Charging Infrastructure Planning Research Articles

Exploring Cost Effective Fleet Electrification Possibilities for Public Transit Services in Kutch Region

The extensive use of fossil fuels in transit buses has led to both environmental problems and fuel scarcity. Renewable energy has emerged as a potential solution to mitigate these issues. Many countries worldwide have already adopted renewable energy to address these challenges. Similarly, the Indian government has initiated e-vehicle adoption in various sectors through subsidies to reduce dependence on conventional fuels. This research investigates two key areas: Fuel consumption dependency and air quality impact assessment for diesel-operated public buses (GSRTC) and Exploring fleet electrification possibilities. The recent significant rise in fossil fuel costs has exposed the inherent conflicts between achieving net-zero goals, ensuring energy security, and maintaining affordability. This research employs a three-stage energy modeling methodology: Route energy planning, charging infrastructure planning and total cost of ownership (TCO) analysis. This study explores the potential for fleet electrification at the Mundra depot, aiming to replace existing diesel-powered internal combustion engines (ICEs) with environmentally friendly electric vehicles (EVs). A total of 18 buses from the Mundra depot, covering 22routes with 103 trips, were surveyed. An optimal route requiring minimal energy was prioritized. Based on this and a minimum state-of-charge requirement of 25%, five buses were chosen for further study. An analysis of annual capital costs for both ICE diesel and electric buses was conducted, considering bus capitalization, battery capitalization, charging infrastructure, and annual operation costs. The findings revealed that the TCO for an EV bus is significantly lower than that of a diesel bus (49.64 Rs/km vs. 72.80 Rs/km).

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

Prioritizing the development of public transport is an effective way to improve the sustainability of the transport system. In recent years, bus passenger flow has been declining in many cities. How to reform the operating mode of the public transportation system is an important issue that needs to be solved. An autonomous modular bus (AMB) is capable of physical coupling and uncoupling to flexibly adjust vehicle capacity as well as provide high-quality service under unbalanced passenger demand conditions. To promote AMB adoption and reduce the operating cost of the bus route, this paper presents a joint optimization method to simultaneously determine the AMB dispatching plan, charging plan, and charging infrastructure configuration scheme. Then, a mixed-integer programming model is formulated to minimize the operating costs of the bus route. A hybrid intelligent algorithm combining enumeration, cloning algorithm, and particle swarm optimization algorithm is designed to resolve the formulated model. Subsequently, an actual bus route is taken as an example to validate the proposed method. Results indicate that the developed method in this paper can reduce the operating costs and operational energy consumption of the route compared with the real route operating plan. Specifically, the reduction ratio of the former is 23.85%, and the reduction ratio of the latter is 5.92%. The results of this study validate the feasibility and advantages of autonomous modular transit service, contributing positively to the sustainable development of the urban public transportation system.

Power consumption prediction for electric vehicle charging stations and forecasting income

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling.

An Analysis of Electric Vehicle Charging Intentions in Japan

This study focuses on charging-related decisions for fast charging at highway service and parking areas, slow charging at home, fast charging at commercial facilities, and fast and slow charging at workplaces. This research contributes to the existing literature by estimating the charging behavior variables, as well as understanding the role of explanatory variables in influencing charging-related decisions. Responses from the stated preference (SP) survey in Japan in 2021 were analyzed with a mixed logit model (MXL). The results showed that, (1) when the battery level is 75% or higher, users of battery electric vehicles (BEVs) are not keen to charge their vehicles, but when the next trip is anticipated to be 50 or more kilometers, they choose to charge their vehicles; (2) individuals are not willing to tolerate any waiting time for their vehicles to be charged at each location; and (3) the recurrence of charging at the target location affects the charging decision of BEV users. We found significant relationships between socioeconomic characteristics and charging decisions. Furthermore, we examined the practical applications of the empirical findings in this study for policymaking and charging infrastructure planning.

Vehicle-to-grid enabled charging infrastructure planning and operations considering demand uncertainties

Vehicle-to-grid (V2G) technology can alleviate the grid load pressure by delayed charging of electric vehicles (EVs) and discharging back to the grid in peak hours. This study aims to optimize V2G-enabled charger deployment and its operations considering two charging modes, public and private chargers. Two bi-objective models are formulated respectively to determine the optimal size of the V2G-enabaled chargers and the charging-discharging strategies considering uncertainties in photovoltaic (PV) energy output, EV charging demand, and household base load. The optimization problems are tested on a residential neighborhood in Shanghai, China. The results show that compared to unmanaged charging, the V2G modes could reduce peak-valley differences and load variation substantially, while increasing PV consumption slightly. The current financial feasibility of V2G system relies on additional subsidies. A fair market mechanism should be established to promote the healthy development of V2G applications.

Behavior-Driven Planning of Electric Truck Charging Infrastructure for Intercity Operations

Behavior-Driven Planning of Electric Truck Charging Infrastructure for Intercity Operations

Optimal Planning of Dynamic Wireless Charging Infrastructure for Electric Vehicles

Optimal Planning of Dynamic Wireless Charging Infrastructure for Electric Vehicles

Robust Resilience Enhancement by EV Charging Infrastructure Planning in Coupled Power Distribution and Transportation Systems

Robust Resilience Enhancement by EV Charging Infrastructure Planning in Coupled Power Distribution and Transportation Systems

Charging autonomous electric vehicle fleet for mobility-on-demand services: Plug in or swap out?

This paper compares two prevalent charging strategies for electric vehicles, plug-in charging and battery swapping, to investigate which charging strategy is superior for electric autonomous mobility-on-demand (AMoD) systems. To this end, we use a queueing-theoretic model to characterize the vehicle waiting time at charging stations and battery swapping stations, respectively. The model is integrated into an economic analysis of the electric AMoD system operated by a transportation network company (TNC), where the incentives of passengers, the charging/operating shift of TNC vehicles, the operational decisions of the platform, and the planning decisions of the government are captured. Overall, a bi-level optimization framework is proposed for charging infrastructure planning of the electric AMoD system. Based on the proposed framework, we compare the socio-economic performance of plug-in charging and battery swapping, and investigate how this comparison depends on the evolving charging technologies (such as charging speed, battery capacity, and infrastructure cost). At the planning level, we find that when choosing plug-in charging, increased charging speed leads to a transformation of infrastructure from sparsely distributed large stations to densely distributed small stations, while enlarged battery capacity transforms the infrastructure from densely distributed small stations to sparsely distributed large stations. On the other hand, when choosing battery swapping, both increased charging speed and enlarged battery capacity will lead to a smaller number of battery swapping stations. At the operational level, we find that improved charging speed leads to increased TNC profit when choosing plug-in charging, whereas improved charging speed may lead to smaller TNC profit under battery swapping. The above insights are validated through realistic numerical studies.

Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Radial Power Distribution Networks (PDNs) often suffer from limited reliability, flexibility, and efficiency, leading to service interruptions. Planning for radial PDNs is essential to enhance redundancy resilience, reduce disruptions, and improve overall efficiency. However, traditional PDN planning methods have become obsolete due to the proliferation of Distributed Generation (DG) resources and energy storage systems. Additionally, the rise of Electric Vehicles (EVs) demands sophisticated charging infrastructure planning. This article presents a Mixed-Integer Linear Programming (MILP) model for joint expansion planning of PDN and Electric Vehicle Charging Stations (EVCSs). The model takes into account the construction or reinforcement of substations and circuits, along with the integration of EVs, the installation of DGs, and the placement of capacitor banks, all regarded as traditional conventional expansion options alternatives. To address uncertainties associated with DG generation, conventional loads, and EV demand, our model identifies optimal installation and asset locations. We formulate this as a stochastic scenario-based program with chance constraints for Power Distribution Network Expansion Planning (PDNEP), minimizing investment, operational, and energy loss cost costs over a planning horizon. Through two deterministic and stochastic approaches, encompassing six case studies on an 18-node test system, we evaluate the effectiveness of our model. Results are further validated on a 54-node system, confirming the model’s robustness. Notably, the numerical findings underscore the substantial cost reduction achieved by including EVCSs in the stochastic expansion planning approach, demonstrating its cost-effectiveness. In case study I, where all EVs charge at home during peak hours, it’s the worst case for the PDN. The 54-node system, more complex, demands longer computational time. In the 18-node system, costs improve from 9.97% (case study II) to 3.96% (case study VI) versus the worst-case (case I). In the 54-node system, improvements range from 10.47% (case study II) to 1.40% (case study VI). As a result, In comparative analyses against deterministic and stochastic approaches, our model consistently outperforms in diverse test case studies. The proposed model’s adaptability to address uncertainties underscores its suitability for solving the PDNEP problem in PND.

Dual ant colony optimization for electric vehicle charging infrastructure planning

Dual ant colony optimization for electric vehicle charging infrastructure planning

Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach

Electric vehicle (EV) charging demand is an essential input of charging facility location models. However, charging demand may vary across seasons. In response, this paper first provided insights into the seasonal variance in charging demand using a unique GPS trajectory dataset which contained travel, parking, and charging information of 2,658 private EVs in Beijing. The dataset was collected in January, April, July, and October 2018, which were representative months in winter, spring, summer, and autumn, respectively. Through statistical and spatiotemporal analyses, we found that in winter, EVs got recharged when their state of charge (SOC) was lower: the average SOCs on working days were 51.96%, 48.39%, 50.86%, and 43.50%, in spring, summer, autumn, and winter, respectively. Furthermore, the central urban areas tended to have a higher charging demand in winter. To further explore how the seasonal variance in charging demand may influence infrastructure deployment, we used the classical p-median model to deploy charging facilities with the charging demands in the four seasons, considering the modifiable areal unit problem (MAUP). The results suggested that the seasonal variance did influence the layout of charging facilities under different spatial analysis units (SAUs). The deployment of charging facilities in the central urban areas and outer suburbs tended to be more sensitive to seasonal variance in charging demand. The findings are expected to be useful for charging infrastructure planning in both the transport and power sectors.

Enhancing equitable resilience of urban energy systems via strategic planning of EV charging infrastructure

This paper seeks to address the profound power resilience inequity in New York City by means of strategic allocation of electric vehicle (EV) charging infrastructure to support the power grid operation in challenging scenarios, such as when facing high demand or during natural disasters. First, we uncover the most disproportionately affected communities in New York by developing a metric of power resilience inequity to measure the combined impact of power failure-related factors on these areas. We employ data-driven approaches to infer the statistical relationships between communities’ power resilience index, their available EV charging infrastructure, and several other prominent socio-demographical variables. This inference yields the development of a machine learning model that can predict the reduction of power resilience inequity after deployment of the proposed resource allocation strategy. We further develop an optimization framework that jointly considers equity and efficiency to guide the optimized distribution of EV charging infrastructure across the city. A number of case studies are leveraged to demonstrate the capability of the devised approach in enhancing urban power resilience equity, yielding favorable results in marginalized communities.

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.

En-route fast charging infrastructure planning and scheduling for battery electric bus systems

En-route fast charging infrastructure planning and scheduling for battery electric bus systems

Electric Vehicle Fleet and Charging Infrastructure Planning

Electric Vehicle Fleet and Charging Infrastructure Planning

Electric Bus Scheduling and Charging Infrastructure Planning Considering Bus Replacement Strategies at Charging Stations

Electric Bus Scheduling and Charging Infrastructure Planning Considering Bus Replacement Strategies at Charging Stations

Coordinated Planning of Electric Vehicle Charging Infrastructure and Renewables in Power Grids

This paper proposes a new planning model to coordinate the expansion of electric vehicle charging infrastructure (EVCI) and renewables in power grids. Firstly, individual electric vehicle (EV) charging behaviours are modeled considering EV customers adopting smart charging services as the main charging method and those using fast charging, super fast charging and battery swapping services as a complementary charging approach. Next, EV aggregation and the associated system economic dispatch model are built. A novel model predictive control (MPC) learning approach is then proposed to iteratively learn the correlation between different types of EV charging loads and the EV interactions with renewables and other generating units in modern power grids of the future. The simulation results demonstrate that the proposed approach can be used to quantify the ratio of different types of charging loads in a region and strategically guide on the integration of EVs and renewables to achieve the clean energy transition goals. The proposed framework can also be used to decide charging capacity needs in a charging demand zone.

Full accessibility and user-friendly smart mobility services across the European charging infrastructure

Although most electric vehicle (EV) charging still takes place at home or work, widespread, open-access public charging infrastructure is essential to support EV drivers beyond early adopters. USER-CHI works to unlock the massive potential of electromobility in Europe by integrating innovative charging technologies with a holistic perspective, putting the user at the centre and empowering it, whilst at the same time makes financially attractive for the relevant stakeholders. To fill the existing gaps and to achieve its strategic challenges and objectives, USER-CHI*⁎https://www.userchi.eu/ combines technical, regulatory and business elements and produces a set of solutions that cover all aspects of a massive deployment of electric vehicles, from the optimal design and planning of innovative charging infrastructure to specific solutions to to allow interoperability and e-roaming in Europe (Martín et al., 2021).