Plug-in Electric Vehicles Charging Infrastructure Research Articles

The potential for community financed electric vehicle charging infrastructure

Charging infrastructure is one of the key ingredients necessary to further increase the market uptake of plug-in electric vehicles (PEVs). Most public charging infrastructure in Europe is not profitable, and 42% of European households reside in dwellings with no possibility to install a private charger, which are key limitations to the build-up of charging infrastructure. This work presents and analyzes an alternative business model for ‘community-owned charging points’ that combines many of the convenience advantages of private charging with the cost savings of a joint purchase. Survey data from 3,131 households in Austria is used to identify barriers to PEV charging and charging infrastructure usage, and investigate consumers’ preferences with respect to community-owned charging points. Four key target groups of customers for such a business model are identified based on their socio-economic characteristics, purchase history, and expressed level of environmental concerns, including current PEV owners, high income households, photovoltaic (PV) owners, and environmentalists. Identified distinct preferences for the configuration of a collective charging station amongst these groups include lower importance attached to flexibility to charge the vehicles at any time by current PEV owners, higher sensitivity to cost per charge of PV owners, and strong preferences for comfort and flexibility demonstrated by high income households.

Dynamic Pricing for Differentiated PEV Charging Services Using Deep Reinforcement Learning

With the increasing popularity of plug-in electric vehicles (PEV), charging infrastructure becomes widely available and offers multiple services to PEV users. Each charging service has a distinct quality of service (QoS) level that matches user expectations. The charging service demand is interdependent, i.e., the demand for one service is often affected by the prices of others. Dynamic pricing of charging services is a coordination mechanism for QoS satisfaction of service classes. In this article, we propose a differentiated pricing mechanism for a multiservice PEV charging infrastructure (EVCI). The proposed framework motivates PEV users to avoid over-utilization of particular service classes. Currently, most of dynamic pricing schemes require full knowledge of the customer-side information; however, such information is stochastic, non-stationary, and expensive to collect at scale. Our proposed pricing mechanism utilizes model-free deep reinforcement learning (RL) to learn and improve automatically without an explicit model of the environment. We formulate our framework to adopt the twin delayed deep deterministic policy gradient (TD3) algorithm. The simulation results demonstrate that the proposed RL-based differentiated pricing scheme can adaptively adjust service pricing for a multiservice EVCI to maximize charging facility utilization while ensuring service quality satisfaction.

Refueling infrastructure planning in intercity networks considering route choice and travel time delay for mixed fleet of electric and conventional vehicles

The range anxiety has been a major factor that affects the market acceptance of electric vehicles. Even with the recent development of battery technologies, a lack of charging stations and range anxiety are still significant concerns, specifically for intercity trips. This calls for more investments in building charging stations and advancing battery technologies to increase the market share of electric vehicles and improve sustainability. This study suggests a configuration for plug-in electric vehicle charging infrastructure to support long-distance intercity trips of electric vehicles at the network level. A model is proposed to minimize the total system cost including infrastructure investment (building charging stations/spots) and travel time delays (charging time, waiting time in the queue, and detour time to access charging stations). This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations. Furthermore, the proposed model, which is formulated as a mixed-integer program with nonlinear constraints, solves the optimization problem at the network level. At the network level, impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles need to be considered. To this end, a traffic assignment module is integrated with a simulated annealing algorithm. The numerical experiments show a satisfactory application of the model for a full-scale case study (intercity network in Michigan). The solution quality and efficiency of the proposed solution algorithm are evaluated against those of an enumeration approach for a small case study. The results suggest that even for the current market share and charging stations’ setting, a significant investment is needed to support intercity trips without range anxiety issues and with acceptable delays. Furthermore, through sensitivity analyses, the required infrastructure and battery investments to support intercity trips with acceptable delays are established for hypothetical increased market shares and battery size in the future.

QoS-Aware Capacity Planning of Networked PEV Charging Infrastructure

Plug-in electric vehicle (PEV) charging infrastructure is necessary to accommodate the rapid increase in PEV penetration rate. Capacity planning of PEV charging infrastructure (EVCI) must ensure not only a satisfactory charging service for PEV users but also a reliable operation of the power grid. In this paper, we propose a quality-of-service (QoS) aware capacity planning of EVCI. In particular, the proposed framework accounts for the link between the charging QoS and the power distribution network (PDN) capability. Towards this end, we firstly optimize charging facility sizes to achieve a targeted QoS level. Then, we minimize the integration cost for the PDN by attaining the most cost-effective allocation of the energy storage systems (ESSs) and/or upgrading the PDN substation and feeders. Additionally, we capture the correlation between the occupation levels of neighboring charging facilities and the blocked PEV user behaviors. We model the EVCI as a queuing network with finite capacity, and utilize the non-stationary queuing models to study the temporal variability of the PEV charging demand. A network of charging facilities is used to demonstrate the effectiveness of the proposed framework.

PEV Charging Infrastructure Siting Based on Spatial–Temporal Traffic Flow Distribution

Plug-in electric vehicles (PEVs) offer a solution to reduce greenhouse gas emissions and decrease fossil fuel consumption. PEV charging infrastructure siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locations. Thus, the various types of charging facilities should be located based on an accurate location estimation of the potential PEV charging demand. In this paper, we propose a spatial–temporal flow capturing location model. This model determines the locations of various types of charging facilities based on the spatial–temporal distribution of traffic flows. We utilize the dynamic traffic assignment model to estimate the time-varying traffic flows on the road transportation network. Then, we cluster the traffic flow dataset into distinct categories using the Gaussian mixture model and site each type of charging facilities to capture a specific traffic pattern. We formulate our siting model as an mixed integer linear programming optimization problem. The model is evaluated based on two benchmark transportation networks, and the simulation results demonstrate the effectiveness of the proposed model.

An early look at plug-in electric vehicle adoption in disadvantaged communities in California

Prior research on plug-in electric vehicle (PEV) adoption has revealed that early adopters tend to be wealthy consumers, this may mean that the benefits of PEVs are not being equitably distributed. Extensive research has shown that low-income and minority commutes are disproportionately impacted by environmental and transportation injustice. PEVs can contribute to importing air quality and could provide lower cost and more reliable transportation to low-income and minority communities if they are deployed there. This paper takes an early quantitative look at PEV adoption in disadvantaged communities (DACs), which are census tracts in California that suffer from a combination of economic barriers and environmental burden. We use six datasets to examine PEV market share, socioeconomic characteristics of PEV owners, and PEV charging infrastructure. Analysis confirms that adoption of both new and used PEVs in DACs occurs at very low rates - 5.7% and 8.7% of all PEV sales, respectively - that are disproportionate with the number of households that reside in these areas. Owners of new or used PEVs in DACs have slightly lower incomes than PEV owners in non-disadvantaged communities. However, as a group they have higher incomes, are higher educated, and fewer are home-renters than the DAC average, indicating that they are not representative of their surrounding community. Encouragingly, charging infrastructure is present in DAC census tracts, suggesting that further PEV adoption could be supported. Additionally, there are higher proportions of used PEVs in DACs than new PEVs, which may indicate potential for adoption of these lower-priced vehicles, however rates of adoption are still low. Despite the considerable benefits that PEVs could offer in DACs, there are still substantial barriers to PEV. Key barriers for policy-makers to continually address are the prohibitive price of the technology, lack of knowledge about or ease of accessing PEV incentives, and lack of access to public or private charging infrastructure located near multi-unit housing.

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute in reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. Integration of PEVs into the electric power system will result in a considerable addition to electricity demand. Due to PEV mobility, this demand has a random distribution in space and time among distribution system nodes. Therefore, short term forecast of PEV charging demand is more challenging than that for conventional loads. Assessment of PEV impacts on the power system is essential to mitigate the impairments from PEV loads. Optimal planning of PEV charging infrastructure will promote the penetration rate of PEVs and minimize the negative impacts of PEVs on the electric power distribution system and transportation road network. Design of charging facilities with integrated distributed energy resources (DER) is considered a solution to alleviate strain on the grid, reduce the integration cost with the distribution network and the charging cost. In this paper, we present a comprehensive literature survey on modelling of PEV charging demand, impact assessment approaches and tools, and charging infrastructure planning. Moreover, an overview on charging facility design with integrated DER is given. Some future research directions are identified.

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.

Is awareness of public charging associated with consumer interest in plug-in electric vehicles?

Policymakers often seek to increase the visibility of plug-in electric vehicle (PEV) chargers in public locations in effort to build familiarity and interest in PEVs. However, it is not clear if the visibility of public charging stations actually has an impact on PEV demand. The purposes of the present study are to (1) assess the current levels of visibility for public PEV charging infrastructure within Canada and (2) identify whether or not a statistically significant relationship exists between consumer awareness of public charging infrastructure and interest in purchasing a PEV. We use data collected from a sample of 1739 Canadian new-vehicle buyers in 2013. About 18% of Canadian respondents have seen at least one public charger, while the proportion is highest in British Columbia (31%). We find a significant bivariate relationship between public charger awareness and PEV interest. However, when controlling for multiple explanatory variables in regression analyses, the relationship is weak or non-existent. While perceived existence of at least one charger exhibits no significant relationship with PEV interest, perceived existence of multiple chargers can have a weak but significant relationship. Thus, public charger awareness is not a strong predictor of PEV interest; other variables are more important, such as the availability of level 1 (110/120-volt) charging at home.

Mesh Network for RFID and Electric Vehicle Monitoring in Smart Charging Infrastructure

With an increased number of plug-in electric vehicles (PEVs) on the roads, PEV charging infrastructure is gaining an ever-more important role in simultaneously meeting the needs of drivers and those of the local distribution grid. However, the current approach to charging is not well suited to scaling with the PEV market. If PEV adoption continues, charging infrastructure will have to overcome its current shortcomings such as unresponsiveness to grid constraints, low degree of autonomy, and high cost, in order to provide a seamless and configurable interface from the vehicle to the power grid. Among the tasks a charging station will have to accomplish will be PEV identification, charging authorization, dynamic monitoring, and charge control. These will have to be done with a minimum of involvement at a maximum of convenience for a user. The system proposed in this work allows charging stations to become more responsive to grid constraints and gain a degree of networked autonomy by automatically identifying and authorizing vehicles, along with monitoring and controlling all charging activities via an RFID mesh network consisting of charging stations and in-vehicle devices. The proposed system uses a ZigBee mesh network of in-vehicle monitoring devices which simultaneously serve as active RFID tags and remote sensors. The system outlined lays the groundwork for intelligent charge-scheduling by providing access to vehicle’s State of Charge (SOC) data as well as vehicle/driver IDs, allowing a custom charging schedule to be generated for a particular driver and PEV. The approach presented would allow PEV charging to be conducted effectively while observing grid constraints and meeting the needs of PEV drivers.

Actual Versus Estimated Utility Factor of a Large Set of Privately Owned Chevrolet Volts

In order to determine the overall fuel economy of a plug-in hybrid electric vehicle (PHEV), the amount of operation in charge depleting (CD) versus charge sustaining modes must be determined. Mode of operation is predominantly dependent on customer usage of the vehicle and is therefore highly variable. The utility factor (UF) concept was developed to quantify the distance a group of vehicles has traveled or may travel in CD mode. SAE J2841 presents a UF calculation method based on data collected from travel surveys of conventional vehicles. UF estimates have been used in a variety of areas, including the calculation of window sticker fuel economy, policy decisions, and vehicle design determination. The EV Project, a plug-in electric vehicle charging infrastructure demonstration being conducted across the United States, provides the opportunity to determine the real-world UF of a large group of privately owned Chevrolet Volt extended range electric vehicles. Using data collected from Volts enrolled in The EV Project, this paper compares the real-world UF of two groups of Chevrolet Volts to estimated UF's based on J2841. The actual observed fleet utility factors (FUF) for the MY2011/2012 and MY2013 Volt groups studied were observed to be 72% and 74%, respectively. Usingmore » the EPA CD ranges, the method prescribed by J2841 estimates a FUF of 65% and 68% for the MY2011/2012 and MY2013 Volt groups, respectively. Volt drivers achieved higher percentages of distance traveled in EV mode for two reasons. First, they had fewer long-distance travel days than drivers in the national travel survey referenced by J2841. Second, they charged more frequently than the J2841 assumption of once per day - drivers of Volts in this study averaged over 1.4 charging events per day. Although actual CD range varied widely as driving conditions varied, the average CD ranges for the two Volt groups studied matched the EPA CD range estimates, so CD range variation did not affect FUF results.« less