Optimal deployment of public charging stations for plug-in hybrid electric vehicles

This paper develops an equilibrium modeling framework that captures the interactions among availability of public charging opportunities, prices of electricity, and destination and route choices of plug-in hybrid electric vehicles (PHEVs) at regional transportation and power transmission networks coupled by PHEVs. The modeling framework is then applied to determine an optimal allocation of a given number of public charging stations among metropolitan areas in the region to maximize social welfare associated with the coupled networks. The allocation model is formulated as a mathematical program with complementarity constraints, and is solved by an active-set algorithm. Numerical examples are presented to demonstrate the models and offer insights on the equilibrium of the coupled transportation and power networks, and optimally allocating resource for public charging infrastructure.     

A general corridor model for designing plug-in electric vehicle charging infrastructure to support intercity travel

This paper proposes to optimally configure plug-in electric vehicle (PEV) charging infrastructure for supporting long-distance intercity travel using a general corridor model that aims to minimize a total system cost inclusive of infrastructure investment, battery cost and user cost. Compared to the previous work, the proposed model not only allows realistic patterns of origin–destination demands, but also considers flow-dependent charging delay induced by congestion at charging stations. With these extensions, the model is better suited to performing a sketchy design of charging infrastructure along highway corridors. The proposed model is formulated as a mixed integer program with nonlinear constraints and solved by a specialized metaheuristic algorithm based on Simulated Annealing. Our numerical experiments show that the metaheuristic produces satisfactory solutions in comparison with benchmark solutions obtained by a mainstream commercial solver, but is more computationally tractable for larger problems. Noteworthy findings from numerical results are: (1) ignoring queuing delay inducted by charging congestion could lead to suboptimal configuration of charging infrastructure, and its effect is expected to be more significant when the market share of PEVs rises; (2) in the absence of the battery cost, it is important to consider the trade-off between the costs of charging delay and the infrastructure; and (3) building long-range PEVs with the current generation of battery technology may not be cost effective from the societal point of view.     

Impacts of driving patterns on tank-to-wheel energy use of plug-in hybrid electric vehicles

We evaluate the implications of a range of driving patterns on the tank-to-wheel energy use of plug-in hybrid electric vehicles. The driving patterns, which reflect short distance, low speed, and congested city driving to long distance, high speed, and uncongested highway driving, are estimated using an approach that involves linked traffic assignment and vehicle motion models. We find substantial variation in tank-to-wheel energy use of plug-in hybrid electric vehicles across driving patterns. Tank-to-wheel petroleum energy use on a per kilometer basis is lowest for the city and highest for the highway driving, with the opposite holding for a conventional internal combustion engine vehicle.     

Deploying public charging stations for electric vehicles on urban road networks

This paper explores how to optimally locate public charging stations for electric vehicles on a road network, considering drivers’ spontaneous adjustments and interactions of travel and recharging decisions. The proposed approach captures the interdependency of different trips conducted by the same driver by examining the complete tour of the driver. Given the limited driving range and recharging needs of battery electric vehicles, drivers of electric vehicles are assumed to simultaneously determine tour paths and recharging plans to minimize their travel and recharging time while guaranteeing not running out of charge before completing their tours. Moreover, different initial states of charge of batteries and risk-taking attitudes of drivers toward the uncertainty of energy consumption are considered. The resulting multi-class network equilibrium flow pattern is described by a mathematical program, which is solved by an iterative procedure. Based on the proposed equilibrium framework, the charging station location problem is then formulated as a bi-level mathematical program and solved by a genetic-algorithm-based procedure. Numerical examples are presented to demonstrate the models and provide insights on public charging infrastructure deployment and behaviors of electric vehicles.     

Retrofitted plug-in hybrid vehicles: Results of NYIT drive share program

This paper presents results from a plug-in hybrid vehicle drive share program involving retrofitted hybrid electric vehicles. A potential for high fuel efficiency is indicated, however, the average fuel efficiency was only marginally better than conventional hybrid vehicles. This is due to the majority of vehicle miles traveled occurring on trips outside the “all electric” range and very short trips where fuel consumption is dominated by emissions control strategies. The work also considers the availability of the battery for vehicle to grid services and finds that there are a large number of trips in the afternoon period, typically when electrical demand is at a peak. Vehicle charging activity also tended towards daytime activity, contrary to the oft-assumed off-peak charging pattern.     

Prospects for plug-in hybrid electric vehicles in the United States and Japan: A general equilibrium analysis

The plug-in hybrid electric vehicle (PHEV) may offer a potential near term, low-carbon alternative to today’s gasoline- and diesel-powered vehicles. A representative vehicle technology that runs on electricity in addition to conventional fuels was introduced into the MIT Emissions Prediction and Policy Analysis (EPPA) model as a perfect substitute for internal combustion engine (ICE-only) vehicles in two likely early-adopting markets, the United States and Japan. We investigate the effect of relative vehicle cost and all-electric range on the timing of PHEV market entry in the presence and absence of an advanced cellulosic biofuels technology and a strong (450 ppm) economy-wide carbon constraint. Vehicle cost could be a significant barrier to PHEV entry unless fairly aggressive goals for reducing battery costs are met. If a low-cost PHEV is available we find that its adoption has the potential to reduce CO₂ emissions, refined oil demand, and under a carbon policy the required CO₂ price in both the United States and Japan. The emissions reduction potential of PHEV adoption depends on the carbon intensity of electric power generation. Thus, the technology is much more effective in reducing CO₂ emissions if adoption occurs under an economy-wide cap and trade system that also encourages low-carbon electricity generation.     

Anticipating plug-in hybrid vehicle energy impacts in California: Constructing consumer-informed recharge profiles

Plug-in hybrid electric vehicles (PHEVs) can be powered by gasoline, grid electricity, or both. To explore potential PHEV energy impacts, a three-part survey instrument collected data from new vehicle buyers in California. We combine the available information to estimate the electricity and gasoline use under three recharging scenarios. Results suggest that the use of PHEV vehicles could halve gasoline use relative to conventional vehicles. Using three scenarios to represent plausible conditions on PHEV drivers’ recharge patterns (immediate and unconstrained, universal workplace access, and off-peak only), tradeoffs are described between the magnitude and timing of PHEV electricity use. PHEV electricity use could be increased through policies supporting non-home recharge opportunities, but this increase occurs during daytime hours and could contribute to peak electricity demand. Deferring all recharging to off-peak hours could eliminate all additions to daytime electricity demand from PHEVs, although less electricity is used and less gasoline displaced.     

A corridor-centric approach to planning electric vehicle charging infrastructure

The transition to electric vehicles (EV) faces two major barriers. On one hand, EV batteries are still expensive and limited by range, owing to the lack of technology breakthrough. On the other hand, the underdeveloped supporting infrastructure, particularly the lack of fast refueling facilities, makes EVs unsuitable for medium and long distance travel. The primary purpose of this study is to better understand these hurdles and to develop strategies to overcome them. To this end, a conceptual optimization model is proposed to analyze travel by EVs along a long corridor. The objective of the model is to select the battery size and charging capacity (in terms of both the charging power at each station and the number of stations needed along the corridor) to meet a given level of service in such a way that the total social cost is minimized. Two extensions of the base model are also considered. The first relaxes the assumption that the charging power at the stations is a continuous variable. The second variant considers battery swapping as an alternative to charging. Our analysis suggests that (1) the current paradigm of charging facility development that focuses on level 2 charging delivers poor level of service for long distance travel; (2) the level 3 charging method is necessary not only to achieve a reasonable level of service, but also to minimize the social cost; (3) investing on battery technology to reduce battery cost is likely to have larger impacts on reducing the charging cost; and (4) battery swapping promises high level of service, but it may not be socially optimal for a modest level of service, especially when the costs of constructing swapping and charging stations are close.     

Mainstream consumers driving plug-in battery-electric and plug-in hybrid electric cars: A qualitative analysis of responses and evaluations

Plug-in electric vehicles can potentially emit substantially lower CO₂ emissions than internal combustion engine vehicles, and so have the potential to reduce transport emissions without curtailing personal car use. Assessing the potential uptake of these new categories of vehicles requires an understanding of likely consumer responses. Previous in-depth explorations of appraisals and evaluations of electric vehicles have tended to focus on ‘early adopters’, who may not represent mainstream consumers. This paper reports a qualitative analysis of responses to electric cars, based on semi-structured interviews conducted with 40 UK non-commercial drivers (20 males, 20 females; age 24-70 years) at the end of a seven-day period of using a battery electric car (20 participants) or a plug-in hybrid car (20 participants). Six core categories of response were identified: (1) cost minimisation; (2) vehicle confidence; (3) vehicle adaptation demands; (4) environmental beliefs; (5) impression management; and, underpinning all other categories, (6) the perception of electric cars generally as ‘work in progress’ products. Results highlight potential barriers to the uptake of current-generation (2010) plug-in electric cars by mainstream consumers. These include the prioritization of personal mobility needs over environmental benefits, concerns over the social desirability of electric vehicle use, and the expectation that rapid technological and infrastructural developments will make current models obsolete. Implications for the potential uptake of future electric vehicles are discussed.     

An activity-based assessment of the potential impacts of plug-in hybrid electric vehicles on energy and emissions using 1-day travel data

This paper assesses the potential energy profile impacts of plug-in hybrid electric vehicles and estimates gasoline and electricity demand impacts for California of their adoption. The results are based on simulations replicating vehicle usage patterns reported in 1-day activity and travel diaries based on the 2000–2001 California Statewide Household Travel Survey. Four charging scenarios are examined. We find that circuit upgrades to 240 V not only bring faster charging times but also reduce charging time differences between PHEV20 and PHEV60; home charging can potentially service 40–50% of travel distances with electric power for PHEV20 and 70–80% for PHEV60; equipping public parking spaces with charging facilities, can potentially convert 60–70% of mileage from fuel to electricity for PHEV20, and 80–90% for PHEV60; and afternoons are found to be exposed to a higher level of emissions.     

Simulating the household plug-in hybrid electric vehicle distribution and its electric distribution network impacts

This paper presents a multi agent-based simulation framework for modeling spatial distribution of plug-in hybrid electric vehicle ownership at local residential level, discovering “plug-in hybrid electric vehicle hot zones” where ownership may quickly increase in the near future, and estimating the impacts of the increasing plug-in hybrid electric vehicle ownership on the local electric distribution network with different charging strategies. We use Knox County, Tennessee as a case study to highlight the simulation results of the agent-based simulation framework.     

Evaluation of alternative battery technologies for a solar assist plug-in hybrid electric tractor

This paper looks at the various battery technologies available for use in solar assist plug-in hybrid electric tractors In this context, solar assist plug-in hybrid electric tractors are those that may be used in light-duty agricultural operations. To determine the most suitable battery, four common technologies are analyzed looking at the environmental life-cycle effects and costs.     

Impact of vehicle usage on consumer choice of hybrid electric vehicles

We analyze the vehicle usage and consumer profile attributes extracted from both National Household Travel Survey and Vehicle Quality Survey data to understand the impact of vehicle usage upon consumers’ choices of hybrid electric vehicles in the US. In addition, the key characteristics of hybrid vehicle drivers are identified to determine the market segmentations of hybrid electric vehicles and the critical attributes to include in the choice model. After a compatibility test of two datasets, a pooled choice model combining both data sources illustrates the significant influences of vehicle usage upon consumers’ choices of hybrid electric vehicles. Even though the data-bases have in the past been used independently to study travel behavior and vehicle quality ratings, here we use them together.     

Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data

This paper studies electric vehicle charger location problems and analyzes the impact of public charging infrastructure deployment on increasing electric miles traveled, thus promoting battery electric vehicle (BEV) market penetration. An activity-based assessment method is proposed to evaluate BEV feasibility for the heterogeneous traveling population in the real world driving context. Genetic algorithm is applied to find (sub)optimal locations for siting public charging stations. A case study using the GPS-based travel survey data collected in the greater Seattle metropolitan area shows that electric miles and trips could be significantly increased by installing public chargers at popular destinations, with a reasonable infrastructure investment.     

Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications

Transportation - The market penetration rate of electric vehicle (EV) is on the rise globally. However, the use behaviors of private EVs have not been well understood, in part due to the lack of...     

Home charge timing choice behaviors of plug-in hybrid electric vehicle users under a dynamic electricity pricing scheme

This paper examines choice behaviors pertaining to the time at which users of plug-in hybrid electric vehicle with 24 km electric range charge their vehicles after arriving at home under a dynamic electricity pricing scheme. The following mutually exclusive alternatives are presented: no charging, charging immediately after arriving at home, charging at the cheapest time, and charging at other times. Four versions of a mixed logit model with unobserved heterogeneity are applied to panel data on vehicle usage from 9 households with 2226 observations in Toyota City. Estimation results suggest that users’ willingness to charge become stronger with increasing driving distance when the driving distance is less than the electric range of 24 km, while tend not to charge when the driving distance is longer than the electric range. Users who return home at the cheapest time or during the day are willing to charge immediately after arriving at home. Electricity prices significantly affect choices to charge at the cheapest time for all users, and stay-at-home mother users and users returning home in the evening tend to charge at the cheapest time. Users returning home in the evening also tend to charge at other times, and being accustomed to charge at a certain time increases the probability of charging at other times. In addition, considerable variations are found across individuals with respect to their preferences for charge timing alternatives as well as for electricity prices.     

A multi-period optimization model for the deployment of public electric vehicle charging stations on network

A multi-period multipath refueling location model is developed to expand public electric vehicle (EV) charging network to dynamically satisfy origin–destination (O–D) trips with the growth of EV market. The model captures the dynamics in the topological structure of network and determines the cost-effective station rollout scheme on both spatial and temporal dimensions. The multi-period location problem is formulated as a mixed integer linear program and solved by a heuristic based on genetic algorithm. The model and heuristic are justified using the benchmark Sioux Falls road network and implemented in a case study of South Carolina. The results indicate that the charging station rollout scheme is subject to a number of major factors, including geographic distributions of cities, vehicle range, and deviation choice, and is sensitive to the types of charging station sites.     

External costs of electric vehicles

Electric vehicles (EV) are often considered a promising technology to decrease external costs of road transport. Therefore, main external cost components are estimated for EV and internal combustion engine vehicles (ICEV). These include costs of accidents, air pollution, climate change, noise, and congestion. All components are estimated over the product lifetime and, where appropriate, differentiated according to fuel type, vehicle size as well as emission location and time. The advantage of this differentiation is, however, compensated by high uncertainties of most cost estimates. Overall, the external costs of EV and ICEV do not differ significantly. Only for climate change, local air pollutants in congested inner-cities, and noise some advantageous effects can be observed for EV. The advantages depend strongly on the national electricity power plant portfolio and potentially also on the charging strategy. Controlled charging might allow for higher emission reductions than uncontrolled charging of EV.     

The design of electric vehicle charging network

The promotion of Electric Vehicles (EVs) has become a key measure of the governments in their attempt to reduce greenhouse gas emissions. However, range anxiety is a big barrier for drivers to choose EVs over traditional vehicles. Installing more charging stations in appropriate locations can relieve EV drivers’ range anxiety. To determine the locations of public charging stations, we propose two optimization models for two different charging modes - fast and slow charging, which aim at minimizing the total cost while satisfying certain coverage goal. Instead of using discrete points, we use geometric objects to represent charging demands. Importantly, to resolve the partial coverage problem (PCP) for networks, we extend the polygon overlay method to split the demands on the road network. After applying the models to Greater Toronto and Hamilton Area (GTHA) and to Downtown Toronto, we show that the proposed models are practical and effective in determining the locations of charging stations. Moreover, they can eliminate PCP and provide much more accurate results than the complementary partial coverage method (CP).     

Conventional,hybrid and electric vehicles for Australian driving conditions. Part 2: Life cycle CO2-e emissions

This paper is the second of a two part study which quantifies the economic and greenhouse performance of conventional, hybrid and fully electric passenger vehicles operating in Australian driving conditions. This second study focuses on the life cycle greenhouse gas emissions. Two vehicle sizes are considered, Class-B and Class-E, which bracket the large majority of passenger vehicles on Australian roads.Using vehicle simulation models developed in the first study, the trade-offs between the ability of increasingly electric powertrains in curtailing the tailpipe emissions and the corresponding rise in the embedded vehicle emissions have been evaluated. The sensitivity of the life cycle emissions to fuel, electricity and the change in the energy mix are all considered. In conjunction with the total cost of ownership calculated in the companion paper, this allows the cost of mitigating life cycle greenhouse gas emissions through electrification of passenger transport to be estimated under different scenarios. For Class-B vehicles, fully electric vehicles were found to have a higher total cost of ownership and higher life cycle emissions than an equivalent vehicle with an internal combustion engine. For Class-E vehicles, hybrids are found to be the most cost effective whilst also having lowest life cycle emissions under current conditions. Further, hybrid vehicles also exhibit little sensitivity in terms of greenhouse emissions and cost with large changes in system inputs.