Submitted:
24 October 2024
Posted:
25 October 2024
You are already at the latest version
Abstract
Keywords:
1. Introduction
- What is the present status of EV adoption in the world to meet growing travel demand and comply with smart city initiatives?
- What is the demographic and socio-economic profile (e.g., age, gender, education, income, family size, vehicle ownership, and political affiliation) of EV adopters?
- What are the impacts of various factors such as travel and charging behaviors, battery range and charging status, charging infrastructure, cutting edge technology, the built environment, energy demand, and financial and institutional aspects on EV adoption?
- What are the research gaps in the extant literature regarding the transition to EVs in the context of the smart city and how can they be addressed?
2. Tools and Techniques
2.1. Study Approach
2.2. Key Attributes of the Selected Articles and Reports
3. Current Status of EV Adoption
4. Synthesis of Extant Literature
4.1. Multi-Factor Interactions of EV Adoption
4.2. Prior Knowledge About EV
4.3. Willingness to Pay for EV and Charging Infrastructure
4.4. Socio-Economic Profile of EV Users
4.4.1. Users’ Age
4.4.2. Gender
4.4.3. Educational Attainment
4.4.4. Household Income
4.4.5. Household Size, Composition, and Type
4.4.6. Number of Vehicles in the Household
4.4.7. Driver’s License and Political Affiliation
4.5. Travel Behavior
4.6. EV Charging Behaviors
4.6.1. Charging Duration and Frequency
4.6.2. Charging Time During the Day and Night
4.6.3. State of Charge
4.6.4. Location and Type of Charging Station
4.6.5. Electricity Demand for EVs
4.7. Innovative Technology
4.8. Car Purchase Price and Gasoline Cost
4.9. Environmental Awareness
4.10. Institutional Aspects
| Study | Incentives | Key Result | context |
| [86] | Tax savings of $1,000 and $3,000 | 4% and 13% increase in HEVs | US |
| [89] | $1,000 incentive | 4.6% increase in HEV sales | US |
| $3150 incentive | 15% increase in Toyota Prius sales | US | |
| [83] | Rebates of $7500 in 2020 than 2010 | BEV (22.9%), PHEV (24.1%), HEV (20%), ICE (33.1%) | Australia |
| Rebates of $7500 from 2020 to 2030 | BEV (23.1%), PHEV (23.4%), HEV (20%), ICE (33.6%) | ||
| Rebates of 25% (max $8500) from 2020 to 2030 | BEV (20.8%), PHEV (24.1%), HEV (20.1%), ICE (35.1%) | ||
| Feebate (upfront additional fees) of 4% from 2015 to 2030 | BEV (12.7%), PHEV (21.3%), HEV (22.1%), ICE (43.9%) | ||
| [69] | $8023 subsidy for 3 years, $8023 subsidy for 6 years. | 40%-58% share of PIHV and BEV | UK |
| [73] | No vehicle tax, free parking, and bus lane use | 27% increase in PHEVs, 1% increase of BEVs | Germany |
| Purchase price premiums | 13% increase in PHEVs, 35-36% increase in BEVs | ||
| [163] | Total of more than 5.9 billion RMB as direct subsidies in 2016 | 12.57% increase of PEVs | China |
| [129] | Subsidy of $9000 (US) and $18,000 or more (China) | To achieve a 50% share of low range PEVs | China and the US |
| Subsidies of more than $20,000 in both US and China | To achieve a 50% share of long-range PEVs | ||
| [144] | License fee exemption | 18.1% increase of PHEVs, 45.6% increase of EVs | China |
| [130] | Parking fee full exemption | 9.5% increase in EVs | China |
| Full exemption of road tolls | 4.1% increase in EVs | ||
| Purchase tax full exemption | 30.1% increase in EVs | ||
| Insurance charge full exemption | 5.18% increase in EVs | ||
| Vehicle and vessel (V & V) tax exemption | 1.77% increase in EVs | ||
| [155] | Production subsidies of $13450 | 70% increase in EVs | China |
| Purchase subsidies of $7300 | 60% increase in EVs | ||
| [102] | Purchase subsidy of $6600 to $8800 | 33 % increase in new registered vehicles | Greece |
| Home charger subsidy of $550 | |||
| Old car withdrawal subsidy of $1100 |
4.11. Built Environment
5. Discussion
5.1. Summary
5.2. Smart City Development and Transition to EVs
6. Conclusions and Directions for Future Research
- Numerous studies have gathered data through household travel surveys to estimate EV adoption and charging behaviors [38,61]. Some of these studies relied on small sample sizes to represent real-world scenarios for relevant policy formulation, which may inadequately capture the complexity of this phenomenon [45,61,122]. Thus, future research should collect data from larger, more diverse samples that include various demographics (e.g., age, gender, income, education, EV awareness, geography) to gain a more comprehensive understanding of EV adoption and charging patterns. This will particularly enable us to study whether EV-based mobility technologies may be instrumental in reducing social disparities in mobility or exacerbate current disparities.
- Several studies have investigated charging infrastructure requirements for personal EVs only, disregarding demands from transportation network companies and other shared mobility providers [11]. This may lead to an inaccurate estimation of the number of charging stations and of the impact on electric grids. Future study should include all transportation modes to calculate the number of charging stations accurately and avoid any inconvenience for the EV consumers.
- Some studies have used simulated pseudo-synthetic datasets generated from personal GPS data to assess the economic feasibility of shared EV services [11,63]. However, the lack of actual data on user travel behaviors and charging patterns may undermine the real-world impacts of these mobility systems. To overcome this, it is recommended to collect data from actual shared mobility systems to accurately estimate their impacts and adoption rates.
- Most previous studies are cross-sectional in nature, limiting insights on how attitudes and opinions, and EV adoption evolve over time [64,67]. Consequently, longitudinal studies are necessary to estimate EV adoption as socio-political systems and technologies change, by observing the same individuals at different points in time.
- While estimating EV charging demand, researchers have assumed uniform travel patterns and continued decrease trend in electricity costs [67]. They often overlooked the potential rebound effects on energy systems from a growing EV population and the limited capacity of transmission and power grids. Future study should consider this rebound effect and the existing capacity of electrical grids to better estimate actual charging demand, as these factors can significantly influence EV market share.
- Most studies have been conducted in developed countries (e.g., North America, European cities, Australia, Japan, Korea) and a handful of developing nations (e.g., China, India). Considering the rapid growth of EVs, it is crucial to understand consumer behaviors in other developing and least developed countries to enrich literature. This will enable policymakers in diverse countries to identify key determinants of EV adoption and implement pertinent effective measures to promote EVs across all segments of population. This also fits well with burgeoning interest towards decarbonization and green energy transition in these countries.
- While the existing literature addresses the relationship between EV adoption and smart city development, no studies, to the best of our knowledge, have specifically explored the perspectives of EV consumers on this connection. Thus, a study can be conducted to assess EV owners’ knowledge of smart cities and how they perceived EV’s role in smart city development.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
| ABM | Agent-based modeling |
| AFV | Alternative Fuel Vehicle |
| AHP_OWA | Analytic hierarchy process-ordered weighted averaging |
| BEV | Battery Electric vehicle |
| BEVxx | Battery electric vehicle with a range of xx miles |
| BV | Biofuel Vehicle |
| BLAST-V | Battery Lifetime Analysis and Simulation Tool for Vehicles |
| BTPCAR | Bivariate and trivariate Poisson–lognormal conditional autoregressive models |
| CV | Conventional vehicle |
| DCFC | Direct Current Fast Charge (Level 3 charger) |
| DS | Descriptive statistics |
| DSO | Distribution system operator |
| ECML | Error component multinomial logit model |
| ERDEC | Estimating Required Density of EV Charging stations model. |
| eVMT | Electric vehicle miles traveled |
| EVI-Pro | Electric Vehicle Infrastructure Projection Tool |
| EVSE | Electric vehicle supply equipment |
| FA | Factor analysis |
| FCEV | Fuel Cell Electric Vehicle |
| GMM | Gaussian Mixture Models |
| GaD | Gamma distribution |
| GauD | Gaussian distribution |
| GHG | Greenhouse Gas |
| GM | Generalized method of moments model |
| GP | Graphical presentation |
| HEV | Hybrid electric vehicle |
| ICT | Information and Communication Technology |
| ICE | Internal Combustion Engine |
| IEA | International Energy Agency |
| IoT | Internet of Things |
| LCM | Latent class model |
| LDV | Light-duty vehicle |
| LM | Logit model |
| MCA_CM | A diffusion of Multi-criteria analysis (MCA) and choice modeling |
| MCDS | Multi-criteria decision support |
| MCM | Monte Carlo method |
| MFRLM | Modified flow-refueling location model |
| MUD | Multi-unit dwelling |
| MILM | Mixed integer linear model |
| MLM | Mixed logit model |
| MNL | Multinomial logit model |
| MWh | Mega-watt hour |
| MCA | Multiple correspondence analysis |
| NGV | Natural gas Vehicle |
| NO | Numerical optimization |
| OLM | Ordered logistic model |
| OLS | Ordinary least squares regression |
| PEV | Plug-in electric vehicle |
| PHEV | Plug-in hybrid electric vehicle |
| PHEVxx | Plug-in hybrid electric vehicle with a range of xx miles |
| QGM | Quadratic growth model |
| SEM | Structural equation model |
| SErM | Spatial error model |
| SA | Supplier or retailer |
| SOC | State-of-Charge |
| SLM | Standard logit model |
| SRA | Stepwise regression analysis |
| SUD | Single-unit dwelling |
| TOU | Time-of-Use |
| TWh | Terawatt Hour |
| TPCAR | Trivariate Poisson-lognormal conditional autoregressive model |
| TSO | Transmission system operator |
| US | United States |
| USDOT | US Department of Transportation |
| VMT | Vehicle miles traveled |
| WOA | Weighted overlay analysis |
| WTP | Willingness to pay |
| WSM | Two-level weighted sum model |
| ZEV | Zero-emission Vehicle |
Appendix A
| Feature | Results | |
| Age | Median age | 39.22 [68], 50.36 [80], 42 [90] |
| Less than 50 | 43.9% [55], 62% [70], 57.8% [74], 73% [72], 59.2% [75], 58.3% in US and 77.9% in Japan [79], 97.3% [177], 92.7% [130]. | |
| 50 and above | 66.7% [5], 41.8% [55], 38% [70], 27% [72], 40.8% [75], 7.3% [130], 6.07% [178], 12.23% [179] | |
| Gender | Male | 75% [5], 93% [55], 71% [56], 73% [68], 74.6% [70], 40.4% [74], 43% [72], 79.6% [75], 38.2% in US and 56% in Japan [79], 47% [90], 63.4% [177], 77.7% [130], 64% [127], 53% [91], 57.7% [120], 49% [134], 59.54% [178], 47.83% [179], 53.4% [94] |
| Marital status | Married/ couple | 85.1% [5], 69.8% in US and 80.3% in Japan [79], 87% [71], 48.4% [177], 84.11% [127] |
| Education | Bachelor/ Master | 90% [5], 70% [55], 87% [56], 47.6% [68], 43.5% [70], 63.5% [74], 37% [72], 66.6% [75], 41.05% [80], 81.6% [71], 51.7% [177], 54.6% [130], 77.09% [127], 52% [91], 57.7% [120], 66.6% [134], 86.85% [178], 69.84% [179], 67.5% [94] |
| Income | Over $100,000 | 80% [5], 79% [56], 10.3% [72], 57% [86], 25% [127] |
| Household size | 2 or more | 90.3% [5], 93% [56], 84.7% [74], 87% [71], 95.61% [179] |
| Homeownership | 96% [56] | |
| Home type | Detached | 91% [56], 72.8% [72], 72% in US and 54% in Japan [79], 66.7% [71] |
| Apartment | 20.8% [72], 16.4% [71] | |
| Vehicle ownership | No or 1 | 57.4% [74], 38.1% [72], 55.1% [177], 79.8% [130], 38% [91], 89.13% [179], 24.9% [97] |
| 2 or more | 92.8% [5], 70% [49], 58% [50], 42.3% [74], 61.8% [72], 73% [55], 10.87% [179], 75.1 [97] | |
| EV ownership | 22% [49], 4% in US and 21.5% in Japan [79], 3.87% [80], 5.7% [120] | |
| License | Yes | 78% [90] |
| Interested to buy EV/HEV | Next purchase | EV 20% and HEV 31% [51], EV 24% [50], 52.7% [74], 60% in US and 53% in Japan [79], 22.22% [80], 53% current EV owner and 82% current non-EV owner [52], 79% [55], HEV 44% and EV 33% [71] |
| Political affiliation | Democrats | 52% [5] |
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| Study | Data Source | Study Methodology |
| [38] | Trial of 212 EVs (4) | GMMs (1) |
| [39] | Trial of 44 EVs (4) | MCM (1, 2) |
| [40] | 2001 National Household Travel Survey (NHTS) (2) | GaD (1) |
| [41] | Travel data from the vehicle using GPS (1) | GaD (1) |
| [42] | Trial of 44 EV (4), interview (1) | GP (4) |
| [43] | Field trial (4) | GP (4) |
| [44] | 70 residential users (1) | GP (4), NO (4) |
| [45] | Travel data from the vehicle using GPS (1) | GauD (1) |
| [46] | Travel data from the vehicle using GPS (1) | MCM (1, 2) |
| [47] | Typical semi-urban/rural 15 kV grid (4) | MCM (1, 2) |
| [48] | PEV registration data from Southern California Association of Governments (5) | QGM (4) |
| [49] | Mail survey to EV owners (1) | GP (4) |
| [50] | Survey of 1,027 respondents by Opinion Research Corporation (ORC) for NREL (1) | GP (4) |
| [51] | Telephone survey of 1,003 respondents (1) | GP (4) |
| [52] | Mobile survey to 6,499 individuals (1) | GP (4) |
| [53] | Household travel survey of 264 respondents (1) | GP (4) |
| [54] | Online survey of 1,257 EV owners (1) | MNL (3) |
| [12] | GPS travel survey by INRIX (5), 2016 American Community Survey (ACS) (3) | EVI-Pro (2) |
| [55] | Survey of 2,300 CarMax customers (1) | GP (4) |
| [56] | Survey of 1,419 PEV owners (1) | GP (4) |
| [57] | Data from 270 public chargers and 700 residential chargers (1) | GP (4) |
| [58] | ACS (3), 2011 Massachusetts Travel Survey (MTS) (1), Alternative Fuels Data Center (AFDC), and IHS Automotive (5) | OLS (3), EVI-Pro tool (2) |
| [59] | GPS travel survey by INRIX and IHS Automotive (5) | EVI-Pro tool (2) |
| [60] | 2010-2012 California Household Travel Survey (1) | EVI-Pro tool (2) |
| [11] | Vehicle registration data from IHS Automotive (5) | EVI-Pro tool (2) |
| [61] | 2001 NHTS (2), Household survey of PHEV owner (1) | GP (4) |
| [62] | Vehicle registration data from IHS Automotive (5), Front Range Travel Counts (FRTC) survey of 12385 households (1) | BLAST-V (2) |
| [63] | GPS travel survey by INRIX (5) | EVI-Pro tool (2) |
| [64] | Survey of 500 EV owners (1) | WSM (3) |
| [65] | Online survey of EV owners (Phase-1 666 and Phase-2 593) (1) | LM (3) |
| [66] | 2017 NHTS (2), Simulated trip chains of 1000 EVs for 30 days (4) | ABM (2) |
| [67] | Online survey conducted by Gallup Korea (1) | MLM (3) |
| [68] | Online survey of 252 respondents (1) | GP (4) |
| [69] | EV sales data, purchase price, operating costs, speed, fuel availability, emission rating, and battery range from Energy-saving trust and Automobile association (5) | MNL, MLM (3) |
| [70] | Survey of 598 potential car buyers (1) | SLM (3) |
| [71] | Survey of 1,754 new car buyers (1) | MNL (3) |
| [72] | Online survey of 3,029 potential car buyers (1) | MNL, LCM (3) |
| [73] | Online survey of 711 potential car buyers (1) | MNL and MLM (3), simulation (2) |
| [74] | Online survey of 711 potential car buyers (1) | MNL, LCM (3) |
| [75] | A survey of 54 respondents in the Western Australia Electric Vehicle trial (1) | MNL and MLM (3) |
| [76] | Online survey of 1152 potential car buyers (1) | GP (4) |
| [77] | UK Ordnance Survey (5), A series of consumer surveys (1) | WOA (4) |
| [78] | Socioeconomic, environmental and transportation data from European Statistical Databases (2010), European Commission (5) | MCDS and AHP_OWA (4) |
| [79] | Online survey of 4202 respondents in US and 4000 in Japan (1) | ECML (3) |
| [80] | Online survey of 2,302 respondents (1) | OLS (3) |
| [81] | Real EV taxi operation data collected by Daejeon Techno Park (5) | ERDEC (2) |
| [82] | Simulations (4) | ABS (2) |
| [83] | Focus group discussion (1), Australian Bureau of Statistics (ABS) (3) | MCA_CM (4) |
| [84] | 2010 ACS (3) | MILM, MFRLM (4) |
| [85] | 2010 ACS (3) | BTPCAR, SErM (3) |
| [86] | 2010 NHTS (2) | MLM (3) |
| [87] | 2012 vehicle registration data from Delaware Valley Regional Planning Commission (DVRPC) (5) | TPCAR (3) |
| [88] | 1000 household surveys by ORC International (1) | SRA (3) |
| [89] | Monthly sales data of HEVs for the 2000-2010 period from Data Center Archives (5) | GM (3) |
| [90] | Puget Sound Regional Council’s 2006 Household Activity Survey (1) | OLS (3) |
| [91] | Stated preference survey of 996 individuals in October-December 2018 (1) | MLN (1) |
| [92] | Household questionnaire survey of 332 respondents (1) | FA, LM (3) |
| [93] | Survey of 660 respondents (1) | FA, k-means clustering (3, 4) |
| [94] | Online survey of 2,493 respondents (1) | FA, MLM (3) |
| [95] | Online survey of 2,198 individuals (1) | FA, MCA, LM (3) |
| [96] | Survey of 982 individuals (1) | FA, OLM (3) |
| [97] | Survey of 346 participants (1) | DS (4) |
| [98] | Survey of 366 individuals (1) | FA, SEM (3) |
| [99] | Survey of 675 students (1) | SEM (3) |
| [100] | Survey of 405 individuals (1) | SEM (3) |
| [101] | Survey of 526 respondents (1) | SEM (3) |
| [102] | Survey of 350 individuals (1) | OLM (3) |
| [103] | Survey of 172 respondents (1) | FA (3) |
| [104] | Survey of 403 participants (1) | Partial least squares SEM (3) |
| [105] | Online survey of 488 respondents (1) | SEM (3) |
| [106] | Census data (3) | OLS (3) |
| [107] | Survey of 511 respondents (1) | SEM (3) |
| Studies | Results |
| [49] | Reduced air pollution (76%), money savings on gasoline (50%), cutting-edge technology (30%), easy driving (39%), quite ride (33%), reliability (96%), easy to maintain (89%), using less or no gas (20%). |
| [51] | Concern for environment (80%), lower long-term costs (67%), cutting-edge technology (54%), access to the carpool lane (35%), reliability (92%), fuel economy (87%), crash rating (77%), cost (71%), vehicle performance (69%), advanced safety technology (60%). |
| [52] | Affordable pricing (52%), longer driving range (37%), improved infrastructure (19%). |
| [54] | Concern for the environment (75%), reduce dependence on petroleum (45%), low price of electricity vs gasoline (43%), tax breaks and net price of the vehicle (38%), cutting-edge technology (32%), vehicle performance (21%). |
| [74] | Replacement of old vehicle (82.7%), additional vehicle (12.1%), initial vehicle purchase (5.2%). |
| [80] | Fuel economy (59.66%), appearance (19.77%), adequate space (8.32%), advanced safety technology (22.29%), reduced dependence on gasoline (26.57%). |
| [118] | Range (59.9%), price (57.3%), charging station (48.5%), consumer knowledge (41.9%), apartment charging (21.6%), lack of incentives (19.8%), lack of car model (17.2%), impacts to grid (16.3%), winter weather (15.9%), lack of political will (12.3%), long charging time (11%). |
| [119] | Charging stations (13.6%), purchase price (12.6%), long-term planning by government (12.1%), repair and maintenance workshops (6.9%), tax exemption policy (6.7%), range (6.1%), battery life (5.7%), battery replacement cost (5.5%), reliability and performance (5.2%), awareness-raising (5.0%), domestic industry (4.1%), understanding of product quality (3.5%), electricity price (2.1%), knowledge about EVs (2.6%), credit access to purchase EVs (2.8%). |
| Study | EV | Charging station | Fuel |
| [71] | $1000 to $3000 for PHEV, $3000 to $6000 for EV | - | - |
| [70] * | - | $1089 to $525 | - |
| [88] | - | - | $5.36 -$3.38 per gallon |
| [86] | $963 to $1718 for hybrid | - | - |
| [79] | $21.5 in both US and Japan for full battery | $49.8 in the US and $33.6 in Japan | $49.8 in the US and 36.7 in Japan for fuel cost savings, Americans pay more for emissions reduction than Japanese (i.e., $29 vs. $26.2) |
| [75] | - | $1.17 extra for 10-minute reduction in charging time | - |
| [73]* | $22.1-$45.5 for BEV for every additional driving range, $3215.4 and $6486 for vehicle tax reduction | $62.1 to $127 for 1% expansion of stations, $7 and $24.84 for saving every charging minute | $731.4 to $1476.6 for fuel cost savings of $1.38 per 100 km, $27.6 to $55.2 and $62.1 to $124.2 depending on the budget and environmental concern for a 1% reduction of CO2 emissions |
| [72] | $35 to $75 for a mile of added driving range, $6000 to $16,000 for desirable attributes | $425 to $3250 for a one-hour reduction in charging time | $4853 for each $1.00/gallon reduction, up to $4300 for a 95% reduction of pollution |
| [74]* | $12.6–$131.05 for additional km of driving range, $7522 for vehicle tax exemption, $6212 for using bus lanes and parking for free | $63 to $310.3 for increasing fuel availability by 1%, $5.24-$203.4 for saving one minute charging time | $1105 for fuel cost saving of $1.05/100 km, $14.7 to $1501.3 for reducing 1% of CO2 emissions |
| [52] | Extra $5000 for increasing range from 150 to 200 miles | - | - |
| [56] | - | 40% - 70% more for public charging, double for 15 minutes charge by DCFC what they pay for 1 hour of Level 2 charging, 2.5 to 3 times higher for meeting emergency demands than the daily charging | |
| [65]* | Extra $1131.29 if the monthly leasing cost of battery reduced by $10.2 | - | - |
| [129] | $10,000-$20,000 for BEV technology (US), $0-$10,000 (China), Chinese pay almost twice more than Americans to decrease operating costs of EV ($3000 vs $1600 per $0.01/mile reduction), Chinese pay almost three times more than Americans to decrease acceleration time ($5000 vs $1200 per 1 s decrease), Chinese pay less than Americans to purchase most preferred vehicle ($18,000 vs $27,000) | $6400 for fast charging capability | |
| [130]* | $3822 for an increasing range of 100 km | $4917 for exemption of public charging fee | - |
| [91]* | $3059 to $17180 more for an EV, $34 for an extra km of driving range | $120 for per minute saving for Fast charging time | $353 for fuel cost saving of $1.18/100 km |
| [131] | - | More than 50% of consumers favored quick charging option and were willing to pay double price for that | - |
| Study | Average daily travel distance | Travel time | |
| Depart from home | Arrive at home | ||
| [39] | 24.17 miles | - | - |
| [40] | 40 miles | - | - |
| [45] | 24.54 miles | - | 6:55 pm |
| [49] | 20+ miles (30%), 33 miles (33%) | ||
| [46] | <6.2 miles (24%), 21% 6.2-12.43 miles (21%), 12.43-18.64 miles (18%) | 6am to 9am, 6pm to 7pm | 3pm to 6pm |
| [54] | 11.3 and 33.6 miles | - | - |
| [58] | 34.9 miles | - | - |
| [61] | 32.73 miles | - | - |
| [63]. | 37 miles | - | - |
| [68] | 26.72 miles | - | - |
| [80] | 28.35 miles | - | - |
| [81] | Maximum 89.17 miles | - | - |
| [44] | <30 miles | - | - |
| [138] | 12.5 miles (34%), 25 miles (23%), 37.5 miles (15%), 50 miles (10%), 62.5 miles (7.5%), 75 miles (6%) and 87.5 miles (3%) | - | - |
| [127] | 27.42 miles | - | - |
| Study | Charging duration | Connection time | Charger location | Charger type |
| [56] | - | 8pm-8am (67%), 12am-6am (peak) | Home (91%), public and work (71%) | Level 2 (56%), |
| [55] | 4-8 hrs (48%), 1-3 hrs (23%) and 9-12 hrs (11%) | - | Home (84%), public (8%), Within 5miles (69%), 6-10 miles (18%) | Level 2 (64%) |
| [12] | - | 4pm-8pm, 8am-12pm | Home (66%), | Level 2 and DCFC |
| [53] | after 12am, 7-8 am, 5pm-6pm | Level 2 | ||
| [49] | - | - | Home (87%), work (8%) | - |
| [48] | 6am-9am, 9am-3pm | Work, Metro station | ||
| [54] | - | - | Home (80%), highly travelled corridors | - |
| [51] | 30 min (68%), 15min (44% women, 33% men) | - | - | - |
| [44] | 1-2 hrs, >3 hrs | 4pm-10pm, 8pm-9pm (peak), 4am-7am | - | - |
| [43] | - | 4pm-6:30pm, 10:30pm | Home, work, public | - |
| [42] | - | Working hour | Work | - |
| [39] | - | 12pm, 9am-1pm, midnight | Home, work, public | - |
| [38] | - | 8am-9am, 12pm, 6pm, night | - | - |
| [79] | - | - | Home (82.3% in US, 70.3% in Japan) | - |
| [77] | - | - | Home, City center | - |
| [139] | - | - | City center, along major highways | - |
| [64] | 43 min (retail), 21 min (office), 2 hours 9 min (park and ride), 1-hour 21min (transit station), 21 min (gas station) | - | Home, work, public | - |
| [141] | - | - | Home (85%-95%), work (25%), public (18%) | - |
| [62] | 3 pm-6 pm | Level 1, Level 2, DCFC | ||
| [61] | 3 hrs | Evening, 11pm | - | Level 1 |
| [67] | - | 9am-12pm, 3pm-6pm, | Home (59.1%), public (40.9%) | - |
| [11] | 20 min (DCFC) | Home (88%) | Level 2DCFC | |
| [59] | 8am (peak), 4pm-12am | Home, work, public | Level 2DCFC | |
| [58] | 4pm-10pm, 7am-2pm, 8am-8pm, | Home, work, public | Level 1, Level 2, DCFC | |
| [57] | 1-2 hrs | 9 am-7pm | Home, public (low) | - |
| [68] | 30 min (DCFC) | - | Home (58.4%), work (29.1%) | Level 1 (38.6%), Level 2 and DCFC (51.4%) |
| [66] | - | 8pm (peak), 8am-11am | Home (44.4%), work, public (58.7%) | Level 2 and DCFC |
| [47] | - | 10pm-8am (low rate) | Home, public | Level 1, Level 2, DCFC |
| [138] | - | 7am-10am and 4pm-7pm (65%), 4pm-12am (63%) | - | - |
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