Fast Charging Stations Research Articles

Optimal allocation of fast charging stations for large-scale transportation systems

The modern quest for sustainable cities increasingly relies on using distributed energy resources (DERs), which requires new planning practices. This paper proposes an optimisation strategy to solve the fast charging station (FCS) allocation of electric vehicles (EVs). A mixed-integer programming (MIP) model minimises investment and operation costs, considering the building of FCSs with photovoltaic (PV) systems over carports and battery energy storage systems (BESSs) as planning alternatives. The embedded set covering problem has special aspects that allow the development of a novel approach to evaluate candidate sites to accommodate FCSs. A preprocessing strategy is developed to fine-tune the entire solution space. A multiobjective approach is used to obtain an optimal compromise solution for the MIP model when it is required to serve the maximum number of EV owners at the lowest possible cost. The combined strategies reduce the computational burden, allowing full-scale studies of EV charging system planning. The results of studies using a real-world Brazilian case certify the benefits of the proposed strategy in the FCS allocation problem and in optimising the operation when considering renewable alternatives.

Distribution network voltage analysis with data-driven electric vehicle load profiles

The number of electric vehicles (EVs) is expected to increase significantly in the near future, which will affect the operation of future power networks. This study investigates the impact of EVs on the distribution network voltage profile by collecting measurements from residential, slow commercial, and fast public EV charging stations throughout the annual cycle. It is revealed from the data that annual utilization factors for residential, slow commercial, and fast public charging stations are 89%, 55%, and 24%, respectively. The optimal power flow solutions have been presented without the EV and with EV (home, commercial, and public) load profiles. Simulation results identify the voltage violation's extent, instant, and duration that may affect the future distribution grid. Most voltage violations happen in the evening, weekday afternoon, and midday for residential, slow commercial, and fast public EV charging stations, respectively. Furthermore, multiple enabling technologies have been demonstrated to reduce voltage violations, such as coordinated charging (reduced voltage violation by 50%), renewable charging (by 50%), and combinations of multiple types of customers (by 30%). This study presents some key insights into future power system operational scenarios with widespread EV charging stations that can be managed by multiple enabling technologies.

Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources: Hybrid approach

AbstractA hybrid method is proposed for electric‐vehicle (EV) fast charging station (FCS)‐based power electronics converters with energy‐storage‐systems (ESS) and renewable‐energy‐sources (RESs). The proposed approach is the combination of the fire hawk optimizer (FHO) and gradient boost decision tree (GBDT) algorithms; hence called as FHO‐GBDT approach. The key objective of the FHO‐GBDT approach is to lessen the peak power demand on the grid. The proposed method is incorporated into EV‐FCS with the capability of a mixture of RESs and energy‐storage‐systems. The capacities of energy‐storage aid in improving power‐demand by lessening the demand for peak power. The structure of the energy storage system minimizes the net cost of the DC micro‐grid (MG). The ESS is mostly composed of batteries, which are reused by EVs. The proposed approach and the ESS enable a decrease in obtaining the greatest amount of power possible from the power‐grid (PG). By then, the performance of the proposed approach is simulated in MATLAB, and it is compared to various existing methods. The simulation result shows that the proposed method offers more power than the existing methods.

Development of level three solar based fast charging station for electric vehicle

The whole research is based on the charging process of electric vehicle as this is the emerging future of Pakistan. to reduce the environmental pollution the whole world is transferring towards renewable energy, so the transportation sector is also a great source of causing environmental pollution so to reduce this problem the transportation section is moving toward pollution free vehicle as the electric vehicle is now a days in market. But the charging of electric vehicle is again must be pollution free to reduce pollution impact. In order to achieve this goal, the charging strategy must be pollution free so renewable energy is the best solution and to reduce the charging time the fast-charging station is presented to charge a battery in less time using renewable energy. The advanced used batteries are lithium-ion battery these batteries have two charging modes of charging combining these two will generate more effective results for charging in less time. By using two hybrid sources the system become reliable in case of weather dependent situation

Assessment of hybrid transfer learning method for forecasting EV profile and system voltage using limited EV charging data

The number of electric vehicles (EV) is increasing exponentially, significantly affecting the planning and operation of future electricity grids, albeit the availability of EV data is very limited to perform power system studies. To overcome the poor forecasting accuracy associated with limited available data, this research proposes a time-series-based hybrid transfer learning forecasting approach, namely CNN-BiLSTM (Convolutional Neural Network – Bidirectional Long Short-Term Memory) to forecast EV charging profile. Additionally, the proposed algorithm has been applied to forecast network voltage from the EV data without performing power flow. EV charging demand datasets collected over a year for residential, slow commercial, and fast commercial charging stations and their corresponding voltage profiles have been used to test the effectiveness of the proposed hybrid transfer learning framework. The results confirm the improved accuracy of the proposed hybrid CNN-BiLSTM model compared to the conventional CNN model, newly created models in predicting the EV charging demand and voltage profiles.

Reasons Why Only Kuwaiti Citizens Drive Electric Vehicles despite Being Only a Quarter of the Population

The adoption of fully battery-powered electric vehicles (EVs) in Kuwait is currently at less than one percent of the population, and hence Kuwait has the lowest index rank among countries around the globe. Effectively, only Kuwaiti nationals have the opportunity to own EVs, as there are no fast direct-current charging stations in Kuwait, and Kuwaiti landlords (expats cannot own real estate) do not allow the installation of EV-charging home wall boxes at their rented properties. Given these facts, this paper only focuses on the preferences of Kuwaiti nationals, even though they only constitute 25 percent of the population of Kuwait. To accomplish the present objectives, a quantitative descriptive method (closed-ended questions) was used to collect data from a sample of 227 Kuwaiti nationals who were representative of the owners of half a million internal combustion engine (ICE) cars and that were categorized as early majority consumers. The findings of the present study indicate that over 50 percent of the respondents would prefer to buy an EV in the following three years when certain criteria were satisfied, including government-controlled pricing policies and recharging point availability, high-speed roads, and free EV-dedicated parking spaces. Furthermore, over 40 percent of respondents stated that they would contemplate purchasing an electric vehicle if the price of gasoline or diesel increased by 19 to 50 percent. The findings also indicated that more than 40 percent of respondents believed that EVs are fire- and crash-safe, and roughly 50 percent of the respondents would be willing to pay between 6 and 20% more for an EV because they believe that EVs are ecofriendlier vehicles and are significantly faster than conventional petrol vehicles. Additionally, respondents rewarded those vehicles with an excellent mark because of their ecological, economical, and technological attributes and benefits.

A profitability assessment of fast-charging stations under vehicle-to-grid smart charging operation

Vehicle-to-grid (V2G) technology, a key driver for reducing carbon emissions and promoting sustainability, promises significant economic benefits through efficient energy exchanges between electric vehicles (EVs) and power distribution grids. However, the inherent uncertainty and variability in EV charging behavior pose challenges in accurately estimating potential economic gains from coordinated smart charging, presenting difficulties for charging service providers. In response to this, our study introduces a data-driven framework for assessing the profitability of fast-charging stations based on real-world operational data. This framework integrates data analytics, mixed-integer optimization, and behavioral theory. To address the computational challenge posed by optimizing charging schedules for numerous sessions, a tailored sub-gradient method is proposed. Additionally, a logit-based choice model is incorporated to account for the participation decisions of users. The flexibility of 0.7 million charging session profiles is thoroughly analyzed under peak-shaving incentives from the grid and quantifies the resultant monetary benefits. The analysis further extended to multiple influencing factors such as climate and the Covid-19 pandemic. Results suggest that fast charging stations under a coordinated charging scheme could experience a monthly bill curtailment of 20–30% compared to uncoordinated circumstances. These findings could pave the way for more efficient and profitable V2G operations, thereby accelerating the transition toward a more sustainable transportation infrastructure.

Can retired lithium-ion batteries be a game changer in fast charging stations?

As the global popularity of electric vehicles (EVs) continues to grow, the demand for EV batteries has significantly increased. Unfortunately, when the battery state of health (SoH) reaches a certain value, the batteries may no longer meet the driving requirements of EVs, resulting in many retired batteries. Properly disposing of these batteries is a major challenge. In this paper, we will take fast charging stations (FCSs) as an example to evaluate the economic feasibility of reusing retired batteries, which is one of the most environmentally friendly methods of disposal. To make a fair comparison between fresh and retired batteries, a bi-level sizing framework is designed to determine the optimal sizes of fresh and retired batteries, as well as photovoltaic (PV) panels in FCSs, to minimize the total cost. To ensure the high fidelity of results, diverse EV travel patterns are considered to generate the charging power demand. Additionally, a dynamic battery degradation model focused on batteries that have not yet reached their turning point is developed. This model is designed to precisely quantify the gradual reduction in battery capacity. We conducted several representative case studies using real-world data, and the simulation results indicate that FCSs with fresh batteries can achieve 42.2 % cost savings compared to those without energy storage systems, while retired batteries can achieve an additional 5.41 %–11.79 % cost savings under different scenarios. These findings can be generalized that retired batteries are promising in small-scale applications due to the relatively low refurbishment cost, and a new market stream can be potentially generated in this direction.

Fast Charging Station for Electric Vehicles Based on DC Microgrid

Increasing numbers of electric vehicles (EVs) have led to an increased power demand from the grid. The fast-charging station requires very high power for charging the EVs in a short time (30mins). The rapid high-power (80-240kW) demand results in voltage instability in the power grid. It is important to reduce the impact on the grid due to fast charging of EVs. The effect on the grid due to EVs fast charging is included in this paper. The grid instability is visible during the fast-charging period of the battery. As a solution, a renewable energy source integrated DC microgrid using a multi-step constant current fast charging method is proposed to reduce the effect on the grid. Additionally, the proposed system provides solar PV and/or vehicle to grid power transfer during the no charging/the peak demand period of the grid. The simulation and analysis of the proposed system using the coulomb counting based state of charge estimation method is implemented to show the grid impact and reduction using DC microgrid. A hardware prototype is developed to validate the proposed fast charging method and also to reduce grid impact during the fast-charging operation.

A CryStAl-RDF technique-based integrated circuit topology for fast charging station of electric vehicle (EV)

A CryStAl-RDF technique-based integrated circuit topology for fast charging station of electric vehicle (EV)

Identifying generalizable equilibrium pricing strategies for charging service providers in coupled power and transportation networks

Transportation electrification, involving large-scale integration of electric vehicles (EV) and fast charging stations (FCS), plays a critical role for global energy transition and decarbonization. In this context, coordination of EV routing and charging activities through suitably designed price signals constitutes an imperative step in secure and economic operation of the coupled power-transportation networks (CPTN). This work examines the non-cooperative pricing competition between self-interested EV charging service providers (CSP), taken into account the complex interactions between CSPs' pricing strategies, EV users' decisions and the operation of CPTN. The modeling of CPTN environment captures the prominent type of uncertainties stemming from the gasoline vehicle and EV origin-destination travel demands and their cost elasticity, EV initial state-of-charge and renewable energy sources (RES). An enhanced multi-agent proximal policy optimization method is developed to solve the pricing game, which incorporates an attention mechanism to selectively incorporate agents' representative information to mitigate the environmental non-stationarity without raising dimensionality challenge, while safeguarding the commercial confidentiality of CSP agents. To foster more efficient learning coordination in the highly uncertain CPTN environment, a sequential update scheme is also developed to achieve monotonic policy improvement for CSP agents. Case studies on an illustrative and a large-scale test system reveal that the proposed method facilitates sufficient competition among CSP agents and corroborates the core benefits in terms of reduced charging costs for EV users, enhancement of RES absorption and cost efficiency of the power distribution network. Results also validate the excellent generalization capability of the proposed method in coping with CPTN uncertainties. Finally, the rationale of the proposed attention mechanism is validated and the superior computational performance is highlighted against the state-of-the-art methods.

Life cycle optimization framework of charging–swapping integrated energy supply systems for multi-type vehicles

The energy supply infrastructure is an important guarantee for vehicle electrification. Its economy, service capability and grid friendliness are critical factors drawing wide attention. To reduce the cost of energy storage devices that alleviate the high-power grid impact from fast charging station, this study proposes a novel energy supply system configuration that integrates fast charging for passenger vehicles and battery swapping for heavy trucks, and discharges the large-capacity swapping batteries to support fast charging. The influences of station configurations, including in-station batteries, charging and swapping equipment on the system economy, service quality, and grid capacity demand are revealed through system modeling and analysis. The impact of the charging time on battery degradation during operation is also explored. Moreover, a life cycle optimization framework for the charging–swapping integrated system is formulated, together with the complementary control strategy that realizes bidirectional energy coupling of fast charging and battery swapping loads. In this framework, the battery swapping service model is established to quantify the service quality; the electrochemical mechanism model is used to evaluate the battery degradation during charging and discharging processes; and the life cycle cost model is established by integrating the investment and operation costs of the energy supply system. On this basis, the design and control variables are collaboratively optimized towards the maximal life cycle benefits. Based on the actual load characteristics of charging and swapping stations, a comparative study is performed for the proposed operation scheme and the general service quality-prioritized scheme. The obtained results show that the maximum station power is reduced by more than 0.6 MW, and the total life cycle cost of the energy supply system is reduced by over 1 million RMB under the proposed scheme, verifying its notable effect of life cycle economy improvement.

Reliability oriented techno- economic assessment of fast charging stations with photovoltaic and battery systems in paired distribution & urban network

Installing fast charging electric vehicle stations (FCEVS) is crucial for increasing public acceptance of electric vehicle (EV) adoption. The enormous energy demands of FCEVS, as well as the inclusion of renewable energy resources (RES) into utility grids, may have a significant influence on system reliability and pose challenges owing to uncertainty. Therefore, a new reliability-oriented analysis-based two-stage planning framework for optimal deployment of solar photovoltaic (SPV) powered FCEVS and battery energy storage systems (BESS) in coupled transportation and distribution networks is proposed in this article, while encapsulating the complex interactions between EVs, SPVs, and BESS. A novel composite metric, the average voltage deviation reliability index (AVDRI), has been developed for the deployment of FCEVS infrastructure considering the goals and limitations of both distribution and transportation networks. Models such as the demand for charging electric vehicles, the reliability evaluation system, and the economic analysis model are offered to provide essential inputs for the study of distribution network reliability and profitability. In the first stage, AVDRI, active power loss, captured EV flow, and capital, along with operating and maintenance costs of the FEVCS and SPV system, are all taken into consideration as objectives when optimizing the placement and sizing of SPV assisted FCEVS using Multi Objective Particle Swarm Optimization (MOPSO) and a fuzzy satisfaction-based hybrid technique. Then, the optimal resource sizing, such as the count of charging spaces, the capacity of the SPV system at each optimal FCEVS, and captured EV flow, are estimated. In the second step, a bi-section approach is used to compute the capacity of the BESS and the extra SPV rating necessary to power it at each optimal FCEVS location, taking into account the irradiance profile for all conceivable seasons within a planning year. A 25-node transportation network coupled to an IEEE 33 bus distribution system is used to validate the proposed methodology under a range of seasonal scenarios.

Economic assessment of integrating fast-charging stations and energy communities in grid planning

Distribution grid companies and distribution system operators (DSOs) still mostly follow a traditional framework for grid planning. Such frameworks have so far served DSOs well in the economic assessment and cost–benefit analysis of passive measures, such as grid reinforcement. However, the development towards active distribution grids requires DSOs to also be able to assess an extended set of active measures. To this aim, this paper extends and implements a general planning framework for active distribution grids that builds upon the well-proven traditional framework. The methodology integrated in the framework includes: (1) decoupled models for (i) operation with active measures and (ii) optimal grid investment, and (2) methods for economic assessment considering active measures from both (i) a DSO cost–benefit analysis perspective and (ii) a willingness-to-pay perspective. In this paper, operational models are integrated for two examples of active measures, namely the use of fast-charging stations (FCS) and local energy communities (LEC). The methodology is demonstrated in a long-term grid planning case study for a realistic Norwegian medium voltage distribution system. For this case, grid planning with FCS as an active measure reduces the present value of grid investment costs by 70% compared with a passive grid planning strategy. The results also demonstrate how the methodology can be used in negotiating the price of active measures between the DSO and distribution system actors such as LEC and FCS operators.

Topology comparative assessment for hybrid medium-voltage AC/DC networks

Recently, there has been significant growth in integrating renewable energy generation and electric vehicles and deploying new technologies into the electrical networks, leading to new challenges for planning, design, operation, control and protection of the distribution grid. In particular, an increasing amount of large-scale DC load and generation is expected to be connected to the distribution system, especially at the MV level. Thus, adoption of new grid topologies is required and among others, hybrid AC/DC networks have emerged as an attractive pathway to meet new grid operational requirements. This paper evaluates future hybrid AC/DC distribution systems at the MV level, considering potential network topologies and new technologies. Steady-state analysis is performed to compare five topologies of hybrid MV AC/DC networks. Key technologies for such hybrid grids such as voltage source converters and fast charging stations are considered. The results provide quantitative comparisons between topologies regarding large-scale DC load and generation integration capability, contingency operation, sensitivity analysis. Several challenges presented to the protection and fault location that are caused by the adoption of new topologies are also identified. The topologies are compared by network losses, voltage and power factor constraints, analysing the different benefits and drawbacks of the hybrid AC/DC topologies.

A New Hybrid Modular Multilevel Rectifier as MVac–LVdc Active Front-End Converter for Fast Charging Stations and Data Centers

As the power demands from DC energy-storage and loads continue to grow in electric vehicle (EV) fast charging stations and data centers, the power delivery infrastructure faces challenges with the installation of bulky, heavy, and slow responsive line-frequency power transformer (LFT). These transformers are required to step-down the feeder voltage from medium-voltage (MV) ac grid-service to low-voltage (LV) ac, followed by LVac-LVdc rectifiers. This approach results in a large equipment footprint, heavy conductor copper usage, and lower efficiency. Consequently, there is increasing interest in exploring direct interface from MVac to LVdc without the need for LFT. This paper proposed a new solution called MVac-LVdc hybrid modular multilevel rectifier (HMMR). The HMMR serves as a centralized step-down active front-end converter, enabling power delivery to LVdc with a reduced number of dc/dc back-end isolated converters. Compared to the modular multilevel converter (MMC) used as the MVac interface solution, the proposed HMMR could save the submodule number by 40%, reduce losses by 22%, and significantly reduce the footprint area by 37%, effectively increasing the power density and reducing the construction cost. Moreover, the proposed HMMR has the potential to operate in both unity and non-unity power factor modes, allowing it to provide the grid-support functionality. The performance of HMMR is evaluated and compared with full-bridge MMC in the case of 13.8 kV ac to 6 kV dc. The feasibility of the proposed converter is verified by the simulation results with the same specifications. Finally, a scale-down 1.4 kV HMMR prototype is developed to validate the effectiveness of the proposed converter.

(Invited) NiO/ β-(Al x Ga1-x )2O3 /Ga2O3 Heterojunction Lateral Rectifiers with Reverse Breakdown Voltage > 7kV

There is significant recent interest in development of Ga2O3 power devices due to their capability for high temperature operation, reduced on-state and switching losses due to lower on-resistance for high voltage devices, and potentially higher frequency switching capability. These are targeted for renewable energy transmission systems, electric vehicle (EV) traction inverter and motor control systems, fast charging stations and more electric aircraft. Since the efficiency of EV powertrain inverters is partially determined by the efficiency of the switching transistors, it is of interest to examine ultra-wide-bandgap semiconductor electronics. The successful development of these power transistors is expected to significantly increase the longevity of a battery charge and the resultant cost of an EV.In this work, NiO/ β-(Al x Ga1-x )2O3 /Ga2O3 heterojunction lateral geometry rectifiers with diameter 50-100 µm exhibited maximum reverse breakdown voltages >7kV, showing the advantage of increasing the bandgap using the β-(Al x Ga1-x )2O3 alloy. This Si-doped alloy layer was grown by Metal Organic Chemical Vapor Deposition with an Al composition of ~20%. On state resistances were in the range 50-2180 Ω.cm2, leading to power figures-of-merit up to 0.72 MW.cm-2. The forward turn-on voltage was in the range 2.3-2.5 V, with maximum on/off ratios >700 when switching from 5V forward to reverse biases up to -100V. Transmission line measurements showed the specific contact resistance was 0.12 Ω.cm2. The breakdown voltage is among the highest reported for any lateral geometry Ga2O3-based device. Figure 1

Assessing the Impacts of Electric Vehicle Penetration in Curaçao’s Power Network

Electric vehicles (EVs) have gained considerable attention in the last decade due to a paradigm shift in the transport sector driven by a higher awareness of environmental issues. While the importance of EVs cannot be overstated in the context of the global climate crisis, it does raise the question of whether certain countries or states are ready for their implementation. It is, therefore, necessary to analyze the impact of EVs in the power grids of these countries and states, considering factors such as line congestion and the eventual degradation of voltage profiles, to determine their hosting capacity and assess eventual expansion options. This paper proposes a representative prototype of Curaçao’s electrical system, which is used for assessing the impacts of EVs, allowing us to determine its hosting capacity. Curaçao is an island in the southern Caribbean Sea that uses fuel generators, wind energy, and solar energy to generate electricity. The idea behind this paper is to analyze the effects caused by an increase in EVs on Curaçao’s power grid and propose preventive measures to deal with such problems. Eight EV charging stations were considered, one DC super fast-charging station, three normal DC fast-charging stations, and four AC fast-charging stations. In 2022, there were an estimated 82,360 vehicles on the island. Using this information, this paper analyzes how many vehicles can be simultaneously connected to the grid before it no longer operates under acceptable values. The results showed that 3.5% of the total vehicles can be hosted by the grid. Nonetheless, this can be increased up to 4.5% with the reinforcement of a transmission line.

Bilevel optimization approach to fast charging station planning in electrified transportation networks

Unplanned en-route charging of electric vehicles (EVs) can unexpectedly cripple traffic conditions. As less-effective location of fast charging station (FCS) may exacerbate this issue, the effects of FCS on EVs' charging behaviors should be incorporated at the system planning stage. This paper proposes a strategic-charging-behavior awared model in the context of electrified transportation network environment. This model is formulated into a bilevel mixed-integer programming problem. A newly designed network equilibrium model targets on the low-level problem, which is to model the drivers' charging reaction to a certain FCS layout. In considering the EV self-serving routing and charging behaviors as well as power network constraints, the upper-level problem is formulated for location and sizing decision-makings, of which the objective is to minimize the overall traffic time and investment cost. To handle the proposed bilevel problem, a descent algorithm is further developed. To examine the effectiveness of the proposed approach, extensive numerical experiments are conducted, through which the obtained results demonstrate that the EV charging demand can perfectly be met while traffic congestions can be alleviated to a great extent.

Optimal EV Fast Charging Station Deployment Based on a Reinforcement Learning Framework

This study aims to determine the optimal deployment plan for EV fast charging stations in a transportation network with a limited budget. The objective of the deployment problem is to maximize the quality of service (QoS) with respect to both waiting time and range anxiety from the perspective of EV customers. With the rapid growth of the electric vehicle (EV) market penetration, state-of-the-art algorithms based on mathematical programming are limited in handling high-dimensional optimization problems adequately. Unlike previous studies, we make the first attempt to formulate the fast charging station deployment problem (FCSDP) as a finite discrete Markov decision process (MDP) in a novel reinforcement learning (RL) framework to alleviate the curse of dimensionality problem. Since creating a supervised training dataset is impractical due to the high computational complexity of the FCSDP, we propose a recurrent neural network (RNN) with an attention mechanism to learn the model parameters and determine the optimal policy in a completely unsupervised manner. Finally, numerical experiments are conducted on multiple problem sizes to evaluate the performance of the RNN-based RL framework. Simulation results show that the proposed approach outperforms the comparing algorithms in terms of solution quality and computation time.