Siting charging stations for electric vehicle adoption in shared autonomous fleets

The transportation sector is undergoing three revolutions: shared mobility, autonomous driving, and electrification. When planning the charging infrastructure for electric vehicles, it is critical to consider the potential interactions and synergies among these three emerging systems. This study proposes a framework to optimize charging infrastructure development for increasing electric vehicle (EV) adoption in systems with different levels of autonomous vehicle adoption and ride sharing participation. The proposed model also accounts for the pre-existing charging infrastructure, vehicle queuing at the charging stations, and the trade-offs between building new charging stations and expanding existing ones with more charging ports.Using New York City (NYC) taxis as a case study, we evaluated the optimum charging station configurations for three EV adoption pathways. The pathways include EV adoption in a 1) traditional fleet (non-autonomous vehicles without ride sharing), 2) future fleet (fully autonomous vehicles with ride sharing), and 3) switch-over from traditional to future fleet. Our results show that, EV adoption in a traditional fleet requires charging infrastructure with fewer stations that each has more charging ports, compared to the future fleet which benefits from having more scattered charging stations. Charging will only reduce the service level by 2% for a future fleet with 100% EV adoption. EV adoption can reduce CO₂ emissions of NYC taxis by up to 861 Tones/day for the future fleet and 1100 Tones/day for the traditional fleet.     

Advances in consumer electric vehicle adoption research: A review and research agenda

In spite of the purported positive environmental consequences of electrifying the light duty vehicle fleet, the number of electric vehicles (EVs) in use is still insignificant. One reason for the modest adoption figures is that the mass acceptance of EVs to a large extent is reliant on consumers’ perception of EVs. This paper presents a comprehensive overview of the drivers for and barriers against consumer adoption of plug-in EVs, as well as an overview of the theoretical perspectives that have been utilized for understanding consumer intentions and adoption behavior towards EVs. In addition, we identify gaps and limitations in existing research and suggest areas in which future research would be able to contribute.     

Trends in life cycle greenhouse gas emissions of future light duty electric vehicles

The majority of previous studies examining life cycle greenhouse gas (LCGHG) emissions of battery electric vehicles (BEVs) have focused on efficiency-oriented vehicle designs with limited battery capacities. However, two dominant trends in the US BEV market make these studies increasingly obsolete: sales show significant increases in battery capacity and attendant range and are increasingly dominated by large luxury or high-performance vehicles. In addition, an era of new use and ownership models may mean significant changes to vehicle utilization, and the carbon intensity of electricity is expected to decrease. Thus, the question is whether these trends significantly alter our expectations of future BEV LCGHG emissions.To answer this question, three archetypal vehicle designs for the year 2025 along with scenarios for increased range and different use models are simulated in an LCGHG model: an efficiency-oriented compact vehicle; a high performance luxury sedan; and a luxury sport utility vehicle. While production emissions are less than 10% of LCGHG emissions for today’s gasoline vehicles, they account for about 40% for a BEV, and as much as two-thirds of a future BEV operated on a primarily renewable grid. Larger battery systems and low utilization do not outweigh expected reductions in emissions from electricity used for vehicle charging. These trends could be exacerbated by increasing BEV market shares for larger vehicles. However, larger battery systems could reduce per-mile emissions of BEVs in high mileage applications, like on-demand ride sharing or shared vehicle fleets, meaning that trends in use patterns may countervail those in BEV design.     

Smart charging management for shared autonomous electric vehicle fleets: A Puget Sound case study

Increasingly, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impacts on energy demand and, in turn, drive variation in energy cost and reliability, especially if the charging is unmanaged. This research proposes a smart charging (SC) framework to identify benefits of active SAEV charging management that strategically shifts electricity demand away from high-priced peak hours or towards renewable generation periods. Time of use (TOU), real time pricing (RTP), and solar generation electricity scenarios are tested using an agent-based simulation to study (1) the impact of battery capacity and charging infrastructure type on SAEV fleet performance and operational costs under SC management; (2) the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; (3) the charging infrastructure requirements; and (4) the system efficiency of powering SAEVs with solar generation. A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model and local energy prices. Results suggest that in the absence of electricity price signals, SAEV charging demand is likely to peak the evening, when regional electricity use patterns already indicate high demand. Under SC management, EVs with larger battery sizes are more responsive to low-electricity cost charging opportunities, and have greater potential to reduce total energy related costs (electricity plus charging infrastructure) for a SAEV fleet, especially under RTP structure.     

Effectiveness of incentives on electric vehicle adoption in Norway

Battery Electric vehicles (BEVs) shift pollution off the road and to potentially less damaging and more varied sources than petroleum. Depending on the source of electricity, a transition to electrified personal transportation can dramatically reduce greenhouse gas emissions and air pollutants. However current EVs tend to be more expensive and have shorter range, which can hinder public adoption. Government incentives can be used to alleviate these factors and encourage adoption. Norway has a long history incentivizing BEV adoption including measures such as exemption from roadway tolls, access to charging infrastructure, point of sale tax incentives, and usage of public bus use limited lanes. This paper analyzed the sales of electric vehicles on a regional and municipal basis in Norway and then cross analyzed these with the corresponding local demographic data and incentive measures to attempt to ascertain which factors lead to higher BEV adoption. It was concluded that access to BEV charging infrastructure, being adjacent to major cities, and regional incomes had the greatest predictive power for the growth of BEV sales. It was also concluded that short-range vehicles showed somewhat more income and unemployment sensitivity than long-range vehicles. Toll exemptions and the right to use bus designated lanes do not seem to have statistically significant predictive power for BEV sales in our linear municipal-level models, but this could be due to neighboring major cities containing those incentive features.     

Exploring electric vehicle charging patterns: Mixed usage of charging infrastructure

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

Survey of the operation and system study on wireless charging electric vehicle systems

This survey investigates the state-of-the-art in operations and systems-related studies of wireless charging electric vehicles (EVs). The wireless charging EV is one of emerging transportation systems in which the EV’s battery is charged via wireless power transfer (WPT) technology. The system makes use of charging infrastructure embedded under the surface of the road that transfers electric power to the vehicle while it is in transit. The survey focuses on studies related to both dynamic and quasi-dynamic types of wireless charging EV – charging while in motion and while temporarily stopped during a trip, respectively. The ability to charge EVs while in transit has raised numerous operations and systems issues that had not been observed in conventional EVs. This paper surveys the current research on such issues, including decisions on the allocation of charging infrastructure; cost and benefit analyses; billing and pricing; and other supporting operations and facilities. This survey consists of three parts. The first provides an orienting review of terminology specific to wireless charging EVs; it also reviews some past and ongoing developments and implementations of wireless charging EVs. The second part surveys the research on the operations and systems issues prompted by wireless charging EVs. The third part proposes future research directions. The goal of the survey is to provide researchers and practitioners with an overview of research trends and to provide a guide to promising future research directions.     

Linking Chinese cultural values and the adoption of electric vehicles: The mediating role of ethical evaluation

Electric vehicles (EVs) have been regarded as one of the most prominent green technologies, touted to help reduce global energy consumption and carbon emissions. China advocates the development of EVs to address the increasing challenges of climate change, urban air pollution and energy security, but consumers’ enthusiasm for adopting EVs remains low. In this paper, we present a concept model that hypothesizes Chinese cultural values as a key to understanding Chinese consumers’ intention to adopt EVs. Based on a nationwide online survey in China, this study explores Chinese consumers’ attitudes toward two types of EVs—battery electric vehicles and plug-in hybrid electric vehicles—by identifying the influence of the human–nature relationship, long-term orientation, face consciousness, and risk attitude, as well as the mediating effect of deontological ethical evaluation in decision-making. The results suggest that public policy and social marketing efforts should pay more attention to the role of cultural values when promoting environmentally sustainable technologies, and importantly, that the promotion efforts should differ for different cultural elements and products with different levels of innovativeness.     

Modeling the effect of electric vehicle adoption on pedestrian traffic safety: An agent-based approach

When operated at low speeds, electric and hybrid vehicles have created pedestrian safety concerns in congested areas of various city centers, because these vehicles have relatively silent engines compared to those of internal combustion engine vehicles, resulting in safety issues for pedestrians and cyclists due to the lack of engine noise to warn them of an oncoming electric or hybrid vehicle. However, the driver behavior characteristics have also been considered in many studies, and the high end-prices of electric vehicles indicate that electric vehicle drivers tend to have a higher prosperity index and are more likely to receive a better education, making them more alert while driving and more likely to obey traffic rules. In this paper, the positive and negative factors associated with electric vehicle adoption and the subsequent effects on pedestrian traffic safety are investigated using an agent-based modeling approach, in which a traffic micro-simulation of a real intersection is simulated in 3D using AnyLogic software. First, the interacting agents and dynamic parameters are defined in the agent-based model. Next, a 3D intersection environment is created to integrate the agent-based model into a visual simulation, where the simulation records the number of near-crashes occurring in certain pedestrian crossings throughout the virtual time duration of a year. A sensitivity analysis is also carried out with 9000 subsequent simulations performed in a supercomputer to account for the variation in dynamic parameters (ambient sound level, vehicle sound level, and ambient illumination). According to the analysis, electric vehicles have a 30% higher pedestrian traffic safety risk than internal combustion engine vehicles under high ambient sound levels. At low ambient sound levels, however, electric vehicles have only a 10% higher safety risk for pedestrians. Low levels of ambient illumination also increase the number of pedestrians involved in near-crashes for both electric vehicles and combustion engine vehicles.     

Powertrain technologies and their impact on greenhouse gas emissions in key car markets

The role alternative car technologies may play in effectively tackling the problem of climate change is still highly uncertain. This paper aims at investigating possible impacts of car powertrain technologies on future energy demand and its corresponding greenhouse gas emissions until 2030. A system dynamics model covering nine car technologies in China, France, Germany, India, Japan and the United States was applied, with a focus on electric cars. Four main scenarios are constructed and sensitivity analysis undertaken. Greenhouse gas emissions from cars in the six countries are simulated to reach up to 2.6 gigatonnes in 2030 (a 13–32% increase between 2020 and 2030, depending on the scenario). The main conclusion from model-based policy analysis is that electric cars may have a positive contribution to emissions mitigation in the passenger road transport system. However, greenhouse gas emissions from cars arising from the combined effect of car manufacturing and scrappage and electricity generation processes are expected to grow more dramatically. As a result, actions that support both low-emission (re-)manufacturing and clean electricity generation are needed. These results complement accurate but static life cycle assessments and open the discussion for dynamic model assumptions.     

Assessing public opinions of and interest in new vehicle technologies: An Austin perspective

Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever-evolving transportation system. Anticipating public acceptance and adoption of these technologies is important. A recent internet-based survey polled 347 Austinites to understand their opinions on smart-car technologies and strategies. Results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7253) appears to be much higher than that for adding partial (Level 3) automation ($3300) to their current vehicles.Ordered probit and other model specifications estimate the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding various automation technologies and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, who live in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.     

Impacts of replacement of engine powered vehicles by electric vehicles on energy consumption and CO2 emissions

This paper evaluates the impacts on energy consumption and carbon dioxide (CO₂) emissions from the introduction of electric vehicles into a smart grid, as a case study. The AVL Cruise software was used to simulate two vehicles, one electric and the other engine-powered, both operating under the New European Driving Cycle (NEDC), in order to calculate carbon dioxide (CO₂) emissions, fuel consumption and energy efficiency. Available carbon dioxide data from electric power generation in Brazil were used for comparison with the simulated results. In addition, scenarios of gradual introduction of electric vehicles in a taxi fleet operating with a smart grid system in Sete Lagoas city, MG, Brazil, were made to evaluate their impacts. The results demonstrate that CO₂ emissions from the electric vehicle fleet can be from 10 to 26 times lower than that of the engine-powered vehicle fleet. In addition, the scenarios indicate that even with high factors of CO₂ emissions from energy generation, significant reductions of annual emissions are obtained with the introduction of electric vehicles in the fleet.     

Eco-driving behaviors of electric vehicle users: A survey study

Previous research has shown that electric vehicle (EV) users could behave differently compared to internal combustion engine vehicle (ICEV) drivers due to their consciousness or practices of eco-driving, but very limited research has fully investigated this assumption. This research explores this topic through investigating EV drivers’ eco-driving behaviors and motivations. We first conducted a questionnaire survey on EV drivers’ driving behavior and some hypothetical decisions of their driving. It indicates various characteristics between EV and ICEV commuters, including self-reported daily driving habits, preferences of route choices, tradeoff between travel time and energy saving, and adoption of in-vehicle display (IVD) technologies. Then, through statistical analysis with Fisher’s exact test and Mann-Whitney U test, this research reveals that, compared to ICEV drivers, EV drivers possess significantly calmer driving maneuvers and more fuel-efficient driving habits such as trip chaining. The survey data also show that EV drivers are much more willing to save energy in compensation of travel time. Furthermore, the survey data indicate that EV drivers are more willing to adopt eco-friendly IVD technologies. All these findings are expected to improve the understanding of some unique behavior found in EV drivers.     

Routing aspects of electric vehicle drivers and their effects on network performance

This study investigates the routing aspects of battery electric vehicle (BEV) drivers and their effects on the overall traffic network performance. BEVs have unique characteristics such as range limitation, long battery recharging time, and recuperation of energy lost during the deceleration phase if equipped with regenerative braking system (RBS). In addition, the energy consumption rate per unit distance traveled is lower at moderate speed than at higher speed. This raises two interesting questions: (i) whether these characteristics of BEVs will lead to different route selection compared to conventional internal combustion engine vehicles (ICEVs), and (ii) whether such route selection implications of BEVs will affect the network performance. With the increasing market penetration of BEVs, these questions are becoming more important. This study formulates a multi-class dynamic user equilibrium (MCDUE) model to determine the equilibrium flows for mixed traffic consisting of BEVs and ICEVs. A simulation-based solution procedure is proposed for the MCDUE model. In the MCDUE model, BEVs select routes to minimize the generalized cost which includes route travel time, energy related costs and range anxiety cost, and ICEVs to minimize route travel time. Results from numerical experiments illustrate that BEV drivers select routes with lower speed to conserve and recuperate battery energy while ICEV drivers select shortest travel time routes. They also illustrate that the differences in route choice behavior of BEV and ICEV drivers can synergistically lead to reduction in total travel time and the network performance towards system optimum under certain conditions.     

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

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.     

The relative importance of price and driving range on electric vehicle adoption: Los Angeles case study

Electric vehicles (EVs) are still a maturing technology. Barriers to their adoption include price and range anxiety. EV batteries are significant in determining both EV prices and costs. In this work, we focus on the impact of a high-capacity battery and EV rebates on an EV ecosystem. Using survey data from Los Angeles, California, we simulate different cases of battery costs and prices by means of an agent-based EV ecosystem model. We find that even in Los Angeles, a geographically spread out city, the price of EVs is a more significant barrier to adoption than EV range. In fact, even a quintupling of battery size at no additional costs improves EV adoption by only 5 %. Therefore, policy makers should focus more on affordability than range in promoting EV adoption.     

A two-sided market platform analysis for the electric vehicle adoption: Firm strategies and policy design

This study explores how to facilitate the electric vehicle (EV) diffusion from a two-sided market platform competition. We develop a stylized model depicting the platform competition between electric and gasoline vehicles by combining indirect network effects of consumer and energy supplier sides as well as vehicle manufacturers’ profits. The findings of this study provide several meaningful strategic and policy implications for EV manufacturers and policymakers who wish to enhance EV diffusion. First, EV sales are significantly influenced by indirect network effects from the energy supplier side to the consumer side, and vice versa. This implies that EV manufacturers who wish to boost EV diffusion should implement a strategy providing energy suppliers with incentives to willingly join the EV platform. Second, the dynamic nature of the effects of energy costs on platform competition might render counter-intuitive evidence that the drop in oil prices does not always negatively influence EV sales. This requires EV manufacturers to prepare a contingent strategy adjusting to such unexpected conditions. Third, governments should consider the energy supplier side as well as the consumer side in designing EV diffusion policies. When governments have a very challenging EV diffusion target, a balanced policy, which treats both gasoline and electric vehicle technologies fairly, may be more effective than a consumer subsidy policy.     

Greenhouse gas emissions associated with electric vehicle charging: The impact of electricity generation mix in a developing country

Since 2012, the government has been promoting the electric vehicles and the development of related infrastructure to encourage local automakers to explore into the alternatively powered vehicles. However, the benefits of grid-dependent EVs can only be harvested under the condition that their use is coupled with a low carbon electricity grid. Thus, it is an additional challenge for Malaysia's that are largely dependent on fossil fuels for electricity generation. The object of this paper is to perform a well-to-wheel life cycle assessment for calculating the greenhouse gas emissions attributable to the usage of ICEVs, HEVs and EVs in Malaysian scenario. These emission calculations will provide the best information for policymakers, researchers, and investors to make appropriate and effective decisions on policies, research and investments in future transport energy. The results show that running EVs with national grid will produce an average of 7% more GHG emissions than HEVs at the same distance. However, they will produce an average of 19% less GHG emissions than the ICEVs. Overall the GHG emissions produced through the usage of EVs are substantial based on the well-to-wheel analysis, as the environmental profile of EVs is linked with the national grid. Therefore, in order to harvest the benefit of EVs towards climate change and global warming mitigation, massive modernization and transformation should be taken for the development of the national grid towards greener sources.     

Dynamic ride sharing using traditional taxis and shared autonomous taxis: A case study of NYC

This study analyzes the potential benefits and drawbacks of taxi sharing using agent-based modeling. New York City (NYC) taxis are examined as a case study to evaluate the advantages and disadvantages of ride sharing using both traditional taxis (with shifts) and shared autonomous taxis. Compared to existing studies analyzing ride sharing using NYC taxi data, our contributions are that (1) we proposed a model that incorporates individual heterogeneous preferences; (2) we compared traditional taxis to autonomous taxis; and (3) we examined the spatial change of service coverage due to ride sharing. Our results show that switching from traditional taxis to shared autonomous taxis can potentially reduce the fleet size by 59% while maintaining the service level and without significant increase in wait time for the riders. The benefit of ride sharing is significant with increased occupancy rate (from 1.2 to 3), decreased total travel distance (up to 55%), and reduced carbon emissions (up to 866 metric tonnes per day). Dynamic ride sharing, wich allows shared trips to be formed among many groups of riders, up to the taxi capacity, increases system flexibility. Constraining the sharing to be only between two groups limits the sharing participation to be at the 50–75% level. However, the reduced fleet from ride sharing and autonomous driving may cause taxis to focus on areas of higher demands and lower the service levels in the suburban regions of the city.