Charging Infrastructure Requirements Research Articles

Strategies and models for optimal EV charging station site selection

Abstract In numerous countries worldwide, adopting electric vehicles (EVs) is gaining momentum as a proactive measure to mitigate the detrimental environmental impacts of traditional fuel-powered automobiles. This shift drives exponential growth in the adoption of EVs, prompting the need for comprehensive analysis to optimize charging infrastructure requirements. Developing reliable and sustainable charging infrastructure depends on practical and strategic site selection of EV charging stations. The main challenge is finding a charging solution that maximizes efficiency within limited financial resources. The present review critically assesses methodologies for selecting optimal EV charging station sites, considering technical, environmental, social, and economic factors. Special emphasis is given to social factors such as population density and service accessibility, as well as technical factors like vehicle battery life, charging time, and grid capacity. Environmental impact and feasibility are also vital criteria under evaluation. Through a synthesis of insights from various studies, this review provides a comprehensive overview of the existing models used in EV charging infrastructure site selection. The findings contribute valuable insights for decision-makers, city planners, and other stakeholders in creating sustainable EV charging networks amidst the dynamic landscape of electric mobility.

Locating charging infrastructure for freight transport using multiday travel data

Vehicle electrification has shown the potential to reduce environmental impacts and greenhouse gas emissions from the transport sector. As electric vehicles (EVs) become increasingly prominent, the efficient placement of charging infrastructure poses a complex challenge that demands careful consideration. This paper delves into the investigation of how travel and parking patterns, derived from empirical data on freight vehicles, influence the optimal distribution of charging infrastructure across the freight network. This paper presents a node-based approach to optimize the allocation of charging infrastructure tailored explicitly for freight transport. The study identifies optimal locations for operator-owned charging infrastructure by leveraging GPS-based data collected from a fleet of freight vehicles operating in the greater Gothenburg metropolitan area. This research aims to enhance our understanding of the charging infrastructure requirements inherent in the freight transport system and provide decision support to logistics companies contemplating the shift from conventional fossil fuel vehicles to electric freight vehicles. The proposed model holds the potential for seamless adaptation to diverse freight transport systems, offering valuable insights to expedite the transition toward fossil-free freight transport on a broader scale.

How the presence of public charging infrastructure can impact the adoption rate of electric vehicles in the UK

In the current work, the presence of public charging facilities and their impact on the adoption rate of Electric Vehicle (EV) are studied across 363 Local Authorities (LA) districts in the UK. This analysis is conducted utilizing the data from the UK government on the location of public electric vehicle chargepoints in various LAs as well as the EV ownership rate between years 2011 and 2021. Various rural, urban and extra urban (e.g. London) regions are analyzed to assess whether EV ownership has been impacted as the charging infrastructure is rolled out in these regions. The quarterly data on the number of licensed Ultra Low Emission Vehicles (ULEV) in each UK local authority is used to track EV ownership levels. The results indicate that the publicly available charging points stimulate the diffusion of EV adoption; however, this varies by region and saturation. The reverse effect of EV ownership on charging infrastructure rollout by Local Authorities cannot be entirely rejected; however, the positive stimulus of public infrastructure on adoption of EV is established. The findings can provide the UK policy makers with insight into the public charging infrastructure requirements and benefits in short to medium term to meet the committed emission reduction targets by 2035 and 2050.

Geographical Modeling of Charging Infrastructure Requirements for Heavy-Duty Electric Autonomous Truck Operations

This study presents an analysis of the charging infrastructure requirements for autonomous electric trucks (AETs) in a specified geographical region, focusing on the state of Texas as a case study. A discrete-time, agent-based model is used to simulate the AET fleet and consider various model parameters such as trip distance/duration, the number of trips, and charging speeds. The framework incorporates unique properties of the Texas road network to assess the sensitivity of charging infrastructure needs. By synergizing electrification and automation, AETs offer benefits such as reduced carbon emissions, enhanced transportation safety, decreased congestion, and improved operational costs for fleets. By simulating daily trips and energy consumption patterns, an analysis of the charging infrastructure needs for cities along the Texas highway triangle formed by I-35, I-45 and I-10 revealed that the total charging energy and average charging power for these major cities ranges between 443~533 MWh/day and 18.5~22 MW, with costs in the range of USD $7.74~$15.93 million for each city, depending on charging infrastructure design and exclusive of any enhancements to the distribution grid infrastructure needed to support the charging infrastructure. This data-driven approach may be replicated for other regions by adapting the simulation parameters to allow policymakers and stakeholders to assess the charging infrastructure requirements and related investments needed to support the transition to electric and autonomous heavy-duty trucking.

Battery only electric traction for freight trains - A technical and operational assessment

The rail transport industry has been required to engage with the world effort to reduce the climate effects, as well as the reduction of criteria pollutant and noise emissions, which strongly affects the populations nearby the rail infrastructure. Electric powertrains have been widely used by railways, with its inherent efficiency and cleanliness. However, the required infrastructure has restricted its use to medium to large density traffic corridors, given the large infrastructure and maintenance costs involved. Light to medium loaded rail stretches and large freight rail corridors, running in rural areas, have adopted the diesel-electric powertrain approach, that relies on an onboard diesel generator, with the inherent environmental negative effects, especially to the rail nearby communities. In this context, alternative traction technologies, such as Hybrid-Electric Rail, Hydrogen Fuel Cell Rail and Battery Only Electric Rail, have been considered by the rail industry as potential alternatives. The BOER route, due to the recent battery technology breakthroughs (associated with performance improvements and reduced costs) is set as a promising alternative and has a potential do be used as a decarbonization strategy for both passenger and freight rail systems at low to medium density traffic lines. Nevertheless, the BOER technology sets great challenges, given the high energy and power required by rail vehicles, as well as the harsh rail operational environment, with huge effects on the performance, charging infrastructure requirements, fleet availability, costs, as well as the environmental sustainability associated with the mining and disposal of materials required by the battery chemistries. This paper presents, based on the state of the art available technical literature, an unbiased review of the BOER technology and an assessment of its potential for the freight rail industry, highlighting the associated technical, operational and environmental challenges.

Optimal Planning of Single-Port and Multi-Port Charging Stations for Electric Vehicles in Medium Voltage Distribution Networks

This paper describes a method to cost-optimally locate and size chargers for electric vehicles (EVs) in power distribution grids as a function of the driving demand while respecting the grid’s constraints. The problem accounts for the notion of single-port chargers (SPCs), where a charger can interface one EV maximum, and multi-port chargers (MPCs), where the same charger can interface multiple EVs, leading to possible cost savings and improved arbitrage of the charging power. The formulation is capable of accounting for and modeling the flexibility of EV owners’ in plugging and unplugging their EVs from and to chargers at different hours of the day, a factor that can impact chargers’ utilization and thus charging infrastructure requirements. Simulation results from a synthetic case study show that implementing MPCs is cost-beneficial over both SPCs and owners’ flexibility for EVs with small batteries (16 kWh). However, this cost benefit vanishes when considering EVs with larger batteries (60 kWh).

Electric Buses in Malaysia: Policies, Innovations, Technologies and Life Cycle Evaluations

A large-scale adoption of electric buses (EBs) is a promising solution to mitigate greenhouse gas emissions from the transportation sector. In the upcoming decades, the development of EB technologies will be initiated worldwide, including in Malaysia. Government policies to support EB deployments have been widely established. Therefore, Malaysia’s National Automotive Policy has stated a roadmap of policies to promote a national groundwork accordingly. Following the elaboration of Malaysia’s goals for EBs deployments by 2014 and 2020, there are crucial associated topics for EBs implementation, including EB innovations and technologies adoption. This study presents a deep discussion about the groundwork of EB innovations that have been initiated in Malaysia to meet the roadmap targets. This paper also comprehensively reviews the technical specifications of EB innovation technologies, including Electric Bus Innovation Malaysia, Malaysia Automotive Institute, and Go Auto prototypes. In addition, this study outlines the EB technologies that have been launched in three states in Malaysia, known as Putrajaya, Melaka, and Sarawak. Furthermore, a generic framework for life cycle assessments of EB is presented, focusing on the economic and environmental impacts. This framework provides the necessary groundwork for further studies on charging infrastructure requirements.

Integrated U.S. nationwide corridor charging infrastructure planning for mass electrification of inter-city trips

This study introduces an integrated modeling framework to evaluate long-term national corridor charging infrastructure requirements in the United States to support the growing inter-city charging demand with the rapid growth in the battery electric vehicle (BEV) market. The core model is an optimization model that considers spatial and temporal dimensions and models heterogeneous behaviors between travelers. The model also introduces the travelers’ inconvenience cost function by linking travelers’ acceptance of the charging infrastructure with exogenous technology and social factors. The inconvenience cost function simulates mode choice between BEVs and alternative modes by heterogenous travelers. We applied the framework to assess the inter-regional charging infrastructure requirements for the entire U.S. mainland interstate highway network. We evaluated impacts on the infrastructure design and its public acceptance with changes in policy, technology, and demographic characteristics, and we also quantified the importance of modeling full-scale inter-regional charging infrastructure requirements compared to the conventional regional level analyses.

Exploring public charging infrastructure requirements for short-haul electric trucks

Vehicle electrification has the potential to revolutionize transport systems globally by enabling a pathway toward lower-cost, zero-emission transport options. Heavy vehicles are poised to potentially be one of the greatest beneficiaries due to their high operating costs. Here a facility location problem approach is adopted to analyze a sample of deidentified truck trip data, providing initial insights into charging network configurations that could support a short-haul electric truck fleet using the case study of South East Queensland. The charging network scenarios include up to 10 optimally-located public sites. The results suggest that a relatively modest public charging network should be sufficient to support the majority of short-haul electric trucks operating in similar regions internationally, in line with other studies that have found that the majority of short-haul electric truck charging is likely to be carried out at depots. Additionally, in outlining limitations of this study, we highlight the need for greater cooperation between researchers, policy-makers and industry to secure access to high-resolution truck trip data to more accurately plan regional charging networks, and freight decarbonization more broadly.

Determining the optimal electric vehicle DC-charging infrastructure for Western Australia

Take-up rates of electric vehicles (EV) are increasing and are predicted to accelerate rapidly. Public EV charging networks will be required to support future EV fleets. If unplanned, public charging networks are highly likely to be suboptimal. Planners need to understand and plan for future EV charging infrastructure requirements, particularly public DC fast charging networks, as both the upfront investment costs and the consequences of misallocation are high. However, the task of determining the optimal locations and allocations (types and numbers) of public EV charging infrastructure is complicated as it requires knowledge of many variables. These include EV driver behaviors, driving patterns, predicting evolutionary changes in EV and EV charging technologies, future EV take-up rates, and what investment may or may not occur in the absence of government funding support.

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.

Integrated transit route network design and infrastructure planning for on-line electric vehicles

The emergence of electromobility along with recent developments in wireless power transfer (WPT) technology offer potentials to improve the carbon footprint of bus transport, while offering quality services. Indeed, the deployment of fast charging stations and dynamic charging roadway segments (lanes) can ensure fast energy transmission to electricity-powered buses, mitigating existing energy-related concerns and limitations. Existing models for public transport network design cannot adequately capture the dependence between electric vehicle charging infrastructure requirements and route operational characteristics. In this context, this paper investigates the combined Transit Route Network Design and Charging Infrastructure Location Problem and proposes a bi-level formulation to handle both planning stages. At the upper level, candidate route sets are generated and evaluated, while at the lower-level wireless charging infrastructures are optimally deployed. A multi-objective Particle Swarm Optimization (MO-PSO) algorithm embedded with an integer programming solver is employed to handle the complexity of the problem and the conflicting design objectives related to passengers and operators. The resulting model is applied to an established benchmark network to assess the tradeoffs arising between user-oriented and operator-oriented solutions and highlight the complex decision process associated with the deployment of electric public transport networks.

Vehicle-to-vehicle wireless power transfer: Paving the way toward an electrified transportation system

To date, three major hurdles have hindered widespread adoption of electric vehicles (EVs): the high cost of batteries, insufficient public charging infrastructure, and the limited driving range of EVs. This study overcomes these three hurdles by introducing a new concept of vehicle-to-vehicle wireless power transfer (V2V WPT) between EVs to facilitate frequent, real-time, and on-demand charging. V2V WPT is enabled by the connected and automated vehicle technology and the sharing economy, and facilitates transfer of power between peer EVs. We formulate the problem of routing, scheduling, and matching vehicles in a V2V WPT platform on a energy-time expanded network, and devise a dynamic programming solution methodology to find the solution efficiently. We quantify the impact of adopting this technology in terms of system-wide energy savings, charging infrastructure requirements, and travel times, and investigate the possibility of reducing battery capacity in EVs as a result of this technology.

Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks

An increasing number of cities are transitioning from fossil fuel-powered buses for public transport to battery electric buses. Evaluating the energy demand of buses has become an important prerequisite for the planning and deployment of large electric bus fleets and the required charging infrastructure. A number of state-of-the-art approaches to determining the energy requirements of electric buses use individual specific energy demand values or rely on standard driving cycles, though these do not consider local bus route characteristics. Others require high-resolution measurements of the vehicles’ driving profiles, which is impractical for large bus fleets. This paper presents a longitudinal dynamics model to calculate the energy demand for electric buses. The model is designed to be easily applied to large bus networks using real data sources that are commonly available to bus transit operators. This data can be derived from low-resolution data collected from day-to-day operations, where only the arrival and departure time of the buses at each bus stop are available. This approach offers a practical alternative to state-of-the-art methods and requires no high-resolution velocity profiles, which are difficult to obtain, while still taking into account the details of the operational characteristics of the transportation network under consideration. The application of the model is demonstrated in a case study to electrify the complete public bus network in Singapore. The results showed that the heterogeneity of driving conditions observed in a large network leads to a high variance in energy requirements between different bus lines and at different times of day. This confirms the need to take the characteristics of each individual bus route into account. In the case-study, a fully electric public bus fleet would require about 1.4 GWh per day for revenue service, which is about one per cent of Singapore’s daily electricity demand. Another finding is that 50% of the bus lines require less than 40 kWh per terminus-to-terminus journey, which indicates a good potential for fast opportunity charging during layover time. The results of the model should serve as the basis for further studies into battery sizes, charging strategies and charging infrastructure requirements.

Cost, Energy, and Environmental Impact of Automated Electric Taxi Fleets in Manhattan.

Shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions. We estimate that costs will be lowest with a battery range of 50-90 mi, with either 66 chargers per square mile, rated at 11 kW or 44 chargers per square mile, rated at 22 kW. We estimate that the cost of service provided by such an SAEV fleet will be $0.29-$0.61 per revenue mile, an order of magnitude lower than the cost of service of present-day Manhattan taxis and $0.05-$0.08/mi lower than that of an automated fleet composed of any currently available hybrid or internal combustion engine vehicle (ICEV). We estimate that such an SAEV fleet drawing power from the current NYC power grid would reduce GHG emissions by 73% and energy consumption by 58% compared to an automated fleet of ICEVs.

Locating charging infrastructure for electric buses in Stockholm

Charging infrastructure requirements are being largely debated in the context of urban energy planning for transport electrification. As electric vehicles are gaining momentum, the issue of locating and securing the availability, efficiency and effectiveness of charging infrastructure becomes a complex question that needs to be addressed. This paper presents the structure and application of a model developed for optimizing the distribution of charging infrastructure for electric buses in the urban context, and tests the model for the bus network of Stockholm. The major public bus transport hubs connecting to the train and subway system show the highest concentration of locations chosen by the model for charging station installation. The costs estimated are within an expected range when comparing to the annual bus public transport costs in Stockholm. The model could be adapted for various urban contexts to promptly assist in the transition to fossil-free bus transport. The total costs for the operation of a partially electrified bus system in both optimization cases considered (cost and energy) differ only marginally from the costs for a 100% biodiesel system. This indicates that lower fuel costs for electric buses can balance the high investment costs incurred in building charging infrastructure, while achieving a reduction of up to 51% in emissions and up to 34% in energy use in the bus fleet.

Simulation-assisted exploration of charging infrastructure requirements for electric vehicles in urban environments

High population densities in today's cities are leading to increasing congestion and air pollution. Sustainable cities of the future will require a large scale transition to electro-mobility. The development of electric vehicle charging infrastructure is necessary to enable this transition. Existing methods for determining charging infrastructure take an optimization approach that ignores existing traffic demands and infrastructure. Moreover, the dynamics of vehicle movement like stop-and-go traffic, congestion and the effect of traffic lights are not considered in determining energy consumption. In this paper, we propose a novel nanoscopic city-scale traffic simulation based method for determining charging infrastructure locations; subsequently, we demonstrate its usefulness in spatio-temporal planning through a case-study of Singapore. Through this method, existing traffic and road network data and the dynamics of individual vehicle movement can be taken into consideration in planning.

Evaluation of charging infrastructure requirements and operating costs for plug-in electric vehicles

Plug-in electric vehicles (PEVs), including plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), have the potential to improve the energy and environmental landscape of personal transportation, but face a hurdle of access to charging infrastructure. Additionally, the types, locations, and quantities of electric vehicle supply equipment (EVSE) that will be required are not well established. This study investigates the charging infrastructure requirements from the perspective of PEV operating cost and BEV feasibility. California was selected as the research region and PEV parameters were selected based on the early deployed vehicles available in the emerging commercial market. To minimize operating cost, an optimal charging strategy based on 24 h travel patterns is proposed. Results indicate that charging time strategy is the most important factor in reducing PEV operating cost while greater numbers of charging locations provide diminishing benefits for PHEVs. Higher charging power capability, combined with an acceptable charging time strategy offer only slight benefits for PHEVs, but charging power is an important factor in increasing BEV functionality and decreasing public charging requirements. The approximation of the electric vehicle supply equipment (EVSE) needed at different types of locations (e.g., home, work place, shopping) is proposed based on an optimal charging strategy.

Assessing the potential impact of workplace charging for a group of commuters

A PHEV demonstration project gave 80 consumers within the Northern California counties of Sacramento, Yolo and San Joaquin the opportunity to drive a PHEV-conversion for at least one month each in lieu of one of their existing vehicles. Households decided for themselves when, where, and how much to charge the PHEV, if at all. Out of the 80 households, 25 were characterized as plausible future PHEV owners who also commuted to a workplace. Each of the PHEV-conversions was equipped with loggers which recorded all travel and charging data. To estimate the potential implications of added workplace charging infrastructure across a group of commuting households, each household’s vehicle usage is simulated with six hypothetical PHEVs, the design characteristics of which are outlined in Table 2 of this paper. Combining each household’s usage data with the hypothetical designs allows their PHEV-conversion experience to be generalized beyond the specific PHEV-conversion to plausible future PHEV designs. Since most households did not have access to charging infrastructure at work, charging events are simulated for each household every time they arrive at their workplace. Comparison between the recorded behavior and the simulated workplace charging case allows for an exploration of the potential impacts of workplace charging on the individual and fleet utility factor, workplace charging infrastructure requirements, and grid load. Workplace charging increases the total fleet average utility factor, however, the benefit varies considerably by household and vehicle charge depleting range. Based on simulation results, up to 75% of commuters would be able to use 1.44 kW charging without experiencing a decrease in electric miles driven, and workplace charging creates a new peak vehicle charging load on the grid in the morning, in the range of 0.8 to 1.4 kW per PHEV.

Valuation of plug-in vehicle life-cycle air emissions and oil displacement benefits

We assess the economic value of life-cycle air emissions and oil consumption from conventional vehicles, hybrid-electric vehicles (HEVs), plug-in hybrid-electric vehicles (PHEVs), and battery electric vehicles in the US. We find that plug-in vehicles may reduce or increase externality costs relative to grid-independent HEVs, depending largely on greenhouse gas and SO(2) emissions produced during vehicle charging and battery manufacturing. However, even if future marginal damages from emissions of battery and electricity production drop dramatically, the damage reduction potential of plug-in vehicles remains small compared to ownership cost. As such, to offer a socially efficient approach to emissions and oil consumption reduction, lifetime cost of plug-in vehicles must be competitive with HEVs. Current subsidies intended to encourage sales of plug-in vehicles with large capacity battery packs exceed our externality estimates considerably, and taxes that optimally correct for externality damages would not close the gap in ownership cost. In contrast, HEVs and PHEVs with small battery packs reduce externality damages at low (or no) additional cost over their lifetime. Although large battery packs allow vehicles to travel longer distances using electricity instead of gasoline, large packs are more expensive, heavier, and more emissions intensive to produce, with lower utilization factors, greater charging infrastructure requirements, and life-cycle implications that are more sensitive to uncertain, time-sensitive, and location-specific factors. To reduce air emission and oil dependency impacts from passenger vehicles, strategies to promote adoption of HEVs and PHEVs with small battery packs offer more social benefits per dollar spent.