Using GPS-data to determine optimum electric vehicle ranges: A Michigan case study

Transportation Research Part D 78 (2020) 102203

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Using GPS-data to determine optimum electric vehicle ranges: A Michigan case study

T

Christoph J. Meinrenkena,b, Zhenyu Shouc, Xuan Dib,c,

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a b c

Earth Institute, Columbia University, New York, NY 10027, USA Data Science Institute, Columbia University, New York, NY 10027, USA Dep. of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA

ARTICLE INFO

ABSTRACT

Keywords: Electric vehicles Personal transportation GPS data Trip distances All electric range Greenhouse gas emissions Life cycle assessment Electrification of transport

Fuel-switching personal transportation from gasoline to electricity offers many advantages, including lower noise, zero local air pollution, and petroleum-independence. But alleviations of greenhouse gas (GHG) emissions are more nuanced, due to many factors, including the car’s battery range. We use GPS-based trip data to determine use type-specific, GHG-optimized ranges. The dataset comprises 412 cars and 384,869 individual trips in Ann Arbor, Michigan, USA. We use previously developed algorithms to determine driver types, such as using the car to commute or not. Calibrating an existing life cycle GHG model to a forecast, low-carbon grid for Ann Arbor, we find that the optimum range varies not only with the drive train architecture (plugin-hybrid versus battery-only) and charging technology (fast versus slow) but also with the driver type. Across the 108 scenarios we investigated, the range that yields lowest GHG varies from 65 km (55+ year old drivers, ultrafast charging, plugin-hybrid) to 158 km (16–34 year old drivers, overnight charging, battery-only). The optimum GHG reduction that electric cars offer – here conservatively measured versus gasoline-only hybrid cars – is fairly stable, between 29% (16–34 year old drivers, overnight charging, battery-only) and 46% (commuters, ultrafast charging, plugin-hybrid). The electrification of total distances is between 66% and 86%. However, if cars do not have the optimum range, these metrics drop substantially. We conclude that matching the range to drivers’ typical trip distances, charging technology, and drivetrain is a crucial pre-requisite for electric vehicles to achieve their highest potential to reduce GHG emissions in personal transportation.

1. Introduction 1.1. Background and relevance Electric cars offer many straight forward advantages, including lower noise pollution, zero local air pollution, petroleum independence, and acting as distributed electric storage (Bradley and Frank, 2009; Hawkins et al., 2012; Meinrenken and Mehmani, 2019; Zheng et al., 2014a, 2014b, 2015, 2018; Zheng and Meinrenken, 2013; Zhen et al., 2019; Song et al., 2019). Arguably their most important advantage is to offer a path to shift personal transportation from gasoline fuel to a long term, sustainable alternative, namely electricity from renewables or other low net-carbon options (Lackner, 2016; Meinrenken, 2015; van der Giesen et al., 2017;

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Corresponding author. E-mail address: [email protected] (X. Di).

https://doi.org/10.1016/j.trd.2019.102203 Received 28 January 2019; Received in revised form 10 December 2019; Accepted 10 December 2019 1361-9209/ © 2019 Elsevier Ltd. All rights reserved.

Transportation Research Part D 78 (2020) 102203

C.J. Meinrenken, et al.

Meinrenken et al., 2018). However, another benefit of electric vehicles, namely the reduction of GHG emissions when compared to gasoline cars of similar size and performance, has been found to be more nuanced, with studies pointing to reductions in GHG emissions in some circumstances but not in others (Samaras and Meisterling, 2008; Hawkins et al., 2013; Sorrentino et al., 2014). One important factor that has been identified and successfully captured in models early on is the carbon intensity of the electricity used to charge the car’s battery. This intensity is a function of the generation mix in the local grid (e.g., coal, gas, renewables). As a result, some geographic regions may offer GHG advantages of electric cars whereas others do not, as e.g. shown state by state for the US (Anair and Mahmassani, 2012). Another factor that affects GHG reductions is the complex interplay of the vehicle’s range, battery capacity and weight, drive train architecture (pure electric or plug-in hybrid), trip distance distributions, and charging options (Thomas, 2012; Meinrenken and Lackner, 2015). This interplay is the focus of the current study. In the following sections, we briefly review recent progress in these research areas and explain how the current study builds on and advances previous work. 1.2. Prior work on trip distance distributions and relevance to electric vehicle charging A growing number of datasets exist that collect individual travel trajectories. These record users’ travel logs by advanced sensing and communication technologies, such as GPS (Castro et al., 2013; Zheng, 2015; Di et al., 2017), cellphones (Gadziński, 2018), connected vehicles (Shou and Di, 2018), or blue tooth (Yoshimura et al., 2017). These datasets have various applications, including travel recommendations (Zheng and Xie, 2011), user similarity (Xiao et al., 2010; Shou and Di, 2018), drivers’ route preference behavior (Morikawa et al., 2005; Di et al., 2017), transport mode detection (Widhalm et al., 2012; Bantis and Haworth, 2017), temporal and spatial regularity (González et al., 2008), estimating origin-destination demand (Alexander et al., 2015; Toole et al., 2015), inferring transport trip purpose (Alsger et al., 2018), and location prediction (Ying et al., 2010; Ying et al., 2011). Range anxiety, a function of the overall availability and specific siting of charging stations on one hand and of the size of batteries on the other, is often identified as a key issue for wider adoption of electric vehicles because it affects (Coffman et al., 2017). Relevant research on the application of trip distance distributions data specifically to electric vehicles, and in particular battery charging behavior, can be broadly grouped into two categories, neither of which typically includes quantification of resulting GHG emissions. The first category aims to optimize the technological capability, charge time, or location of charging stations, with the aim to improve the penetration and market potential of electric vehicles. As an example of such study, multiday travel data from Seattle, WA was utilized to maximize the electric miles traveled and minimize the number of interrupted trips after electrification (Dong et al., 2014). The same dataset was used to investigate the impact of charging infrastructure deployment on the feasibility of battery electric vehicles (Dong and Lin, 2014). Using the GPS trajectory data collected from the taxi fleet of Changsha, China, Yang et al. developed a data-driven optimization model and disclosed that the utilization of chargers increases and thus the number of chargers at each charging site can be decreased by providing waiting spots (Yang et al., 2017). Data from a large fleet of 11,880 taxis in Beijing, China was used to extract the vehicle travel pattern and thus to capture the charging demand and maximize the amount of vehicle distance traveled on electricity (Shahraki et al., 2015). The same taxi dataset was also adopted to examine public charging opportunities based on the taxi stop events so that the possible sites for charging infrastructures could be evaluated (Cai et al., 2014). New York City taxi data was applied to maximize the portion of client trips that could be carried out by an electric vehicle (Hu et al., 2018). The authors found that by adding 372 additional charging station in the city, more than half of the taxi fleet can be replaced by battery vehicles with a 340–500 km range. Leveraging parking information of more than 30,000 records of personal trips in Seattle, a regression technique was used to predict the parking demand and thus help site the charging locations (Chen et al., 2013). Using the U.S. longdistance travel data, He et al. investigated the optimal locations of U.S. fast charging stations with the objective of maximizing the completion of long-distance trips (He et al., 2019). By coupling mobile phone data of Bay area residents and PEV driver survey data, Xu et al. investigated the impact of the PEV on the power grid and discovered that the peak power can be substantially minimized by shifting the charging session under the constraint of urban mobility (Xu et al., 2018). Zhang et al. developed an optimization model to determine the optimal location of level 3 chargers and the charging capability of each charge station under uncertain demand (Zhang et al., 2017). The US Department of Energy funded “EVProject” offers extensive insight into EV drivers’ charging behavior. For example, most drivers use public charging stations or their workplace (“away-from-home” charging) in addition to charging their vehicles at home, with “away-from-home” charges accounting for 18% of all charge events (Smart and Schey, 2012). The second research category is to evaluate the battery size – in other words the range – of electric vehicles vis-a-vis most drivers' daily travel needs. In one example, one-year driving data collected in the U.S. was used to extract driving patterns and then infer the range requirements. Impacts of several factors, including driving distances per day, maximum travel distances per day, days that require adaptation, segmenting drivers by average daily distance, and time-of-day trip distances were evaluated (Pearre et al., 2011). Using a dataset from Winnipeg, Canada, the authors (Smith et al., 2011) found that battery size can be decreased by 40% if there is opportunity for charging during the day. The effect of driver type diversity of daily vehicle distance traveled is ignored in the aforementioned literature. A mixture distribution which characterizes the daily vehicle distance traveled by a driver was proposed and was shown to improve the feasibility of electric vehicles for various drivers (Li et al., 2016). The availability of charging, be in overnight only or also during the day, is a relevant aspect of all aforementioned studies. For taxis specifically, Yang et al. found that equipping taxis with apps enables drivers to take advantage of some un-used cruising time during the day, in addition to charging only during dwell time (Yang et al., 2016). Earlier work used intra-day travel patterns in California to examine electricity requirements for plug-in hybrids of different, fixed ranges under different charging scenarios, but battery-only cars or resulting GHG emissions were not studied (Kang and Recker, 2009). A similar study on plug-in hybrids, using trip distance data for Toronto, Canada, further modified the vehicle’s energy consumption (kWh/km) based on the type of trip, e.g. slow versus fast driving (Raykin et al., 2012). Using the one-year GPS data of 255 Seattle households, Khan et al. found that a battery electric vehicle with 100 miles of range 2

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would accommodate the needs of 50% of one-vehicle households and 80% of multiple-vehicle households (Khan and Kockelman, 2012). Focusing on the shared autonomous electric vehicles (SAEV), Chen et al. reported that one 80-mile range SAEV can replace 3.7 privately owned vehicles and one 200-mile SAEV can replace 5.5 privately owned vehicle under level II (240-volt AC) charging based on 2009 NHTS trip distance and time-of-day distribution (Chen et al., 2016). More recently, a US nation-wide study which used the US National Household Transportation Survey dataset, but broken down by regions, was carried out for a popular, specific car type, the battery only Nissan Leaf. One of the goals of this study was to determine the portion of distances and travel days that such a vehicle could provide (overnight charging). In this study, too, the car’s electricity consumption per distance was varied depending on the type of trip, but no GHG emissions were quantified (Needell et al., 2016). 1.3. Prior work on life cycle GHG emissions of electric cars A large body of research exists on the various environmental impacts of hybrid and electric vehicles (Hawkins et al., 2012b), including their role vis-à-vis the electric grid (Galus et al., 2012; Waraich et al., 2013) and integration of renewable electricity (Richardson, 2013). With regards specifically to life cycle GHG emissions from hybrid and electric vehicles, the research body has continuously progressed over the past ~10 years (Nealer and Hendrickson, 2015). This includes parameter refinements to better capture emissions from manufacturing the battery (Majeau-Bettez et al., 2011; Kim et al., 2016) or the effect of regional weather on the car’s electricity consumption (Yuksel and Michalek, 2015), as well as investigating vehicles for specific uses such as taxis (Ma et al., 2017) or future pathways of grid carbon intensity (Zhou et al., 2013). Results are commonly expressed as gram CO2eq per average km driven. They include effects of manufacturing components, particularly the battery. This is typically done only for specific battery capacities that reflect currently commercially available vehicles – not with the perspective of designing cars with an optimum range that would minimize overall GHG emissions. Typical findings are that life cycle GHG of electric vehicles are approximately 35–45% below those of traditional gasoline vehicles of same size and performance (typical grid generation mix). However, this advantage vanishes when electric cars are compared to gasoline-only hybrids (which use electric propulsion internally but generate the electricity themselves), except in scenarios when the electricity to charge the vehicle is low-carbon such as with home solar or future, renewables-heavy grids (Meinrenken and Lackner, 2015). 1.4. Overview and differentiation of current study As shown with above examples, research on using trip distance data for electric vehicles is not yet well integrated. Some studies focus on technical feasibility and range requirements to enable their adoption, whereas other, typically separate studies focus on GHG emissions. In contrast, the integration of life cycle GHG models with actual, real life driving patterns and the consideration of range and charging options is less well researched. Interesting exceptions include, e.g., the detailed tracking of GHG of a single car in real life driving after having been converted from using gasoline to electricity (Helmers et al., 2017). In previous work, we developed an analytic framework for quantifying relative GHG emissions of electric versus gasoline cars based on modified utility factors that capture the breakdown of distances traveled on electricity versus gasoline, and applied this framework to US national average trip distance distributions and overnight charging (Meinrenken and Lackner, 2011, 2012, 2014, 2015). Said framework includes the principal elements of our approach, namely, using trip pattern data to determine cumulative between-charge distances and then applying these between-charge distances to a life cycle GHG model of electric vehicles, in order to determine optimum battery capacities. However, the framework was applied only to survey-based, US national average trip patterns and to only one charging technology (overnight charging at home). Here, we present the use of GPS-based, regional, personal trip distance data to determine drive use type-specific, GHG-optimized AERs for electric vehicles. Calibrating a previously developed vehicle GHG model to a forecast low-carbon grid for Ann Arbor, MI (USA), we investigate a total of 108 different scenarios: 9 driver use types (as a proof of concept, chosen here via demographics as well as via location pattern detection we previously developed), further broken down by 6 charging technologies and 2 electric drive train architectures. For each of these 108 scenarios, 3 separate metrics are quantified which are relevant to consumers, policy makers, and car manufactures: (i) The optimum range that the electric vehicle needs to have in order to achieve maximum GHG emission reduction; (ii) the magnitude of this reduction (here compared to a baseline of gasoline-only hybrids); and (iii) the portion of total distance traveled that is fuel-switched from gasoline to electricity. 2. Methods 2.1. Distribution of between charge distances (DBCD) for six charging technologies The concept of cumulative between-charge distances (BCD) and their role for the utility factors of plug-in hybrids versus batteryonly cars has been introduced previously, but only for overnight charging (Meinrenken and Lackner, 2015). Briefly, if for example a driver completes 3 consecutive trips (10 km to the grocery store, from there 5 km onwards to the gym, and then 15 km back home) – but only after the third trip is the car parked long enough to allow re-charging the battery back to a full charge – then the respective BCD is (10 + 5 + 15) km = 30 km. Since the dataset used in this study (Section 2.2) includes the start and end times of every trip, we can determine which consecutive individual trips – depending on the required charging times – should be combined to a single BCD and when a next, separate BCD should be started. As shown for overnight charging (Meinrenken and Lackner, 2015), the result is a vector of thousands of BCDs, whose cumulative distance is the same for all charging technologies but whose number of separate BCDs 3

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is the higher (and therefore average BCD the lower), the less amount of time is required for the vehicle to charge to a full battery. We refer to such vector as a distribution of between charge distances (DBCD). This terminology is chosen in order to differentiate it from the more commonly used “trip pattern”, which usually refers to all individual trips, i.e. without aggregation to BCDs. The application of this concept to the dataset used in this study is illustrated in Section 2.2.3 and examples of DBCDs from our dataset are shown in Section 2.2.4. Previous studies of analyzing trip patterns vis-à-vis an electric vehicle’s required electric range have sometimes assumed so called “daily” (or “once-a-day”) charging and thus referred to trip patterns organized into “vehicle days”, e.g. (Meinrenken and Lackner, 2015; Needell et al., 2016). This assumes that a car can be fully charged only once a day, usually overnight at the driver’s home. Consequently, this approach assumes that all trips carried out within the same 24 h period need to be manageable on a single charge. Otherwise the electric car cannot be used that day at all. In other words, in this charging scenario, a possible use of the car for some trips, e.g., in the morning but not in the afternoon, is considered unlikely because of the substantially higher planning logistics required of drivers to ensure the electric car makes it back home for re-charging at all. However, battery and charging technology in recent years has improved immensely, now offering 45 min ultrafast charging in many locations outside people’s homes (Tesla, 2019). This provides the option to charge the car multiple times throughout the day (e.g., at home, at employer-provided, or public charging stations), thus significantly shortening BCDs and alleviating range constraints. Studies have shown that most drivers do indeed use such “away-from-home” charging in addition to home charging (Smart and Schey, 2012). To reflect these improvements, and similar to some previous studies (Kang and Recker, 2009; Yang et al., 2016), our framework considers 6 different charging technologies which were parameterized according to recent industry trends (Table 1). 2.2. Description of trip dataset used for case study 2.2.1. Safety Pilot data To test the operation of connected vehicles under real-world conditions and to understand real driver’s behavior with the connected vehicle technologies, the University of Michigan Transportation Research Institute (UMTRI) and the city of Ann Arbor initiated a scaledown version of the future environment where all vehicles are connected (i.e., the Safety Pilot project) in fall 2012 (safetypilot.umtri.umich.edu). In the project, each participant vehicle features a wireless GPS-enabled device, which cannot only broadcast the basic safety message (BSM), including real-time longitude/latitude, speed, and timestamp of the host vehicle to nearby vehicles and surrounding infrastructures, but also records its own BSMs at a sampling frequency of 10 Hz. Till now, the project has collected more than 2800 vehicles' GPS trajectory data, including over 4 mill. Individual trips, 40 mill. km driven, and 63 billion BSMs. Considering that the purpose of the present study is to investigate the impact of the demographic features of the travelers on their choice of electric vehicles (range and drive train architecture), the traveling distance of each trip instead of the detailed GPS trajectory of each trip is the focus of this study. Further, noticing the fact that the number of active participants reached its maximum in 2013, we use a 12-month data window collected during February 1st, 2013 to January 31st, 2014. An example of the data is shown in Table 2. 2.2.2. Demographic information survey 515 responses were collected from questionnaires sent out to all Safety Pilot participants. From these 515 responses, 103 did not have sufficient data in the research period. The demographic features and respective summary statistics of the remaining 412 participants are listed in Table 3. 2.2.3. Process to determine DBCDs from individual trips Following the approach suggested by Yu et al., trips are removed from the dataset if their duration is less than 1 min or their distance is less than 500 m (Yu et al., 2019). This step was taken to account for possible inaccuracies in GPS-based trip data. Possible reasons for such inaccuracies include poor satellite connection (especially in business areas where many high-rise buildings are located) or occasional GPS device malfunction. As a further precaution, we also checked whether any individual trips in the dataset are above 1000 km. Such distance – if taken as a single, continuous trip – would take on the order of 10 h to complete and would therefore have suggested possible inaccuracies in the dataset. However, the longest individual trips in the dataset are around 500 km, i.e., well within a realistic range. Below, we use a small sample of 8 trips to illustrate how to calculate the BCDs traveled by each individual car under different charging scenarios (Table 4). The participant visited 5 places on 2013-03-01. We further assume that the first trip of this participant on 2013-03-02 is scheduled after 3:05 AM (i.e., at least 8 h after the last trip in this sequence ended). The process for determining the BCD for overnight and for 8hour charging (Table 1) is as follows:

• For overnight charging (typically to occur at home) we simply sum up all the distances on this day and obtain BCD = 84 km • For 8-h charging at either work or home, because in this example there is no 8-h break between any two consecutive trips, the overnight

required BCD if only 8-hour charging were available is again BCD8

hour

= 84 km

For the 3.5 h and 45 min charging technology, the process is similar. However, the BCDs are reduced, in order to account for the charge regained during shorter breaks via topping of the battery which is likely to occur when drivers park their cars in-between trips 4

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Table 1 Overview of 6 charging technologies considered in this study, along with technological implementation aspects. Typical charge times and commercial implementations are based on publicly available, commercial information such as spec sheets (Clippercreek, 2018; PlugInAmerica, 2018; ChargePoint, 2019; Nelder, 2019; Tesla, 2019). “Top-off” charging, included in some scenarios, is explained in more detail in Section 2.2.3. Name in current study

Relevant commercial implementation

Minimum required time between two consecutive trips

Overnight (aka “daily” charging)

Level 1 (low voltage AC, using the car’s onboard AC/DC converter) or Level 2 (high voltage AC, requiring a dedicated AC/DC converter, e.g. in someone’s garage)

N/a (implement as “start every morning with full charge”, i.e. all trips on the same calendar day (here: 0–24 h local time) are aggregated into single BCD)

Level 1 or 2

8 h for charge to full, and location must be either at user’s place of employment or at home (Table 5)

8h

3.5 h with 50% topoff chance

Level 2

3.5 h (regardless of location) for charge to full, but if break is shorter, “top-off” charge occurs at 50% of times when car is parked even briefly

Practical considerations charging location: Home • Assumed longer reflective of technological • No capability, but include as conservative

• • • • • • •

•

45 min with 50% top-off chance

Ultrafast charging (aka “DC charging”, “super charging”)

45 min (regardless of location) for charge to full, but partial “top-off” charge occurs at 50% of times when car is parked even briefly

• • •

3.5 h with 100% top-off chance

Level 2

3.5 h (regardless of location) for charge to full, but partial “top-off” charge occurs at 100% of times when car is parked even briefly

•

baseline to preserve comparability to previous studies For level 1, charge times vary between 5 and 20 h, depending on battery size. But majority of cases will be able to get to a full charge “overnight” Disadvantage for “street parkers” Charging location: Home or work Typically used for charging at home or at work (using car’s built in Level 1 charger) or using an employer-provided Level 2 charger Note this also captures the scenario that households with multiple cars use an electric vehicle for some e.g., morning trips, and then re-charge the car at home while using a 2nd vehicle (battery or gasoline) for subsequent trips of the same day Charging location: Public charging stations at parking sites as well as work or home Top-off: Whenever the car is parked inbetween trips, there is a 50% chance that the driver will do a “top-off” charge (i.e., not always to full). 50% occurrence of such topoff was chosen in line with recent studies showing that about half of all cars parked at charging stations do not actually charge their battery (Desai et al., 2018) If a driver does choose to top-off, the state of charge (SOC) at which the car will start the next trip is calculated by accounting for the SOC at which the last trip ended, the duration of the break, and the charge rate during top-off (see examples in Section 2.2.3). The charge rate will vary with the charging station power, the battery’s state of charge, and the electricity consumption of the vehicle. As an approximation, the charge rate in this study is assumed constant, equivalent to a gain in range of 25 miles per hour of charging (0.6704 km/min.), a typical value in accordance with industry benchmarks for Level 2 chargers (ChargePoint, 2019) Charging location: Public charging stations at parking sites as well as work or home Technologies are evolving, 80% of charge in 30 min are already possible and even faster rates are under development Top-off: Same as previous, but with a gain of 100 miles per hour (2.6817 km/min.) to reflect the typically much larger power delivery of DC chargers (ChargePoint, 2019) Same as scenario “3.5 h with 50% top-off chance”, however the driver chooses a top-off at 100% of parking events inbetween trips, assuming a future much higher penetration of charging stations and 100% use of the parking time for charging

(continued on next page)

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Table 1 (continued) Name in current study

Relevant commercial implementation

Minimum required time between two consecutive trips

45 min with 100% top-off chance

Ultrafast charging (aka “DC charging”, “super charging”)

45 min (regardless of location) for charge to full, but partial “top-off” charge occurs at 100% of times when car is parked even briefly

Practical considerations as scenario “45 min with 50% top• Same off chance”, however the driver chooses a top-off at 100% of parking events inbetween trips, assuming a future much higher penetration of charging stations and 100% use of the parking time for charging

Table 2 Sample of data of Ann Arbor case study data used in current work (single car, excerpt of 5 consecutive trips). Dev. ID

Trip ID

Start Local Time

End Local Time

Durat-ion (s)

Distan-ce (m)

First Lat.

First Long.

Last Lat.

Last Long.

… 3180 3180 3180 3180 3180 …

… 659,691 659,692 659,693 659,694 659,695 …

… 02/01/2013 02/01/2013 02/01/2013 02/01/2013 02/01/2013 …

… 02/01/2013 02/01/2013 02/01/2013 02/01/2013 02/01/2013 …

… 1072.2 21.5 123.7 144.0 1029.6 …

… 13321.61 0.51 1578.17 1736.85 13200.30 …

… 42.25124 42.32053 42.32054 42.31172 42.32051 …

… −83.63583 −83.68148 −83.68147 −83.67463 −83.68106 …

… 42.32053 42.32054 42.31172 42.32051 42.25121 …

… −83.68148 −83.68147 −83.67463 −83.68106 −83.63580 …

8:44:43 AM 9:03:25 AM 11:51:37 AM 12:55:11 PM 5:05:49 PM

9:02:35 AM 9:03:47 AM 11:53:41 AM 12:57:35 PM 5:22:59 PM

Table 3 Demographic features in Safety Pilot that were used in current study. Feature

Level

Explanation

Frequency

Percent

Gender

1 2

Male Female

129 283

31.3 68.7

Number of kids under 18

1 2

0 1+

265 147

64.3 35.7

Age

1 2 3

16–34 35–54 55+

40 197 175

9.7 47.8 42.5

Employment status

1 2 3

Not employed Full-time Part-time

90 270 52

21.8 65.5 12.7

Table 4 Illustration of how cumulative, between charge distances (BCD) were calculated based on trip data. Trip ID

Ori-gin

Destin-ation

Starting time

Ending time

Distance of trip [km]

Break prior to trip

1 2 3 4 5 6 7 8 9

P1 P3 P1 P2 P4 P2 P3 P5 P1

P3 P1 P2 P4 P2 P3 P5 P1 …

07:15:00 AM 07:30:30 AM 08:30:30 AM 1:00:30 PM 1:22:30 PM 6:00:30 PM 6:30:30 PM 6:55:30 PM …

07:25:00 AM 07:42:00 AM 08:55:00 AM 1:08:00 PM 1:29:30 PM 6:25:00 PM 6:40:30 PM 7:05:00 PM …

6 6 20 4 4 22 10 12 …

> =8 h 5.5 min. 48.5 min. 4 h 5.5 min 14.5 min. 4 h 31 min 5.5 min 15.0 min > =8 h

at public parking stations (Table 1). We use the terminology “topping off” to indicate that during these shorter breaks batteries will get some charge, however not always enough to re-gain full SOC. The reduced BCDs are determined probabilistically by simulating via a Monte Carlo simulation whether in any given break between trips a driver chooses to top off or not (in line with empirical evidence that drivers may or may not make use of a top-off option during every break; Table 1)). The chance for such “top-off” charging to occur at all is 50% [100%] depending on the scenario. If it does occur, then the state of charge (SOC) at which the car will start the next trip is calculated from the SOC at which the last trip ended, the duration of the break, and the charge rate during top-off (Table 1), as illustrated in the examples below. The Monte Carlo simulation converged to a sampling uncertainty of less than ± 0.5 km for all BCDs, which typically required 500–1000 runs (driver type dependent). Again using above 8 example trips, the process is as follows: 6

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• For 3.5-h charging, there are two breaks in the 8 example trips that are longer than 3.5 h: Between trips 3 and 4 and between trips

•

5 and 6. Therefore, the 8 trips are split into three separate BCDs, the first BCD consisting of trips 1, 2, and 3, the 2nd BCD of trips 4 and 5, and the 3rd BCD of trips 6, 7, and 8. The 1st BCD is determined as follows: Trip 1 starts with a full battery. Trip 1 means that BCD is at least 6 km. Trip 2 would add another 6 km. However, in the event that a top-off does occur (at a rate of 0.6704 km/min), the car will regain 3.7 km of range before trip 2 starts (but in this case not to a full battery), meaning that trip 2 requires only an additional 2.3 km. In other words, BCD (or the required battery range to make trip 1 and trip 2) would be 8.3 km. If during the break before trip 3, there is no top-off, then trip 3 would require additional 20 km, thus bringing the final BCD to 28.3 km. If, however, a top-off before trip 3 does occur, then in this case the time of the 48.5 min. break would be long enough to re-charge the battery (at a rate of 0.6704 km/min) back to full (because only 8.3 km charge equivalency has been used so far). In such case, the final BCD would simply be equal to the 20 km needed for the last trip (the maximum of 8.3 and 20 km). The second and third BCDs are determined in the same fashion For 45 min charging, there are three breaks which are longer than 45 min, the break between trips 2 and 3, the break between trips 3 and 4, and the break between trips 5 and 6. Therefore, the 8 trips in the example will create 4 separate BCDs, with the 1st BCD consisting of trips 1 and 2, and so forth. The 1st BCD is determined as follows: Trip 1 starts with a full battery. Trip 1 means that BCD is at least 6 km. If no top-off occurs, trip 2 would add another 6 km, bringing the final BCD for trips 1 and 2 to 12 km. However, in the event that a top-off does occur (at a rate of 2.6817 km/min), then in this case car will regain all 6 km of range before trip 2 starts, meaning that the final BCD for trips 1 and 2 would be 6 km. The other 3 BCDs are determined in the same fashion

2.2.4. Examples of DBCDs Because in this study we evaluate 9 driver use types and 6 charging technologies, the underlying dataset yields 54 separate DBCD distributions. Examples are shown in Fig. 1. Boxplots of all 54 distributions are presented in Results. 2.3. Differentiating driver use types 2.3.1. Overview of 9 driver use types To assess the effect of various trip distance distributions on the optimum vehicle range, we split the drivers, or more specifically the cars, in the dataset into 9 driver use types. Table 5 gives an overview of these cases, and details and algorithms to determine them are explained in subsequent sections. The 9 driver use types are chosen as proof-of concept-examples to illustrate how different drivers require different optimum battery ranges, whether differentiated by driver demographics (e.g., by age) on one hand, or by use on the other (e.g., use includes commuting or not). The 9 driver use types are not mutually exclusive. More granular subgroups, e.g., shuttling kids|male|35–54 years, are possible in principle, but not yet included in our current work because the sample size of trips would be too small to yield robust results for achievable GHG savings and optimum ranges. 2.3.2. Using DBSCAN and inferring commute versus no-commute driver type Based on preliminary tests on the data, we found that the survey-based employment status of a driver can be further adjusted based on the driver's trip history. The underlying concept here is that in regions with partial public transport, such as Ann Arbor, being employed does not mean that one uses a personal vehicle to commute to/from work. Therefore, we use map data in order to select only those drivers for the respective driver use type whose car is actually used for commuting. A brief description of the framework of inferring the traveler's employment status is presented below. A detailed version can be found in Shou and Di (2018). Using DBSCAN: The origins and destinations are the stay points where a traveler performs some type of activities. Considering the fact that most travelers have a limited number of motifs (Schneider et al., 2013) and go to several frequently visited places (Shou

Fig. 1. DBCD distributions, here plotted as normalized probability density functions (pdf) of between charge distances (BCDs), for a selection of the 54 different DBCDs evaluated in this study. Left panel: Driver type = “used for shuttling kids”, for 6 different charging technologies. Right panel: Charging technology = “overnight”, for 3 different driver use types. For charging technologies, see Section 2.1. For driver use types, see Section 2.3. 7

8

Drivers whose trips frequently included driving to/from their place of employment All other drivers n/a n/a n.a n/a n/a All drivers (not split into types) Inferred via combination with map data Survey Survey Survey Survey Survey n/a

Inferred via combination with map data (see algorithms detailed below)

All other drivers

NOT used for shuttling kids Use incl. commuting

NOT used for commuting Male drivers Female drivers 16–34 yr old drivers 35–54 yr old drivers 55+ yr old drivers Entire dataset

Combination: ~50% of all drivers who responded in the survey that they have children could also be shown via parcel land use data (DoT, 2018) to frequently drive to institutional places of instruction such as schools. However, all drivers who reported having children in the survey were included in this group since having children also affects shopping behavior and driving to e.g., sport complexes which are not scanned for in the parcel land use data. Combination (see above)

Drivers with children who use their cars partially for childrenspecific trips such as shuttling them to school, kindergarten, or sport events

Use includes shuttling kids

Identification method

Description

Driver use type

Table 5 Overview of the 9 driver use types differentiated in this study.

203 129 283 40 197 175 412

209

265

147

Number of drivers (i.e., cars)

181,904 121,632 263,237 30,251 188,324 166,294 384,869

202,965

230,246

154,623

Number of individual trips

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and Di, 2018), clustering the stay points to several clusters will disclose the traveler's major frequently visited places. The density based spatial clustering of applications with noise method (DBSCAN) is used in this work (Ester et al., 1996). In DBSCAN, there are two parameters, namely, eps (i.e., a distance threshold) and minPoints (i.e., minimum number of points). The basic idea of DBSCAN is to randomly pick a point from the pool of stay points. A cluster is formed as long as there are more than minPoints points located within the distance of eps from the selected point. The newly formed cluster will then expand by checking the other points within the cluster. The detailed procedure of applying DBSCAN to the current dataset has been previously discussed (Shou and Di, 2018). To implement the above DBSCAN algorithm, the package “sklearn.cluster.DBSCAN” in Python ® (Pedregosa et al., 2011) is utilized. The parameters were eps = 300 m and minPoints = Number of total stay points/15. Inferring commute vs. no-commute driver type: After applying DBSCAN to one driver’s trip history, the driver’s clusters are obtained. Each driver’s daily activity sequence can then be formed by a list of timestamped cluster indices. Obviously, the occurrence of home is likely the most frequent, for usually one will stay at home overnight. For a driver who uses her vehicle to commute to work place on weekdays, it is very likely that the driver will park her vehicle at a parking spot near her work place during her working time. Based on these heuristics, we use the following algorithm (Algorithm 1) to infer home and work place of each driver and thus infer if the driver uses her vehicle to commute to her work place (Shou and Di, 2018). The algorithm is taken from (Shou and Di, 2018) for the self-explanatory purpose. Algorithm 1 (Inference of commute versus no-commute driver type). 1. 2. 3. 4. 5.

Input: one driver’s N activity sequences per day, denoted by cluster indices Divide 24 h into a 24 one-hour time intervals n=1 repeat Take the nth daily activity sequence

6. Form the nth 24-dimensional daily activity vector with each component denoting the dominant activity for the corresponding time interval, denoted as dn 7. n = n + 1 8. until n exceeds N 9. initialize a daily activity vector with 24 dimensions, avg_activity 10. h = 1 11. repeat 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24.

Find the dominant activity across all daily activity vectors, dn s for the hth time interval and put it into the hth component ofavg_activity h=h+1 until h exceeds 24 Find element w that occurs > = 6 times inavg_activity[9: 17] Find element h that occurs at > = 8 times in avg_activity[1: 8] Uavg_activity[18: 24] if w & h are identical then driver type = no-commute home = h else driver type = commute home = h work place return driver type, home, work place (if applicable)

2.4. Life cycle GHG emission model 2.4.1. Features We use a previously developed model that quantifies the life cycle-based GHG emissions of electric cars, depending on grid carbon intensity and trip patterns (Meinrenken and Lackner, 2015). The model quantifies emissions for 3 different drive train architectures that are considered in the present study. The unique properties of these 3 architectures, from a driver perspective, for trip distance distributions, and for resulting emissions, are explained in detail in Results. The three drive train architectures are:

• Battery-only electric vehicle (BEV), i.e. a car such as the commercially available Nissan Leaf or Tesla Model S which requires to be fueled by electricity. Once the battery reaches its lowest charge state, the car needs to be re-charged (see charging scenarios). • Gasoline-only hybrid electric vehicle (HEV), i.e. a car with an internal combustion engine that is fueled by gasoline/Diesel but

•

that also employs an electric motor and small “peak” battery for propulsion and to store electricity from re-generative breaking, thus achieving significantly higher fuel economy (km traveled per liter of fuel) than its conventional counterpart with only an internal combustion engine. Our mathematical model assumes a series-hybrid rather than a parallel-hybrid drivetrain, because the resulting similarity to BEVs with regards to the energy flow makes an apples-to-apples comparison (same size and performance) more straightforward. While, in theory, series-hybrids have a somewhat better fuel economy than parallel hybrids (Campanari et al., 2009; van Vliet et al., 2010), in practice this is not always the case (fueleconomy.gov; accessed on 20 January 2019). Therefore, the results for GHG emissions for HEVs in this study are equally valid for series or parallel hybrid architecture. Plug-in hybrid vehicle (PHV), i.e., a car such as the commercially available GM Volt, which usually operates as a BEV for 9

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distances up to the all-electric range (or AER). When the battery reaches its lowest state of charge, the trip can simply continue, without interruption, as an HEV (a property sometimes referred to as “range extension”). Derivation and features of the model have been explained previously (Meinrenken and Lackner, 2015). Briefly, it offers the following features, all of them relevant to the current study:

• All GHG emissions from driving the vehicle, including upstream and point emissions from the “fuel” (electricity and/or gasoline) are considered. • In addition, life cycle emissions from manufacturing the battery, varied by range and thus size of the battery, are considered as • •

•

well. Emissions from manufacturing and de-commissioning the vehicles (everything but the battery) are typically small in comparison to driving-related emissions. They are also almost identical for the three above drive train architectures, and therefore excluded from the model. The vehicle’s fuel economy is modelled dynamically, specifically including the increasing weight effect of larger batteries (for larger AER). Relative GHG emission comparisons between drive train architectures (e.g., %reduction of a BEV versus an HEV, per km) are valid for any two vehicles of same size and performance. Therefore, results presented in this study for optimum AER, resulting GHG reduction, and portions of travel distances electrified, are valid irrespective of car size/performance. This remains true if a mix of different sizes are compared. Consider for example that a large fleet of HEVs, ranging from small/light to large and heavy is replaced with PHVs. Results presented in this study will hold, as long as the mix of small versus large HEV is replaced with the same mix of PHV vehicles. This is crucial for our study, since we do not consider the size/performance of each car and aim for results that are relevant for policy makers and consumers irrespective of current or future fleet characteristics or fuel economy regulations. The model captures GHG effects of combined fuel economies (from driving partially on electricity and partially on gasoline) by employing different utility factors (one for PHVs and one for BEVs) which are determined, for each driver type and charging technology separately, via the DBCD (Section 2.5).

2.4.2. Parameters, including grid carbon intensity for Ann Arbor, Michigan All parameters of the model, except for the DBCDs and the forecast grid carbon intensity in Ann Arbor, Michigan, are the same as previously published (Meinrenken and Lackner, 2015). Table 6 provides an overview, but we refer readers to our previous work, including Supplementary Information therein, for a detailed discussion of sources (Meinrenken and Lackner, 2015). 2.5. Utility factors for PHVs and BEVs As reviewed in detail previously (Meinrenken and Lackner, 2015), utility factors are required when modeling the combined fuel economy (and thus GHG emissions) of PHV cars (or combinations of BEV and HEV cars) that carry out a portion of all kms represented in any given DBCD on electricity and the remainder on gasoline. Briefly, for PHVs, the relevant utility factor was described early on when PHVs first became commercially available (Society of Automotive Engineers, 2010). For PHVs, the approach is straight forward conceptually, because any single HEV can be replaced by a single PHV. The utility factor is thus simply used in order to determine the combined electricity/gasoline fuel economy (and resulting GHG emissions) for a single car, namely as the weighted average of per km emissions when driving on electricity and when driving on gasoline, with the weights being the portions of total kms traveled on the respective fuel (e.g., 30% of all trip kms on electricity vs. 70% on gasoline). For BEVs, however, the concept of utility factors needed to be modified in order to account for the fact that a single BEV cannot replace all trips carried out by a single HEV. Rather, only some of that HEV’s trips can be carried out by the BEV whereas others still require the use of a second, gasolinepowered car. While straight forward mathematically, this now introduces the combined fuel economy (and emissions) of set of at least two cars. Equations for both utility factors, the traditional one for PHVs and the modified one introduced more recently (Meinrenken and Lackner, 2015), and their application to DBCD data to determine portions of distances electrified (as function of AER) are as described previously (Meinrenken and Lackner, 2015). Results explain the conceptual approach from a driver perspective with examples of utility factors shown in Fig. 3. In summary, any single DBCD for a specific driver type and charging technology results in two different characteristic curves that quantify the portion of electrified travel distances as a function of AER (one curve for PHV and another one for BEV & HEV). The total number of scenarios explored in this study is thus 9 driver use types times 6 charging technologies times 2 drive train architectures, i.e. 108 scenarios. 3. Results 3.1. Distribution of between-charge-distances (DBCD) and relevance for electric vehicles Because of the emphasis on electrified transportation, DBCD in this study refer specifically to the distribution of distances [km] that a car travels cumulatively in-between opportunities to charge the battery. For example, if a driver completes 3 consecutive trips (10 km to grocery store, from there 5 km onwards to the gym, and then 15 km back home) – but only after the third trip is the car parked long enough to allow re-charging the battery back to full – then the respective between charge distance (BCD) is (10 + 5 + 15) km = 30 km. During the shorter breaks in-between these 3 trips, if the driver uses partial “top-off” charging (not 10

All life cycle GHG, from e.g., mining of coal or petroleum, oil refining, and building & decommissioning refineries, power plants and transmission lines (incl. renewables), except for GHG from combustion at power plants Burning of coal, natural gas, or oil in power plants or gasoline in car engine (renewables and nuclear contribute zero)

Upstream fuel emissions

Combined AC/DC conversion and one way charging losses; discharging losses are the same In hybrid cars, a portion of the electricity generated by the range extender is fed directly to the electric motor, thus bypassing charging/discharging losses Electricity: Li-ion battery Gasoline: Tank (negligible)

Charging losses

11

For the vehicle battery, this is usually known as the healthy depth of discharge 80% of nominal) For gasoline, this is modelled as 50% of gasoline energy density (assuming half of the carried fuel is consumed during a typical trip) This reflects the average life time of the storage medium until replacement For BEV, this reflects the additional structural requirements in order to safely accommodate increasingly heavy batteries in the chassis For HEVs, this reflects the conversion losses of the range extender Increase in base consumption for additional weight such as from larger batteries

Nominal storage capacity inaccessible for driving Weight of fuel

Weight penalty

Gasoline to electricity losses

Structural weight premium

Average life time

Electricity: Li-ion battery Gasoline: Tank (negligible)

Weight of storage medium

GHG of storage medium

Energy bypass

Transmission losses (electricity) or incremental fuel consumption afforded for distribution to pumps (gasoline)

Transmission losses

Plant combustion emissions

Description

Name

0.025 Wh per kg and km

n/a

60%

0.025 Wh per kg and km

68%

60%

160,000 km

0.0383 kg/kWh (i.e., 0.370 kg per liter)

n/a 160,000 km

n/a

0.0509 kg per kWh of gasoline (i.e., 0.49 kg per liter)

0.235 kg CO2eq per kWh of gasoline (i.e., 2.27 kg CO2eq per liter)

50%

n/a

0.527%

60 g CO2eq per kWh of gasoline (i.e., 580 g CO2eq per liter)

Value HEV

20%

10 kg per kWh nominal capacity

120 kg CO2eq per kWh nominal capacity

n/a

15%

4.49%

50% of 502 g CO2eq per kWh electricity

57 g CO2eq per kWh electricity

Value BEV

Multiple

EPA

BEV: Multiple HEV: Negligible effect (Thomas, 2012)

Gasoline energy density, at 50% because on average 50% of gas no longer in tank

BEV: Multiple (see sensitivity analysis in Discussion). Most recent work cites 140 kg CO2eq per kWh for the Ford Focus (Kim et al., 2016) HEV: Estimate (negligible impact) BEV: Multiple (see sensitivity analysis in Discussion). Most recent work cites 1 kg per kWh for the Ford Focus (Kim et al., 2016) HEV: Estimate (negligible impact) (van Vliet et al., 2010)

(van Vliet et al., 2010)

BEV: EGrid, release 2016, for Michigan; accessed 17 Jan.2019, decreased by 50% in order to reflect a future, low carbon grid as committed to for Ann Arbor (Arbor, 2016) HEV: GREET BEV: EGrid, release 2016, for Michigan; accessed 17 Jan.2019 HEV: GREET GREET

GREET

Source

Table 6 Summary of parameters used in life cycle GHG model for all battery-only cars (BEV) and gasoline-only, hybrid cars (HEV). Note that plugin electric cars (HEV, i.e., cars capable of driving substantial distances on electricity alone, and then switching to gasoline once the battery reaches its lowest state of charge) are modelled as combinations of HEV and BEV and therefore do not have their own dedicated parameters (Meinrenken and Lackner, 2015).

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necessarily to a full battery), this BCD is reduced accordingly, using a probabilistic simulation (Methods). The underlying framework of this study assumes that each type of driver has a specific DBCD and then chooses a drive rain type and battery range according to his/her needs, instead of adapting his/her trip patterns when faced with range constraints by the vehicle (Discussion). Depending on the drive train type the driver chooses, this leads to three distinct situations:

• HEV (basecase): If the driver uses a gasoline-only vehicle, such as a series or parallel hybrid car (HEV), then this car can be used • •

for any such trip, regardless of the BCD, because the car can be refueled easily and quickly within minutes at a gas station without significant disruption to the intended trip distance and schedule. PHV: The same is true if the driver uses a plug-in hybrid vehicle (PHV), running on electricity for the first portion of BCD up to the all-electric range of the vehicle (AER), and then, if required, continuing the trip on gasoline (again without significant interruption). In other words, a single HEV vehicle can be replaced by a single PHV vehicle, for all trips. The combined fuel economy (of partially driving on gasoline and partially electricity) and resulting GHG emission are captured via a so called utility factor (Fig. 3) which is a function of each driver’s DBCD (Methods). BEV & HEV: For a battery-only electric vehicle (BEV), trips can only be carried out in this vehicle if the range of the vehicle is equal to (or exceeds) the BCD distance. If the range is smaller, in order to avoid an interruption to the usual trips taken by having to stop to charge the battery while “en route”, then the entire BCD has to be carried out in a separate car (e.g., a second, typically gasoline-driven car which the family owns for longer BCDs, a rental car, car sharing etc.). In other words, from the perspective of a car owner, a single HEV vehicle can be replaced only by a combination of a BEV and another car without range limitation (Discussion). The combined fuel economy (some trips on electricity in one car versus others on gasoline in a second car) and resulting GHG emission are captured via a modified utility factor which is a function of each driver’s DBCD (Methods).

DBCDs thus depend on the driver use type (i.e., do they tend to make longer trips, shorter trips, is the car parked for substantial amounts of time?) and the assumed battery charging technology (i.e., how long does the car need to be parked in-between two trips in order to re-charge the battery). As a proof of concept, we investigated 9 different driver use types which were based on demographics and origin/destination patterns. Driver use types and charge technologies are explained in detail in Methods. Fig. 2 shows the DBCDs for all 54 use cases explored in this study. Average BCDs vary from 17 km (55+ year old drivers; 45 min charging with 100% chance to top-off) to 59 km (male drivers; overnight charging). The mean and standard deviation of all 54 average BCDs are 30 km and 13 km, respectively. As expected, BCDs vary strongly with the charging technology. For example, for drivers whose use includes commuting to/from work, average BCD distance is 56 km for overnight charging, but only 18 km if 45 min charging with 100% topoff events is available. But BCDs also vary with use type. For example, drivers whose use includes shuttling kids to school/kindergarten have an average BCD of 58 km for overnight charging, whereas the respective value for drivers whose use does not include shuttling kids is 52 km. 3.2. Illustration of optimization framework using single driver type Using a single driver use type as an example, Fig. 3 illustrates the main metrics of our analytical framework and the trade-offs with regards to minimal GHG emissions that determine the optimum AER for each use case. With increasing AER, an increasing portion of trip km of a specific driver type can be driven on electric fuel instead of gasoline. PHV scenario: If 45 min charging with 100% top-off events is available (Methods), then the optimum AER is 68 km (i.e., AER leading to lowest GHG relative to base case, which is all HEV). Note that for different car types (large, small, more/less aerodynamic,

Fig. 2. Box plots of all 54 DBCDs explored in this study. Box plots show (top to bottom) the maximum distance, 75% percentile, median, and 25% percentile. Solid line shows average BCD for each DBCD. Kolmogorov-Smirnov tests (Everitt, 2003, Zhang et al., 2018) show that all 54 DBCDs are pairwise distinct from each other (p < 0.05). 12

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Fig. 3. Portion of electrification, aka “utility factors” (heavy lines, right axis), and resulting relative GHG emissions (thin lines, left axis) for one driver use type (car use includes shuttling kids) and two charging technologies: Overnight (dashed lines) and 45 min with 100% top-off (solid lines). Our framework further differentiates between two electric vehicle types: Gasoline-only HEVs (baseline) replaced by PHVs (black lines) or HEVs replaced by a combination of BEVs and HEVs (red lines). Superimposed grey arrows and dashed lines are explained in Section 3.2. Blue open square and blue open circle are explained in Section 3.5. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

etc.), the battery capacity (in kWh) that would enable this specific AER will vary. However, the optimum AER as well as GHG emission reductions relative to base case will remain the same. In other words, as it focuses on relative GHG reductions, our framework is agnostic to the size/performance of the vehicle type and thus applicable to any car type a particular driver may prefer (as long as the driver chooses to replace his/her current gasoline car with an electric option of similar size and performance; Methods). At the optimum AER of 68 km, 83% of the PHV kms are driven on electricity, leading to a 44% GHG reduction. Grid carbon intensity for this analysis was set to 291 g CO2eq/kWh (at charging station, i.e. including upstream effects and line losses), representing a low carbon grid as pursued by Ann Arbor, Michigan (Methods). If the AER of the PHV were further increased, a larger portion of kms would be electrified. However, at the same time, the increasingly large battery would incur higher GHG, both from its manufacturing and because of the deterioration in the PHV’s fuel economy resulting from the heavier battery. This results in a tradeoff such that overall GHG per km driven would increase. For AER upwards of ~500 km, this penalty becomes so pronounced that the electric option would actually incur higher GHG than the gasoline-only base case, despite the low gird carbon intensity assumed in the analysis. As can be seen in Fig. 3, the optimum AER further depends on the charging technology: If only traditional, overnight charging is available, then DBCD for the same driver type changes towards longer distances in-between charges. Therefore, a larger AER is required to travel a comparable fraction of overall kms powered by electricity. The optimum AER for this use case is 109 km, resulting in only 36% less GHG (vs. base case) and 80% electrified km. BEV&HEV scenario: The situation changes if the driver opts to migrate from a gasoline-only HEV (base case) to a combination of a battery-only vehicle (BEV) for the trips covered by the BEV’s range and a gasoline-only HEV for the other trips. The reason is that, for the same AER, the overall portion of kms electrified will be smaller for the BEV&HEV scenario than the PHV scenario (because BCDs exceeding AER cannot even be partially driven on electricity). Given above tradeoffs, the optimum AER for 45 min charging with 10% top-off is 95 km, providing 40% GHG reduction and 76% of kms electrified. For overnight charging, the optimum AERs is 145 km, providing 31% GHG reduction and 69% of kms electrified. In summary, for a single driver type (e.g., use includes shuttling kids), the choice of optimum AER varies strongly by charging technology and type of electric vehicle (range 68–145 km). The best scenario, from a GHG perspective, is PHV with 45 min charging and 100% top-off ability. For the other scenarios, while an adjustment in AER can mitigate some of the detrimental effects on GHG savings, GHG savings deteriorate from 44% down to 31% (while the portion of kms electrified deteriorate from 83% to 69%). 3.3. Optimal AER by driver type, charging technology, and drive train architecture In the previous section, we illustrated the basic concepts of the analytic framework based on a single driver use type, two charging 13

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Fig. 4. Optimum AER (i.e., lowest GHG) for all 9 driver use types and 6 charging technologies. As explained in Section 3.2, the analysis further differentiates between drivers choosing PHVs (top) versus BEV & HEVs (bottom).

technologies, and two electric drive train architectures. In this section, we show respective results for all 9 driver use types, each further broken down by 6 charging technologies and the 2 drive train architectures (Methods). Fig. 4 shows optimum AERs for each of these 108 scenarios, along with the average and standard deviation for all scenarios of the same drive train architecture. AERs vary from 65 km to 111 km for the 54 PHV scenarios, and from 85 km to 158 km for the 54 BEV&HEV scenarios. Fig. 5 shows achievable GHG reductions and portions of kms electrified at optimum AER for the same 2x54 scenarios. Because of the trade-offs illustrated in Fig. 3, if the AER is optimized for lowest GHG, achievable GHG reductions vary less across use cases than AERs do (34–45% for PHV and 29–41% for BEV&HEV). Likewise, portions of electrified travel distance vary only moderately across scenarios, from 76% to 86% for PHV and 66–82% for BEV&HEV. 3.4. Importance of full statistics of DBCD versus just average trip distance Comparing the average BCD distances for each of the 9 DBCD cases in Fig. 2 to the optimum AERs in Fig. 4(a) or (b), one finds a clear pattern: DBCDs with lower average BCD tend to require lower optimum AER. This finding is as expected: If BCDs in a driver’s travel schedule are overall shorter, then an electric vehicle with smaller range suffices to enable enough electrified travel kms to achieve optimum GHG reduction. Based on this pattern, we investigated whether simply knowing a driver’s average BCD would suffice to determine the optimum AER; or whether knowing the full distribution of BCDs is in fact required. As shown in Fig. 6, optimum AER is correlated with average BCD (94% for the PHV scenario, and 90% for the BEV & HEV scenario; p < 0.01). However, average BCD explains only a portion of the overall variance in AERs, with an R2 of 0.88 for PHVs and 0.80 for BEV & HEV. To give a concrete example, consider drivers whose use includes commuting and who have access to 8 h charging. The average BCD is 36.7 km and optimum AER for BEV is 130 km (red square in Fig. 6). However, the average BCD (8 h charging) for drivers who do not use their car for commuting is almost the same (36.6 km) – but the respective optimum AER for BEVs is 152 km (red diamond in Fig. 6). If non-commuting drivers (8 h charging) had chosen a BEV with 130 km AER (instead of the optimum 154 km), their achieved electrification portion would have dropped to 60% (whereas the optimum would have been 66%). This shows that knowing a driver’s average BCD can help to approximate the optimum AER. However, only knowledge of the full statistics of DBCD (i.e., the distribution of all between-chargedistances) allows to determine the optimum AER and thus achieves the highest possible GHG savings and electrification of travel.

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Fig. 5. Highest achievable GHG reductions (left) and electrified travel distance portions (right) for all 9 driver use types and 6 charging technologies. As explained in Section 3.2, the analysis further differentiates between drivers choosing PHVs (top) versus BEV & HEVs (bottom). False colors illustrate the relative magnitude of % figures graphically. Note that GHG reductions are specified versus an optimistic, all HEV baseline, whose emissions are already 35–45% below those of conventional gasoline vehicles without any hybrid technology (Introduction). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6. Relationship between average distance of the DBCD and optimum AER, for all 54 use cases. Trend lines show linear fits. The two specially marked data points (red square and red diamond) are explained in Section 3.4. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.5. Interpretation and implication for users As illustrated in Fig. 3 and further by the full data for all 108 scenarios in Fig. 4, the optimum AER a driver should choose in order to minimize GHG varies by more than a factor of 2, from 65 km to 158 km (for trip distance distributions and a forecast low carbon grid in Ann Arbor, Michigan). The aspect that causes the largest variation in AER is the drive train architecture: The average optimum AER for PHVs is 84 km versus 118 km for BEVs. As expected, within the same drivetrain architecture (either PHV or BEV) and driver use type, optimum AER depends on the available charging technology. For example, for 16–34 year old drivers, the optimum AER drops by 45 km, from 158 km for overnight charging to 113 km for 45 min charging with 100% top-off (BEV&HEV). As an example for variation by driver use type, the largest change is by 12 km, from 95 km for drivers whose use includes shuttling kids to 85 km for 55+ year old drivers (BEV&HEV, 45 min charging with 100% top-off). Moving on to Fig. 5, we find that – provided the optimum AER is chosen – the maximum possible GHG reductions and 15

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electrification portions vary less: Lowest GHG reduction is 29%, the highest 46% (factor 1.6); the lowest portion of kms electrified is 66%, the highest 86% (factor 1.3). In other words, despite pronounced differences in drive train architecture, driver use type, and charging technology, choosing a car with the optimum AER for each scenario can lead to broadly similar GHG reductions and electrified travel distances across all 108 scenarios. However, adjusting the AER to the scenario is crucial: For example, as shown in Fig. 3, the optimum AER for PHV drivers with 45 min charging|100% top-off and whose use includes shuttling kids is 68 km. This leads to 44% GHG reduction and 83% kms electrified. But if the same AER of 68 km were chosen by BEV drivers who only have access to overnight charging (again for shuttling kids), then GHG reductions would drop by more than half, from 44% to 21% (blue open square in Fig. 3) and the portion of electrified kms would also drop by more than half, from 83% to 37% (blue open circle in Fig. 3). In summary, adjusting the AER according to individual drivers’ trip distance distributions, charging technology, and drivetrain architecture is a crucial pre-requisite for electric vehicles to achieve their highest potential in reducing system-wide GHG emissions and fuel switching from electricity to gasoline in the personal transportation sector. 4. Summary and discussion 4.1. Conclusions Our work sought to integrate two types of previous studies on electric vehicles, one of which focuses on the technical feasibility and range requirements to enable their adoption whereas the other focuses on GHG emissions. Integrations such as in this study are less common or have considered overnight charging only. The latter also identified the need to select optimum battery sizes in order to maximize GHG savings, but because of a lack of detailed driving pattern data, the role of different charging options and driver types could not yet be explored (Introduction). We present the first application (to our knowledge) of GPS-based, regional, personal trip distance distribution data to determine use-case specific, GHG-optimized AERs for electric vehicles of different drive train architectures. Calibrating a previously developed vehicle GHG model to a forecast low-carbon grid for Ann Arbor, MI (USA), we find that the optimum AER varies most strongly with drive train architecture, followed by charging technology, and driver use type. We conclude that adjusting the AER according to drivers’ trip distance distributions, charging technology, and drivetrain architecture is a crucial pre-requisite for electric vehicles to achieve their highest potential in reducing system-wide GHG emissions and fuel switching from electricity to gasoline in the personal transportation sector. Only knowing the full underlying distribution of trip distances enables determining the AER accurately enough to achieve the maximum possible GHG reductions and electrification of travel distances. We thus expect that access to detailed trip distance distribution data, be it from GPS devices in cars (as in our study) or for example from mobile phones, will become increasingly important for policy makers, car manufacturers, and consumers to determine optimum AERs for electric vehicles. 4.2. Limitations, parameter sensitivity, and future work 4.2.1. Technical feasibility and adoption behavior Our framework compares all 108 electrification scenarios side by side, without yet considering in detail their individual feasibility, cost, or ease of use for drivers. For example, adoption of the BEV & HEV scenario may be less likely owing to the likely higher purchasing price of BEVs vs. PHVs (due to the larger battery) and the logistical difficulty of needing a 2nd, gasoline-powered car (rented or owned) for trips beyond the BEV’s range. On the other hand, consumers may prefer BEVs (in conjunction with HEVs) because of their more straightforward technology and sustainability appeal than PHVs, such as low noise and zero local air pollution. Furthermore, effects of subsidies to promote certain vehicle types or other total cost of ownership considerations have not been considered. Another important feasibility consideration is the availability of charging stations – along with the sophistication of logistical planning on behalf of drivers (where is the next charging station?; will I get there on my current charge, etc.?). High penetration of charging stations also implies overall increased load on the grid. For example, the grid may be able to handle the relatively smaller charging power requirements of PHVs (whose optimum battery size will be smaller), especially when only overnight charging is considered i.e., when grid use is otherwise low. In contrast, the availability of a sufficient number of 45 min ultrafast charging stations throughout the city – as presumed in our scenarios – presents a higher challenge, from the point of view of the charging station infrastructure, system costs, as well as grid stress. Finally, a premise of our work is that drivers would like to keep their preferred travel patterns, regardless whether they drive an electric or a gasoline car. However, this assumption does not always hold. For example, Langbroek et al. discovered that electric vehicle users tend to make more trips than when they use traditional vehicles (Langbroek et al., 2017). And Khayati and Kang (2017) modelled specific scenarios based on state-wide travel surveys to determine how vehicle drivers may adapt modified travels patterns based on factors such as vehicles’ electric range and charging availability (Khayati and Kang, 2019). 4.2.2. Details of GHG modeling and parameter sensitivity As shown in (Meinrenken and Lackner, 2015), not only the achievable GHG reductions of electric cars but also the optimum AERs to achieve those reductions depend on the carbon intensity of the electric grid. For any specific region, this intensity changes not only from one year to the next, as e.g. more renewable capacity is installed. It can also change during a single day as e.g., night time use may be preferentially provided by so called “base load”, low carbon nuclear generation whereas day time peak use may prompt firing-up older, fuel-inefficient backup plants. This is often referred to as marginal versus average grid carbon intensity. At the same 16

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time, some drivers may have access to 100% renewable, and thus very low carbon electricity, either through their home-installed solar generation or via purchase power agreements from e.g. large, grid-connected wind farms. However, since our analysis is based anyway on a forecast, aspirational low carbon intensity as pursued for Ann Arbor, Michigan (Methods), we did not vary the grid carbon intensity further. Future work may vary grid carbon intensities, e.g., for typical day time versus night time charging. With regards to battery technology, both achievable GHG reductions as well as respective optimum AERs depend on the weight and GHG emitted during the life cycle of the battery. In order to quantify the sensitivity of our results to these parameters (Ciroth and Meinrenken, 2014), we reduced the GHG for the battery from 120 kg CO2eq/kWh as used in our base case (Meinrenken and Lackner, 2015) to 90 kg CO2eq/kWh which reflects benefits from end-of-life recycling of batteries (Dunn, 2002; US EPA, 2013). Furthermore, we reduced the battery energy density from 10 kg/kWh – which is in line with recent commercially available electric cars (Kim et al., 2016) – to 5 kg/kWh, reflecting a future doubling in energy density as pursued by the US DoE and as used in previous related modeling studies (DoE, 2010; Needell et al., 2016). These two parameter changes in combination represent an optimistic, future scenario of substantially improved battery technology. With these updated parameters, one of the scenarios presented in Fig. 3 changes as follows (black, solid lines): Optimum AER increases from 68 km to 87 km, GHG reductions improve from 44% to 47%, and the portion of distance traveled on electricity increases from 83% to 86%. This shows that substantial improvements in battery technology can improve GHG savings and electrification potential, albeit not drastically. Finally, our analytical framework assumes constant fuel economy of a car (kWh/km driven) for all trips, regardless of the origin, destination, or distance of the trip (however, the effect of increasing battery size on the fuel economy is modelled). This is a simplification and indeed creates a bias with respect to trip distance distribution analysis: At same AER, shorter distances, which will more likely consist of more stop-and-go traffic, tend to incur higher kWh/km requirements in comparison to longer trips. 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Glossary GHG: Greenhouse gases BCD: Between-charge distance [km] DBCT: Distribution of BCDs HEV: Hybrid-electric vehicle (parallel or series hybrid car, requiring only gasoline as fuel) BEV: Battery-electric vehicle (requiring only electricity as fuel) PHV: Plug-in hybrid vehicle (car driving on electricity up to AER, and then on gasoline) AER: All-electric range (maximum distance that a BEV or PHV can travel on a full charge) [km] DBSCAN: Density based spatial clustering of applications with noise method

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