. Policy implications The results of this study show that home is the most frequently used charging location for PEV owners. Supporting the development of home location charging should be an important consideration for policymakers, particularly in multi-unit dwellings and those without off street parking. PEV adopters living in apartments are at present mostly dependent on workplace and public infrastructure. As the PEV market moves along the diffusion curve from early adopters living in detached homes with the flexibility to install chargers at home to the early and late majority adopters (Lee et al., 2019) the importance of chargers in multi-unit dwellings will increase. However providing universal access to charging to residents in multi-unit dwelling may be challenging. The results of the logit model show that workplace charging is used more frequently by multi-unit dwelling residents than any other non-home charging location, including DC Fast public charging. In other words, for people without home charging level 2 workplace charging can be a viable alternative. Level 2 work charging is also important for BEV owners who only have level 1 charging at home, reinforcing the importance of this charging location. Development of workplace charging could in some regions enable the grid to align load demand with supply of solar energy from distributed and utility-scale photovoltaic cells. In 2018, California State Government changed the building codes and made solar cells mandatory for all new construction. Utilities have expressed concern about the proposition, particularly in relation to the ‘duck curve’ problem – the imbalance between peak demand and renewable energy production. Considering workplace as a frequently used charging location, using appropriate pricing mechanisms commuters can be incentivized to plug-in their vehicle at a time when electricity production from solar energy is maximum. One interesting result in this study is that more owners of 200-mile plus range BEVs like Tesla BEVs and the Chevrolet Bolt charge only at home. This may have implications for the future development of non-home PEV charging infrastructure. At present infrastructure planning decisions are often made with data from 100 miles BEVs, as they were the dominant PEV in the market. These vehicles are more reliant on work and public chargers. However, in the recent past, all but one of these BEVs has been taken from the market by automakers. Longer range BEVs look set to become the majority of BEVs on the roads in future years and these vehicles may have a reduced or different need for the infrastructure. 5.2. Future research Understanding location-based infrastructure use is insightful for planning purposes. It is also important for researchers to consider charging behavior beyond diurnal trends. As our study shows there are differences in charging behavior during the week and the weekend. In particular, there are differences in the number of workplace charging events during the weekend, which is not surprising. However, we didn’t find a significant difference in DC fast charging during the week and the weekend. One reason could be that BEV owners at present potentially use another vehicle for weekend travel (as they mostly all have an ICE vehicle). In future, more DC fast charging events may emerge during the weekend partially due to longer range BEVs entering the market and because of expanding DC fast charging infrastructure. Understanding this trend will be important for developing PEV charging infrastructure that fits all travel behavior needs, not just the most frequently made trips (e.g. commuting). The results in this study are from self-reported charging behavior of PEV owners in the week prior to taking the survey. As the data is self-reported for a specific 7-day period it is possible that for some respondents it does not fully align with their usual charging behavior. For example, the week prior to taking the survey could have been an anomaly meaning the information we have is not representative of a typical week. However, as the sample is large, we believe the data represents typical charging behavior for PEV 12

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owners on an average. To address this limitation future studies should measure consumers’ actual charging behavior, potentially by using data loggers installed on PEVs. Also, 8.9% of PHEV owners in this study do not charge their vehicles at all. A future study will investigate this behavioral pattern in more detail to understand what factors lead to PHEV owners’ decision to not charge their vehicle. Overall, the classification of PEV owners based on their charging behavior and identification of the characteristics of the mixed users of charging infrastructure lays down the foundation for future research on infrastructure planning for PEV charging, pricing of electricity, and, other policy questions. In the future, we would like to model the choice of charging infrastructure considering the cost of charging and nature of commute trips as factors. We also plan to estimate the effect of associated policies like the promotion of distributed solar on the cost of PEV charging and thereby the choice of charging infrastructure. Author contributions The authors confirm contribution to the paper as follows: study conception and design: Jae Hyun Lee and Gil Tal; data collection: Gil Tal. Author; analysis and interpretation of results: Jae Hyun Lee, Debapriya Chakraborty, Scott Hardman, and Gil Tal; draft manuscript preparation: Jae Hyun Lee, Debapriya Chakraborty, Scott Hardman and Gil Tal. All authors reviewed the results and approved the final version of the manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.trd.2020.102249. References Axsen, J., Kurani, K.S., McCarthy, R., Yang, C., 2011. Plug-in hybrid vehicle GHG impacts in California: Integrating consumer-informed recharge profiles with an electricity-dispatch model. 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