Electric Vehicle Charging Network Research Articles

Joint Path Selection, Energy Trading, and Task Offloading in Electric Vehicle Charging and Computing Network

Joint Path Selection, Energy Trading, and Task Offloading in Electric Vehicle Charging and Computing Network

Reconfigurable-Intelligent-Surface-Enhanced Dynamic Resource Allocation for the Social Internet of Electric Vehicle Charging Networks with Causal-Structure-Based Reinforcement Learning

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.

Optimizing wireless charging infrastructure placement using genetic algorithms

Electric vehicles (EVs) play a crucial role in tackling environmental issues in the transportation industry. The incorporation of effective charging infrastructure is crucial in promoting the broad acceptance of electric vehicles (EVs). This work investigates the optimization of the location of wireless charging infrastructure in urban contexts using genetic algorithms (GAs). The location data, which includes latitude and longitude coordinates, showed a wide range of spatial distributions that are ideal for deploying charging stations. These distributions display variances that are favorable for strategically placing the infrastructure. The examination of power consumption data revealed significant variations in energy demand across different sites, ranging from 180 kWh to 300 kWh. These differences indicate that each location has its own distinct energy needs. The population density statistics exhibited a spectrum of values, ranging from 600 individuals per square unit. The population density is 1200 persons per square kilometer. The abbreviation "km" refers to kilometers, which is used to indicate different levels of prospective electric vehicle (EV) users. In addition, the distance data provided information about the lengths between prospective locations for charging stations, which varied from 400 km to 1200 km. These distances had an impact on the concerns of connection and transmission efficiency. The research highlights the intricate nature of the elements that affect the ideal location of infrastructure, underlining the need for a methodical approach to optimization. Integrating these statistics provides a foundation for developing an objective function in the GA framework to optimize the location of charging infrastructure. The study's results provide valuable understanding of the many factors that influence the location of charging infrastructure. The goal is to promote the development of efficient and easily accessible electric vehicle charging networks in metropolitan areas.

Multiobjective Electric Vehicle Charging Network Planning Considering Chance-Constraint on the Travel Distance for Charging

Electric vehicle charging stations (EVCSs) are important infrastructures to support sustainable development of electric vehicles (EVs), by providing convenient, rapid charging services. Therefore, the planning of electric vehicle charging network (EVCN) has attracted wide interest from both industry and academia. In this paper, a multiobjective planning model for EVCN is developed, where a fixed number of EVCSs are planned in the traffic network (TN) to achieve two objectives, i.e., minimizing both average travel distance for charging (TDfC) of EVs and investment costs of EVCN. According to the random characteristics of EVs’ TDfC, its constraint is presented as a chance constraint in the developed EVCN planning model. The nondominated sorting genetic Algorithm II with the constraint domination principle (NSGA-II-CDP) is customized to solve the developed multiobjective EVCN planning model, by designing a special coding scheme, a crossover operator, and a mutation operator. Then, a maximum gradient principle of investment revenue is designed to select the optimal planning strategy from the Pareto-optimal solution set, when taking the investment return ratio as primary consideration. A 25-node TN is used to justify the effectiveness of the developed methodology.

Anticipatory planning for equitable and productive curbside electric vehicle charging stations

Two challenges to planning public electric vehicle (EV) charging networks remain in U.S. cities, including uneven productivity of charging stations and inequitable charging access across user groups. The unpredictable EV market penetration over the long term further complicates the relevant infrastructure planning. However, the extant planning approaches are limited in addressing both challenges simultaneously when considering future uncertainties. Therefore, we propose a data-driven anticipatory framework to plan for EV charging station allocation near urban amenities based on charging-while-parking behavioral patterns. We focus on two user groups, i.e., multi-family and single-family residents. We compare the productivity-equity outcomes of allocation scenarios under three planning strategies and four possible ratios between both user groups. The framework addresses the incremental charging demands at different market levels for each scenario. An in-depth case study of Alachua County, FL, shows that over-emphasizing multi-family charging demands when placing EV charging stations may undermine their overall productivity. We then suggest three pathways to balance equitable access and optimized productivity for the community based on the comparison of planning scenarios. The proposed framework is generalizable to other EV-initiating communities. This study sheds light on future-oriented adaptive planning for transportation infrastructure during the energy transition.

Hierarchical framework for demand prediction and iterative optimization of EV charging network infrastructure under uncertainty with cost and quality-of-service consideration

As electric vehicles (EVs) continue to grow in popularity, the demand for efficient and effective EV charging infrastructure is rapidly increasing. However, the design and deployment of such infrastructure faces numerous challenges, some of which include demand uncertainty, EV network layout, maintenance cost, and quality of service (QoS), and have proven to be particularly challenging in cases where all objectives need to be addressed simultaneously. To address these challenges, this study proposes a hierarchical iterative optimization approach that aims to minimize the demand–supply mismatch at a higher level and considers both cost and QoS under uncertainty at a lower level in a dynamic EV charging network. Specifically, the proposed approach uses a time-series linear regression ensemble to accurately predict future demand at demand points, which is then used to match demand at demand points with supply at supply points. We then model the infrastructure allocation problem as a knapsack problem and use an allocation algorithm to determine the optimal number of charging stations at each supply point. The infrastructure is allocated to minimize total cost and maximize QoS. A framework for EV infrastructure optimization is designed and can be applied to any type of network. The effectiveness of our proposed approach is demonstrated through extensive experimentation on various datasets by comparing with existing state-of-the-art approaches. Simulation results show that the proposed approach is robust to different EV networks, handles uncertainty well, can effectively address the challenges associated with EV infrastructure optimization, and can provide policy makers and infrastructure planners with a practical and efficient tool for designing and deploying charging infrastructure. This study contributes to the field of EV infrastructure network optimization by modeling QoS and proposing a hierarchical optimization framework that effectively addresses the challenges associated with the design and deployment of charging infrastructure.

Active distribution system planning considering non-utility-owned electric vehicle charging stations and network reconfiguration

This paper introduces a new mixed-integer linear programming formulation for planning active distribution networks and non-utility-owned electric vehicle charging stations (EVCS), using network reconfiguration as a planning option. The method employs multi-objective optimization to address the commercial interests of the utility and the EVCS owner separately. In this manner, the utility makes investment decisions for network assets, such as replacing overloaded conductors and installing capacitor banks and voltage regulators, whereas the EVCS owner decides on the purchase of land and infrastructure for the EVCS installation. Furthermore, as an outstanding feature, the proposed formulation incorporates network reconfiguration within the planning options. The proposed model aims to minimize the total expected cost for both owners. A travel simulation algorithm provides the EVCS load profiles. At the same time, scenario-based optimization addresses uncertainties related to substation energy prices, wind speed, solar irradiance, electricity demand, adoption rate, and loading profile of plug-in electric vehicles. The effectiveness of the proposed model has been demonstrated in a 69-node network. The findings show that when deciding where to install the EVCS (network node), the objectives of both owner’s clash. This discrepancy, however, can be reduced through network reconfiguration. Finally, the scalability of the proposed method was evaluated using a real 134-node system.

Electric vehicle charging network design with capacity and service considerations

Electric Vehicles (EVs) have a critical role in the future of sustainable transportation systems. Recently, improving the infrastructure for EVs has been a major focus of organizations around the world, such as local and federal governments, energy companies, and automotive original equipment manufacturers. In this article, we present a service network design framework to improve the infrastructure for EVs with quality of service constraints taken into consideration. We present a novel charging station location model with capacity allocation. The model maximizes the EV traffic flow captured and minimizes the average service time for drivers. The model formulation allows for assessing capacity and quality of service trade-offs, and operational insights are presented to reflect drivers’ expectations. We demonstrate an application for the solution approach on a real road network and evaluate the performance based on both computational efficiency and solution quality. Our results highlight how charging times at stations affect the quality of service in the whole service system by quantifying the improvements in flow coverage while reducing waiting times. The value of faster chargers in increasing coverage and reducing the average waiting times is also highlighted in relation to a service provider’s budget constraints.

Spatial–temporal QoS assessment of the EV charging network considering power outages

The rapid expansion of electric vehicle charging networks (EVCNs) makes them critical infrastructures bridging power and traffic systems. The EVCN could be vulnerable during power outages at fast charging stations (FCSs), which are induced by planned maintenance or emergency load shedding. This paper proposes an approach to assess the impact of power outages on the Quality-of-service of the EVCN. The Markov decision process is utilized to model the spatial–temporal randomness of EV movement in a graph-based EVCN. The decision of charging by EV drivers is estimated by a fuzzy logic inference system. The spatial–temporal EV charging load at FCSs is formulated by a queuing-based non-linear optimization problem. Yen’s algorithm is adopted to simulate the EV redistribution phenomenon of searching adjacent healthy FCSs in response to the power outage. Quality-of-service (QoS) indices are derived to assess the potential congestions in the adjacent healthy FCSs. The case studies demonstrate that power outages may cause congestion at peripheral FCSs, exacerbating the QoS of the EVCN. Partial charging may alleviate the QoS deterioration in the event of FCS outages.

Electric vehicle entire-trip navigation and charging reservation method based on a high-speed communication network

Having a large number of electric vehicles (EVs) on the road will lead to vehicle congestion, long waiting times at charging stations (CSs), and even the inability to find an available charging position. Main technical challenges in mobile EVs charging may include charging information exchanges between EVs and CSs, EV traveling route and CS selection, and EV charging planning. To solve the above problems, this study provides an optimization solution for entire-trip charging and navigation of EVs, which balances charging resources, protects the travel time of EV drivers, and prioritizes renewable energy. The communication scheme of an EV charging network is designed, including real-time communication between the CSs, EV drivers, road condition monitoring center, power grid company, and the charging control center distributed in every parking lot. CSs in each road segment sort vehicles arriving at stations, allocate charging time slots, provide charging start times, and adjust those times for EVs with reservations that have not yet arrived. EV drivers can plan vehicle charging on their smart devices. The optimization of electric navigation can reduce the travel time or cost for drivers, ensure safe electricity usage, calculate vehicle routes, select CSs, and provide charging time intervals. The effectiveness of the method is verified on a regional road network in China, and the results show that it can balance the charging resource distribution of a CS, guarantee EV state of charge level, greatly reduce travel cost or travel time, provide precise waiting time, and prioritize renewable energy.

$\bm {P}^{\bm {3}}$: Privacy-Preserving Prediction of Real-Time Energy Demands in EV Charging Networks

Real-time and accurate prediction of charging pile energy demands in electric vehicle (EV) charging networks contributes significantly to load shedding and energy conservation. However, existing methods usually suffer from either data privacy leakage problems or heavy communication overheads. In this article, we propose a novel blockchain-based personalized federated deep learning scheme, coined <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P^{3}$</tex-math></inline-formula> , for privacy-preserving energy demands prediction in EV charging networks. Specifically, we first design an accurate deep learning-based energy demands prediction model for charging piles, by making use of the CNN, BiLSTM, and attention mechanism. Second, we develop a blockchain-based hierarchical and personalized federated learning framework with a consensus committee, allowing charging piles to collectively establish a comprehensive energy demands prediction model in a low-latency and privacy-preserving way. Last, a CKKS cryptosystem based secure communication protocol is crafted to guarantee the confidentiality of model parameters while model training. Extensive experiments on two real-world datasets demonstrate the superiorities of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P^{3}$</tex-math></inline-formula> scheme in accurately predicting real-time energy demands over state-of-the-art schemes. Further, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P^{3}$</tex-math></inline-formula> scheme can achieve reasonably low computational costs, compared with other homomorphic-based schemes, such as Paillier and BFV.

Charging Ahead

This year the UK and the US are seeing the roll out of electric vehicle (EV) charging networks happening faster than ever

Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

This paper aims to investigate the electric vehicle (EV) charging network design and utilization management considering user-centric decisions. A hierarchical formulation is developed with the EV charging network design and demand-driven pricing scheme in the upper level and users’ charging decisions to minimize their own travel costs and charging expenses in the lower level. The model aims to minimize the facility deployment cost and maximize the charging income of the network operator while minimizing the user-centric costs. We have converted the proposed bi-level formulation into an equivalent single-level model using the lower-level objective function as complementary equations. Then, we have developed an iterative active-set based solution technique to determine the strategic decisions on charging network design. To partially overcome the computational burden, the arc travel times are estimated using a macroscopic fundamental diagram concept. The proposed integrated methodology is applied to a hypothetical and an empirical case study to evaluate its performance and solution quality. The numerical results indicate that the proposed algorithm can solve the problem efficiently and outperform a system-level bi-level optimization benchmark. Our experiments show a CPU time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.3~hr$ </tex-math></inline-formula> for the proposed approach compared to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$173.1~hr$ </tex-math></inline-formula> of the benchmark. Finally, a series of sensitivity analyses has been conducted to study the impact of input parameters on the solutions and draw managerial insights.

Mercedes-Benz plans proprietary high-power EV charging network

Each Mercedes station will have four to 12 ports delivering up to 350kW of charging power. Mercedes-Benz has unveiled plans to build its own worldwide electric vehicle charging network, starting in North America.

Impact of plug-in electric vehicles on grid integration with distributed energy resources: A review

The rise of electric vehicles (EVs) has a massive impact on the electricity grid due to the electrification of vehicles in the transportation sector. As a result, various techniques are needed to minimize the effects of charging on the grid. One of these techniques is having intelligent coordination between the various components of the EV charging network. This ensures that the network has enough electricity to support the charging needs of the vehicles. This article provides an overview of the many aspects of the EV industry and its charging infrastructure. It also provides a step-by-step approach for implementing the Vehicle to Grid (V2G) deployment, the utilization of recordings from the data by the EV battery through Artificial Intelligence and the cost-benefit analysis from effective utilization of the V2G method. The paper also explores the various aspects of the EV market and the role of aggregators and consumers. Finally, it assesses the possibility of expansion of the EV charging and grid integration system and outlines its challenges and solutions.

Сприяння впровадженню електромобілів як науково-технічна інновація в галузі автомобільного транспорту

The purpose of this study is to analyze the consequences of the implementation of the concept of market orientation on the Ukrainian market of passenger electric cars and to determine the impact of innovations on its development. It is also necessary to analyze the application of new constructive solutions in the field of road transport, which contribute to the wider introduction of electric vehicles. As of January 1, 2023, the total number of passenger electric cars registered in Ukraine is 46,830 cars. Thus, the share of electric vehicles in the total mass of vehicles in our country is about 0.5%. The main driver of the increase in the number of electric cars was economy and a higher level of environmental safety compared to diesel and gasoline cars. Ukraine prioritizes the transition to electric road transport by January 1, 2030. In 2017, the Ministry of Infrastructure of Ukraine developed a 15-year strategy for the development of electric transport in Ukraine. According to the Ministry of Infrastructure of Ukraine, as of November 1, 2021, the Ukrainian charging network for electric vehicles included 3,244 stations with 7,661 terminals. Of them, high-speed (power over 22 kW) – 1,835 units, i.e. 24%. Other 5826 units. (76%) – ordinary terminals with a capacity of up to 22 kW. The main obstacles to the wider introduction of electric vehicles are their cost, underdeveloped recharging, service and repair infrastructure, as well as an insufficient number of maintenance and repair specialists. According to analytical data, the world electric car market is facing a serious transformation. Due to a number of factors such as a jump in electricity prices in Europe, the introduction of electric car taxation, the end of subsidies, problems with the development of global charging infrastructure, a sharp increase in the prices of raw materials, in particular, lithium and nickel. As a result, as noted, the rate of growth of sales of electric cars may slow down, and in some regions may even come to nothing. Electric cars give owners the opportunity to completely abandon traditional types of fuel and save their own money, and the state to localize the sphere of regulation of the level of environmental and man-made safety in the locations of power plants.

Cascading failures in EV charging network

With the rapid increase of Electric vehicle (EV) penetration, EV charging network become an important infrastructure. This paper studies the configuration of EV charging network from a safety perspective. It is found that cascading failures may happen if one charging station is out of work and its load has to be redistributed to its neighboring stations. The cascading failure model borrowed from network science area is used in the study, and the results show the effects of system parameters on the network robustness. The results indicate that in order to achieve a good performance, it is better to assign extra load based on neighbors’ degree when a node is overloaded. On the other hand, the extra load redistribution should not consider the distance between the overloaded node and its neighbors.

Research on a Charging Mechanism of Electric Vehicles for Photovoltaic Nearby Consumption Strategy

With the promotion of the pilot development of distributed whole county roof photovoltaics in China, problems such as power consumption, energy regional balance, and grid stability have become prominent. In this paper, an application mode of electric vehicle (EV) charging network and distributed photovoltaic power generation local consumption is studied. The management idea of two-layer and four model has been established, including the regional distributed photovoltaic output model, electricity consumption model, EV consumption model, and regional grid load dispatching model, which can realize the scheduling of the energy flow formed by photovoltaic, induce the charging of EVs, and make the photovoltaic consumption in office building areas and residential building areas complementary. Firstly, according to the randomness of photovoltaic power generation and EV charging, the dynamic response capability, power support capability, effective convergence time, system stability, system failure rate, and other characteristics of regional loads are comprehensively analyzed, and the grid energy management model of EV charging network and distributed photovoltaic is proposed. Secondly, according to certain statistical characteristics, the distributed photovoltaic will be concentrated, and EV charging will be prioritized to achieve nearby consumption. Finally, different scenarios are described, and the scenarios of charging in the park, community life, and power supply service are selected for analysis. This mode is intended to guide the consumption of new energy through economic leverage, which can realize the unified regulation of distributed energy convergence, consumption and storage.

Comprehensive evaluation of electric vehicle charging network under the coupling of traffic network and power grid

With the rapid development of electric vehicle (EV) technology under the background of double carbon, a reasonable evaluation of the network planning of EV charging stations is conducive to the long-term stable development of the EV industry. Therefore, this paper proposes a comprehensive evaluation system and method for EV charging networks under the traffic network and power grid coupling. First, an EV travel model is developed based on the travel probability matrix to analyze the spatial and temporal characteristics of EVs. Secondly, the coupling relationship among users, charging network, road network, and the power grid is analyzed, four criteria of user feedback, charging network operation, road network operation impact, and power grid operation impact are proposed, and corresponding evaluation indexes are constructed under each criterion to form a comprehensive evaluation index system. Furthermore, the Analytic Hierarchy Process (AHP) and entropy weighting method are used to assign weights to the index layer and the criterion layer respectively, and the improved TOPSIS evaluation method is used to quantitatively analyze the impact of charging networks on different subjects for comprehensive evaluation. Finally, through the comprehensive evaluation of different charging network planning schemes, the effectiveness of the comprehensive evaluation method and model proposed in this paper is verified, which can provide a reference basis for the planning and improvement of EV charging station networks.

Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure

This paper proposes a novel combined event-based simulation model for assessing the explicit demand response potential of electric vehicle (EV) charging networks. The model utilizes different multivariate copulas in generation of realistic artificial charging events that effectively retain the complex dependency structures and parameter distributions of real data important for accurate demand response simulation. A deterministic model is used to estimate the maximal explicit demand response potential of individual charging events based on technical requirements of the frequency containment reserve for disturbance situations (FCR-D) market. The proposed model achieved a mean absolute percentage error (MAPE) of 3.27% when considering averaged daily dispatchable FCR-D potentials, and a MAPE of 4.65% in prediction of dispatchable FCR-D potential with one workweek of data. The results and methodology have been verified and validated with real life data and through comparison with a previous non-copula application for EV FCR profile estimation which it outperformed. The combined event-based simulation model can boost active participation of EVs in power network balancing and is suitable for use in various practical and theoretical applications.