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.     

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.     

Are multi-car households better suited for battery electric vehicles? – Driving patterns and economics in Sweden and Germany

Battery electric vehicles (BEVs) could reduce CO₂ emissions from the transport sector but their limited electric driving range diminishes their utility to users. The effect of the limited driving range can be reduced in multi-car households where users could choose between a BEV and a conventional car for long-distance travel. However, to what extent the driving patterns of different cars in a multi-car household’s suit the characteristics of a BEV needs further analysis. In this paper we analyse the probability of daily driving above a fixed threshold for conventional cars in current Swedish and German car driving data. We find second cars in multi-car households to require less adaptation and to be better suited for BEV adoption compared to first cars in multi-car households as well as to cars in single-car households. Specifically, the share of second cars that could fulfil all their driving is 20 percentage points higher compared to first cars and cars from single-car households. This result is stable against variation of driving range and of the tolerated number of days requiring adaptation. Furthermore, the range needed to cover all driving needs for about 70% of the vehicles is only 220 km for second cars compared to 390 km for the average car. We can further confirm that second cars have higher market viability from a total cost of ownership perspective. Here, the second cars achieve a 10 percentage points higher market share compared to first cars, and to cars in single-car households for Swedish economic conditions, while for Germany the corresponding figure is 2 percentage points. Our results are important for understanding the market viability of current and near-future BEVs.     

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.     

Effects of providing total cost of ownership information on consumers’ intent to purchase a hybrid or plug-in electric vehicle

Energy-saving technologies have a difficult time being widely accepted in the marketplace when they have a high initial purchase price and deferred financial benefits. Consumers might not realize that, in the long-run, the financial benefits from reduced energy consumption offset much or all of the initial price premium. One strategy to address consumer misconception of this advantage is to supply information on the “total cost of ownership”, a metric which accounts for the purchase price, the cost of the fuel, and other costs over the ownership period. In this article, we investigate how providing information on five-year fuel cost savings and total cost of ownership affects the stated preferences of consumers to purchase a gasoline, conventional hybrid, plug-in hybrid, or battery electric vehicle. Through an online survey with an embedded experimental design using distinct labels, we find that respondent rankings of vehicles are unaffected by information on five-year fuel cost savings. However, adding information about total cost of ownership increases the probability that small/mid-sized car consumers express a preference to acquire a conventional hybrid, plug-in hybrid, or a battery-electric vehicle. No such effect is found for consumers of small sport utility vehicles. Our results are consistent with other findings in the behavioral economics literature and suggest that further evaluation of the effects of providing consumers with information on the total cost of vehicle ownership is warranted.