Deployment Of Charging Infrastructure Research Articles

From user to operator: Rationalizing the charging infrastructure deployment. A case study of Berlin

The urge to develop greener transportation necessitates a shift from internal combustion engine vehicles to electric vehicles (EVs). Ensuring user acceptance of EVs through adequate charging infrastructure is crucial for this transition. We hypothesize that a user-oriented, time and space-coherent deployment of charging stations can reduce range anxiety and enhance EV adoption. We present a new multi-criteria deployment model and apply it through a MATSim multi-agent simulation to the Berlin region, observing the effects of different user behaviors on the charging infrastructure deployment. Results indicate that slow chargers are more cost-efficient and that residential charging is vital for user acceptance of EVs. The study concludes that a well-planned deployment schedule significantly reduces range anxiety and supports a coherent charging infrastructure helping EV democratization.

Assessing optimized time-of-use pricing for electric vehicle charging in deep vehicle-grid integration system

The expansion of electric vehicles (EVs) and renewable energy (RE) are the two major strategies countries are adopting to achieve energy transition. However, the discrepancy between intermittent RE generation and EV charging load may impose burden on the RE-dominant power grid of the future. For the issue, Time-of-Use (ToU) pricing for EV charging has been widely discussed or adopted as its potential is well acknowledged by previous studies. While ToU mechanism has been primarily based on drivers' price-responsive behavior, this research highlights that EV charging decisions are influenced by various factors beyond price, such as time of a day, charger accessibility and waiting time. Here, we conducted the discrete choice experiment to measure drivers' preferences for EV charging, and developed an EV charging behavior model which incorporated drivers' situational-responsiveness as well as price-responsiveness. Also, the model was used to design optimal ToU tariffs to minimize net-load variation. The results showed that strategic ToU tariffs can shift EV charging load, but achieving desirable load shifts requires a significant price gap. Additionally, combining ToU pricing with strategic deployment of charging infrastructure can effectively shift EV charging load, reducing RE curtailments by 22.14%, and LNG generation and carbon emissions by 10.12%, compared to the Business-as-Usual (BaU) scenario with current tariff rates. Thus, this study highlights the importance of flexible EV charging pricing and the importance of considering charging infrastructure deployment when designing EV ToU pricing.

Identifying factors that influence electric vehicle charging station performance in expanding networks.

Charging infrastructure deployment has taken off in many cities with the rise of the number of electric vehicles on the road. Expansion of infrastructure is a matter of prioritisation of resources to optimise the infrastructure. This paper explores how to measure charging station performance, to address the challenges that policy makers face. These performance indicators are used in a regression model, based upon current utilisation of the network, to predict which charging stations perform best. The results show that a model based on available geographical data and performance metrics of the current network are best combined to predict infrastructure performance. The variability between public charging stations is however big, as frequent user characteristics do determine the performance to a large extent.

Electric vehicle charging infrastructure deployment: A discussion of equity and justice theories and accessibility measurement

The ultimate goal of this discussion paper is to inject more rigorous consideration and measurement of equity and justice issues into research and policymaking around the deployment of electric vehicle charging infrastructure (EVCI). To achieve this, we first examine equity and justice theories in transportation and their alignment with the concept and measurement of accessibility. Equity and justice frameworks abound (e.g., horizontal vs. vertical equity, ladder of justice standards, theories of equity and justice from economic and political philosophy), but all tend to offer different normative perspectives on how to distribute resources among members of society. The accessibility concept is a promising tool for operationalizing these theories, because it explicitly measures opportunities and can be applied to quantify existing disparities or the differential or aggregate outcomes of policies. Next, we interpret these justice/equity frameworks and accessibility measurement in the context of EVCI deployment and literature on disparities in access to charging stations. We discuss which population subgroups to analyze, how to quantify EV charging opportunities and activities, and what to include in travel time and cost calculations. Our paper concludes with additional discussions on community engagement, supply vs. demand, externalities of EVCI, and other issues. A key takeaway for policymakers and researchers is to clarify the ethical perspective being applied in EV analyses and policy, as different equity/justice theories yield diverse responses to how policies should address disparities, including those related to access to EV charging infrastructure.

Variability in costs of electrifying passenger cars in Canada

The high cost of purchasing electric vehicles (EVs) compared to internal combustion engine vehicles (ICEVs) is a major barrier to their widespread adoption. Additionally, the price disparity is not the same for all households. We conducted a total cost of ownership (TCO) analysis to compare the net present value of EV versus ICEV ownership for various household categories across Canada. We observed spatial and behavioral factors, including variations in costs of electricity, temperature, household archetypes and their purchase decisions, and access to charging infrastructure. We found that EVs are more cost-effective than ICEVs for certain daily driving distances, but typical households in Canada generally do not drive enough for lifecycle costs of EVs to be less than ICEVs. The province of Quebec has the most favorable conditions for EV ownership due to high purchase subsidies and low electricity prices. Variability in costs across other provinces and territories is mainly due to differences in rebates, electricity and gasoline prices, and tax rates. Our findings have implications for policymakers and consumers. For consumers comparing ICEVs to EVs based on a fixed budget, which may be consistent with how many households frame their purchase decision, willingness to accept smaller, non-luxury EVs can result in large cost savings. We also find that although temperature variation has a minimal effect on TCO, it does impact the ‘number of charge-ups’—a metric that we introduce to compare how many charging cycles a user may expect over the lifetime of a vehicle. The policy implication of this would be a need to consider regional differences in cold weather patterns when planning charging infrastructure deployment and the extent to which households in shared dwellings may face additional costs. Lastly, our findings strengthen the argument that equitably decarbonizing transportation will also require investment in strategies other than electrifying personal vehicles.

Economic Implications of Charging Infrastructure Deployment for Electric Vehicles in China: An Empirical Analysis

Based on the empirical analysis of the economic implications of charging infrastructure deployment for electric vehicles in China, this study highlights the crucial role of charging infrastructure in promoting the growth of the electric vehicle (EV) market. By controlling for key factors such as car sales and fuel prices, the regression analysis results demonstrate that the deployment of charging infrastructure, represented by the number of charging piles, is positively related to EV sales in China. However, the study also reveals that the coverage area and charging speed of charging piles remain areas for improvement. Respondents to a survey questionnaire considered improving charging speed a priority over increasing the number of charging piles. This study emphasizes the significance of government policies and actions in supporting the development of the EV industry and charging infrastructure deployment in China. It highlights the need for further investment in charging infrastructure development to facilitate the growth of the EV market. Overall, this study provides valuable insights into the economic implications of charging infrastructure deployment for electric vehicles in China and can guide policymakers in designing and implementing effective policies and initiatives to promote the adoption of electric vehicles.

Optimizing electric bus charging infrastructure deployment under two charging modes considering seasonal differences

ABSTRACT The innovations of battery and charging technologies facilitate worldwide bus electrification, which can conserve energy and reduce emissions. The emergence of wireless power transfer technology offers the potential to alleviate bus range anxiety. This paper constructs the layout models of static wireless and centralized charging stations based on the seasonal differences associated with battery capacities and passenger flow, which aims to obtain the bus charging station layout scheme of the lowest cost. Then, the heuristic algorithm is utilized to solve the layout models as the mixed linear programming problem. This study results confirm that the combined layout of static wireless and centralized charging stations is more advantageous than a single model. Finally, the required fixed cost of supporting bus system operation is explored for future capital reserves.

Light commercial electric vehicles with hydrogen fuel-cell range extender: Refueling strategy evaluation

Electrified light commercial vehicles have gained increasing interest as an energy efficient means of transportation. At the same time, they are characterized by limited traveling range, necessitating careful design of batteries recharging planning and respective charging infrastructure deployment. Early available hydrogen fuel cell vehicles have the potential to overcome the range anxiety limitation, they require though an extended H2-refueling network. The scope of this work is to demonstrate a model-based evaluation of the H2-refueling strategy for a battery electric light commercial vehicle, equipped with a hydrogen fuel-cell based range extender. For this purpose, advanced vehicle modeling was combined with statistical analysis to thoroughly evaluate the studied vehicle's energy consumption, under a broad range of operating conditions and eventually define an optimal refueling strategy. The attempted analysis not only allowed for defining the refueling strategy, but also enabled to optimize the H2-tank size, which tends to highlight the interest of the followed approach.

Charging patterns analysis and multiscale infrastructure deployment: based on the real trajectories and battery data of the plug-in electric vehicles in Shanghai

The rapid development of transportation electrification aims to achieve a fuel-free and zero-carbon emission world. Electric vehicles (EVs) are booming worldwide, especially in China. Even if EVs' cost decreases and performances make significant progress, the convenience and deployment problem of charging infrastructure is still the key restriction for large-scale adoption. Therefore, this study proposes a real-EV-data-based model with the aim of optimal charging infrastructure deployment oriented to charging convenience and charging demand. Traveling and charging behaviors based on the real EV data from Shanghai, where the EV penetration rate is highest in China, are revealed for the first time to reflect the interactions between drivers and infrastructures. Especially, the charging behaviors in Shanghai are beneficial for the battery life of EVs. The deployment and related optimization models are then established to meet the charging demand and promote quantified convenience. Moreover, infrastructure deployment considering both the charging type and the scale can be programmed. The proposed capacity-removal heuristic algorithm have advantages in robustness for deployment solving. The deployment results promote the charging convenience by 119.4%, and the expectation of the average distance to the nearest charger is decreased by 32.0%. For the slow charger, fast charger and mixed charger deployment schemes, the charging experience of drivers can all be improved by over 10% and the return analysis based on the investment cost and charging service fee shows a good rate of return with 8.38% and 18.5%. Besides, scenario analysis shows that charging station deployment with higher-power and larger-capacity chargers is the future trend.

Reliable dynamic wireless charging infrastructure deployment problem for public transport services

Dynamic wireless power transfer (DWPT) technology offers the promise of eliminating the limited driving range of electric buses (EBs), providing greater convenience and safety benefits through the ability to charge EBs in motion. It may be possible to effectively circumvent the drawbacks of EBs such that it becomes mainstream in the future EB market. In this paper, we study the robust DWPT deployment problem for an EB system, aiming to determine the DWPT facility deployment, battery size, and en-route charging schedule simultaneously. We first formulate the deterministic problem as a novel mixed-integer linear programming (MILP) model. Next, we consider the energy consumption uncertainty and travel time uncertainty to develop a two-stage robust model. We further analyze the structural properties of our proposed model, and implement the column-and-constraint generation (C&CG) method to solve it optimally. Finally, a series of experiments based on a test bus network and the National University of Singapore internal bus network are carried out to verify the efficiency and effectiveness of our models and algorithms. Some managerial insights are also provided that public transit (PT) operators could use to enhance the efficiency of the EB system in the future.

Multi-Criteria Approach for Identification and Ranking of Key Interventions for Seamless Adoption of Electric Vehicle Charging Infrastructure

Development of electric vehicle charging infrastructure needs a multi-sectoral approach with due consideration given to different associated stakeholders in the EV ecosystem. With the global transition towards electrification of the transportation sector, several countries have set ambitious goals of Electric Vehicle (EV) adoption. However, deployment of adequate EV charging infrastructure, which is one of the most critical factors for seamless adoption of EVs, is limited by various challenges, including technical, policy and regulatory issues. In order to mitigate the challenges, this paper presents a combined ranking of the key interventions. Critical analysis of all the identified interventions has been undertaken and the learnings have been utilized to frame a multi-criteria decision making (MCDM) process to identify the priority of roll-out of the interventions. The unified ranking of technical, and policy and regulatory interventions would enable rapid and widespread deployment of EV charging infrastructure.

Optimal and efficient planning of charging stations for electric vehicles in urban areas: formulation, complexity and solutions

The deployment of charging infrastructure for electric vehicles is crucial in extending their range. Many studies on the charging infrastructure deployment adopt the Mixed Integer Linear Programming (MILP) method to optimize various objectives. However, as the number of integer variables and constraints increases, the computational time and memory requirements of MILP models increase exponentially. This makes it impractical to use MILP models to solve large-scale optimization problems. In this paper, we formulate and prove that the Planning of Electric Vehicle Charging Stations (PEVCS) is an NP-complete combinatorial optimization problem. We also prove that PEVCS has a significant effect, that is, submodularity. Additionally, we propose two efficient methods that use submodularity to improve the conventional methodology for PEVCS. Furthermore, we provide a provable guarantee for the performance of our proposed methods. Our experimental results demonstrate the efficiency and effectiveness of these methods on small-scale and large-scale datasets, especially in realistic large-scale situations.

Investigating the deployment of initial public charging infrastructure: Planning-based VS market-based approaches

An efficient planning of public charging infrastructure is crucial for the uptake of electric vehicles, which requires not only the cost-effectiveness of charging infrastructure construction, but also the effects in accelerating the process of EV promotion. However, the existing studies on the layout of public charging facilities have rarely taken both of these aspects into account. This study aimed to enrich the available literature by evaluating planning-based versus market-based approaches to installing charging piles and optimizing the scheme for initially deploying public charging infrastructure. An agent-based model that integrated the individual heterogeneity of manufacturers and consumers was constructed, with parameter settings that were defined based on the Chinese market. The impact of the initial deployment of public charging piles was compared in terms of planning-based and market-based strategies. The main results indicated a significantly positive effect of public charging piles construction. The market share of passenger EVs at 2040 could increase from 23.58% to 39.38% at most compared to the baseline scenario. Among the two approaches to deploying charging piles, the planning-based strategy proved superior in cultivating a niche market; however, the market-based approach was more cost-efficient from the perspective of long-term market cultivation, allowing savings of up to 14.2% of the investment costs. The study findings provide new evidence and policy implications for the long-term promotion of electric vehicles.

EV Fast Charging Station Planning Considering Competition Based on Stochastic Dynamic Equilibrium

The rapid growth of electric vehicles (EVs) and the deployment of fast charging infrastructures bring considerable impacts on the planning and operation of power systems. Integrating photovoltaic (PV) and energy storage system (ESS) with fast charging station (FCS) can alleviate the negative impacts and bring benefits to both power system operation and charging service provider. In this paper, an integrated procedure is proposed for the charging service provider to optimally plan fast charging stations integrated with PV and ESS in urban area, while taking the influence of its competitors, EV users' decision-making psychology, the uncertainties of charging demand and PV power output into consideration. Firstly, a charging demand estimation method based on average travel speed is presented. Secondly, a stochastic dynamic user choice equilibrium (SDUCE) model and its solution algorithm based on method of successive averages (MSA) are proposed. The model considers the key factors influencing the charging decision of EV users as well as the uncertainty of their perceptions of utility values. In addition, the time-varying queuing time and the temporal coupling among EVs arriving at a charging station in different time periods are also considered. Thirdly, based on the equilibrium model, a multi-scenario benefit maximization model is built from the perspective of charging service provider, and solved by genetic algorithm. Finally, a distributionally robust optimization (DRO) planning model based on the Wasserstein-metric is formulated to determine the capacities of PV and ESS. The proposed planning method is tested using real traffic data of Shenzhen City. Simulation results verify the effectiveness of the proposed method.

An agent-based simulation study for escaping the “chicken-egg” dilemma between electric vehicle penetration and charging infrastructure deployment

Although charging infrastructure is a prerequisite for widespread electric vehicle (EV) penetration, its deployment is prone to a "chicken-egg" dilemma in the early stages of electrification. Previous studies mainly applied game theoretical approaches to identify the optimal strategy for escaping the dilemma, with minimal attention paid to the complex interactions between EV adoption and charging infrastructure deployment, which is the intrinsic cause of the “chicken-egg” dilemma. Hence, this study establishes an agent-based model to capture the dynamic interactions among EV adoption decisions, EV charging activities, and charging infrastructure investment and operation. Empirical data from Beijing is further applied to calibrate and validate the model before conducting simulation experiments to examine the effectiveness of financial subsidies and environmental factors for resolving the dilemma. The findings suggest that either purchase subsidies for EV adopters or operation subsidies and construction subsidies for charging point operators can help escaping the dilemma only if their subsidy level is within a reasonable range. Otherwise, subsidy strategies may lead to a heavy fiscal burden or an overallocation of charging resources. A substantial change in external factors such as EV sales prices, gasoline prices, and EV driving range can also initiate a virtuous circle between EVs and charging infrastructure, indicating that policy interventions towards these factors may shed light on tackling the dilemma.

Financial Analysis of Charging station (FACt): A tool for easy financial evaluation of public charging infrastructure deployment in India

The MS-Excel-based tool ‘Financial Analysis of Charging station (FACt)’ (the “Tool”) has been developed to help build an understanding of the business case for rolling out public charging infrastructure for electric vehicles (EVs) in the Indian context. It is a flexible, transparent, and user-friendly solution to the problem of conducting a quick preliminary assessment of the financial justification for setting up a public charging station. The wide-ranging outputs from the Tool provide a well-rounded view of the financials of a public charging infrastructure project, including the project cost composition and the possible impact of various factors on the return. Currently, there is no publicly available open access application-based solution, made for the Indian market, that achieves the stated goals of FACt.

Introduction to the special issue on mobility, climate change, and economic inequality

Introduction to the special issue on mobility, climate change, and economic inequality

Impact of Different Charging Methods on Electric Bus Battery Size and Grid Demand

This paper presents an agent-based model (ABM) that estimates battery size and electricity grid demand for bus charging infrastructure. The ABM considers four charging methods: overnight charging (ONC), end-line charging (ELC), occasionally fast charging (OFC), and wireless charging (WLC). The model accurately captures the energy consumption and charge load of battery electric buses (BEBs) by incorporating GPS coordinates, average speed, and temperature profiles. A case study is conducted on a bus route in Cardiff, UK, to showcase the functionality of the ABM. The results of simulations demonstrate significant reductions in electric bus battery sizes for different charging methods. For example, end-line charging reduces battery size by 235 kWh per bus compared to overnight charging. Occasionally fast charging and wireless charging achieve even lower capacities of 86 kWh and 69 kWh, respectively, however, these charging methods lead to higher and more fluctuating grid loads, resulting in a poor load factor. In summary, this ABM provides a practical tool for infrastructure planners. The case study illustrates its effectiveness in optimizing battery size and evaluating grid demand for BEB charging infrastructure. The findings provide critical directions for both bus operator shifting towards electrification of their fleets and city planners responsible for the deployment of related charging infrastructure.

Effects of multiple incentives on electric vehicle charging infrastructure deployment in China: An evolutionary analysis in complex network

With the current booming electric vehicle industry, an insufficient supply of electric vehicle charging infrastructure (EVCI) has become a major barrier to further penetration of electric vehicles. Chinese provincial and municipal governments have introduced multiple incentives to address EVCI investment obstacles and promote EVCI deployment. However, the effectiveness and differential effects of these policies are unclear. In this context, this paper constructs an evolutionary game model in complex networks for EVCI deployment that captures the dynamic interactions as well as internal and external influences in infrastructure networks. Leveraging this model, this paper simulates the effects of multiple incentive policies, including investment subsidies, construction subsidies, operation subsidies, user charging subsidies, and policy mixes on EVCI deployment. The results reveal that investment subsidies are quite effective but have more pronounced marginal diminishing effects. Both construction and operation subsidies have had a steady and positive impact. Implementing the two incentives as a policy mix can exert complementary effects, but a careful cost-benefit analysis is needed to prevent incentive saturation. The effect of charging subsidies is not as significant as other incentives. Finally, incentive policies should be implemented for a long time and adjusted for different markets and stages to optimize effectiveness.

Are consumers in China’s major cities happy with charging infrastructure for electric vehicles?

Extensive charging infrastructure deployment is the crucial link to the large-scale development of electric vehicles. As consumers are the ultimate users of the charging infrastructure, comprehensive and in-depth insight into consumer preferences and choosing tendencies for charging infrastructure is of great practical significance. This paper utilizes the micro-survey data conducted in the four most developed first-tier cities in China to quantitatively explore the factors influencing consumer satisfaction with charging infrastructure. The empirical results indicate that consumers who are young, are insensitive to charging price, have long driving distance, prefer to charge at night, tolerate charging waiting in line, and accept long charging time are more inclined to be satisfied with the charging infrastructure. Then the consumer preferences for public or private charging infrastructure are further explored. The results show that consumers with high-income level, tolerance of waiting in line and long driving distance tend to choose public charging infrastructure. But consumers who prefer charging at night and are sensitive to charging price are more inclined to be satisfied with the private charging infrastructure. The heterogeneity of individual performance was demonstrated by marginal effect analysis. Finally, the discussion on the installation willingness and charging price preference of consumers is conducted.