Electric Vehicle Charging Stations Research Articles

Location and capacity optimization of EV charging stations using genetic algorithms and fuzzy analytic hierarchy process

Location and capacity optimization of EV charging stations using genetic algorithms and fuzzy analytic hierarchy process

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

Optimizing electric vehicle charging scheduling using enhanced multi-agent neural networks with dynamic pricing

Electric vehicles have gained widespread recognition as an environmentally conscious mode of transportation, primarily due to significant technological advancements and reduced emissions. When seamlessly integrated into the smart grid infrastructure, these vehicles can serve as flexible energy consumers and potential energy storage units. However, the efficient management of charging stations due to dynamic load demand & pricing poses a significant challenge, aiming to reduce wait times for electric vehicle owners and reduce electricity costs. To overcome this challenge, this study proposes a solution based on dynamic pricing policy and the adaptive Markov decision process. The approach utilizes a reinforcement learning-enabled enhanced multi-agent neural network (EMANN) to optimize charging schedules. This adaptive Markov decision process achieved optimal scheduling by minimizing charging costs during off-peak hours while completing charging in the shortest possible time. Furthermore, a constrained policy is employed to train the enhanced neural network using reinforcement learning, allowing the network to learn and solve adaptive Markov decision processes directly. To evaluate the effectiveness of this solution, the study conducted a numerical experiment using real-time data from Tesla electric vehicles. Combined with queuing models, the EMANN yielded impressive results, including a peak savings of 19.05% and an average profit of $376.1 per kilowatt-hour, achieved within a network convergence time of 512 s. The proposed algorithm for scheduling electric vehicles has been proven to be efficient in addressing the complexities associated with managing electric vehicle charging stations.

Optimal allocation of EV charging station and capacitors considering reliability using a hybrid optimization approach

This paper proposes a novel approach for strategically placing EV charging stations (EVCS) and capacitors within electrical distribution networks (EDN) to mitigate issues arising from the widespread adoption of Electric Vehicles (EVs). The goal is to maximize the net present value (NPV) by reducing energy losses and minimizing system interruption costs while ensuring reliability. To achieve this, the paper introduces a hybrid optimization technique called HGP_CS, which combines Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Cuckoo Search optimization methods. By employing various compensation coefficients to assess failure rates and identify strategies for enhancing NPV, the approach aims to optimize the placement of EVCS and capacitors within the EDN. Validation is carried out in IEEE 33-bus and 118-bus systems to demonstrate the performance of the proposed approach. It is found that the proposed approach, compared to the base case, reduced the energy loss cost by 34.19% and 31.57% and also the Expected Interruption Costs (EIC) by 17.26% and 13.41%, for IEEE 33-bus and 118-bus system, respectively. Additionally, the proposed method is economically beneficial as it can attain higher profits. In IEEE 33- bus and 118 bus systems, the annual net profit using the proposed approach is 36,805 $/year and 271,670.72 $/year, respectively. Thus, the proposed technique enhances the overall system performance and significantly boosts profitability, making it a compelling choice for optimizing the placement of charging stations and reactive power sources in networks.

Sizing a Renewable-Based Microgrid to Supply an Electric Vehicle Charging Station: A Design and Modelling Approach

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

Global electric vehicle charging station site evaluation and placement based on large-scale empirical data from Germany

Electromobility is a key technology to decarbonize transportation and thereby avoid the worst impacts of anthropogenic climate change. To power such vehicles when away from their home or depot, public charging infrastructure is required which can be split into enroute and destination charging. We define the latter as charging events that occur while users are busy with other activities. To fulfill this purpose, chargers need to be placed in locations where people spend time. This paper introduces a novel approach to do so based on a neural network trained on several thousand public charging stations in Germany. Within the training sample, the approach is able to predict how much energy was charged per station and day with an R2 of 0.61 for the training set and a RMSE of 13 kWh/day. Using the network, we predict utilization across urban, suburban and industrial areas in Europe and make those predictions available through an easy-to-use web interface. It is further possible to perform predictions and, thereby, extrapolate the learnings from Germany to any country with sufficient OpenStreetMap data. The introduced holistic methodology with its prediction and visualization phase is a first-of-its-kind by applying large-scale measured charging data to the placement problem while being usable in areas which have not yet rolled out electromobility.

Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation

The optimal placement of electric vehicle charging infrastructure (EVCI) is critical for meeting the charging demand of battery electric vehicles (BEVs). However, the questions of how to coordinate the stochastic charging demand and placement of charging infrastructure in an economically effective way and how to solve such a problem on a large real-world scale remain unanswered. This study addresses these questions by developing an agent-based charging station placement model (ACPM) that incorporates demand uncertainty by simulating the behaviors of BEV users. A solution algorithm based on random embedding Bayesian optimization (REMBO) is proposed and shown to significantly improve computational efficiency and solve the ACPM on a large real-world scale. The model and algorithm are applied to a case study of the Atlanta metropolitan area. In the study, the algorithm finds an optimal placement using only 2% of the runtime of existing benchmark methods. This enables the use of the algorithm on a more complex, real-world scale. Results from the case study indicate that the optimal placement of EVCI uses level-2 and direct-current fast charging (DCFC) roughly equally and distributes these relatively evenly throughout the region. This optimal EVCI placement has significant economic benefits because it can improve the expected net present value (NPV) by 50% to 100% relative to common benchmark charging placement practices, such as placing the EVCI uniform randomly or weighted by the number of parked vehicles. Finally, a sensitivity analysis demonstrates that factors such as the size of the BEV market, charging preferences, and charging price are shown to have significant impacts on the optimal placement and profitability of an EVCI project.

Research of the main electromagnetic parameters during the operation of an AC charging station for electric vehicles

Problem. The increasing popularity of electric cars worldwide is also seen in Ukraine, leading to a growing need for more charging stations. Studies show that 80% of electric car charging happens at home. This home charging usually occurs either through the standard AC power grid or through dedicated AC charging stations. This raises concerns about the safety of these charging stations and their potential interference with other electrical and electronic devices nearby. Goal. The goal of this work is to determine the main electromagnetic parameters in the connection cable during the operation of an AC electric vehicle charging station. Methodology. To achieve this goal, it is necessary to study the electromagnetic parameters of the charging station connection cable when AC flows through it and to identify the electromagnetic parameters of the interference that occurs during the operation of the AC charging station. Classical electrophysical methods for calculating electric and magnetic fields and methods for determining the parameters of quadrupoles from the theoretical foundations of electrical engineering are used. Results. The main electromagnetic parameters in the connection cable during the operation of the AC electric vehicle charging station have been identified. Formulas for calculating the current strength and magnetic field strength have been obtained. Originality. New formulas for calculating the electromotive force of interference generated during the operation of the electric vehicle charging station, when AC flows through the charging cable, have been developed. Parameters of capacitive and magnetic coupling between two conductors in a common bundle have been identified. Formulas for determining the current induced by these parasitic connections have been obtained. Practical value. Accurately determining and calculating these parameters allows for the design of a charging station that operates reliably over a long period without causing interference with nearby electrical and electronic systems or devices.

Optimizing solar photovoltaic and biomass integration for electric vehicle charging stations in metropolitan cities: A hybrid approach

AbstractThis paper proposes a hybrid strategy for designing and optimizing a hybrid solar photovoltaic (PV) and biomass‐based electric vehicle charging station (EVCS) in metropolitan cities. The proposed strategy is the joint execution of the dung beetle optimizer (DBO) and Finite Basis Physics‐Informed Neural Networks Technique. It is hence called the DBO‐FBPINNs approach. The proposed strategy aims are to minimize initial cost and operating cost, net present cost, and levelized cost of energy. The design phase involves the energy storage systems, integration of solar PV panels, and biomass generators to warranty a reliable and continuous power supply for the EV charging infrastructure. Feasibility analysis encompasses various technical, economic, and environmental aspects. The converter's control signal is optimized via the DBO method. The FBPINNs model is used to forecast the optimal control parameters of the converter. By then, the proposed DBO‐FBPINNs method is implemented in the MATLAB platform and evaluated their performance with various present strategy's like deep neural network (DNN), fuzzy neural network (FNN), and recurrent neural network (RNN). When compared to other current technologies, the proposed strategy exhibits a low cost of $1.2.

Identifying locations for electric vehicle charging stations in urban areas through the application of multicriteria techniques

The incorporation of electric vehicles (EVs) into urban mobility is increasing daily, driving the need to incorporate urban charging stations (EVCSs) additionally. This study proposes a methodology for identifying adequate EVCS locations within an urban area. This methodology is based on the joint application of multicriteria decision-making methods (MCDMs) to solve urban planning problems related to the placement of EVCSs. As a starting point, the locations of the existing fossil fuel supply stations in each city are used as alternatives. The city of Cuenca, Ecuador, is used as a case study. Criteria and subcriteria have also been determined based on the available information and are used to propose three scenarios based on their value variation, which is established as preponderance weights. Among the results obtained, three alternatives scored better than the rest in most of the evaluations. As there was no conclusive result, a statistical analysis was conducted to strengthen the decision-making process and determine the most favourable potential locations.

Optimal site selection for electric vehicle charging stations: Analysis with hybrid FUCOM and geographic information systems

One of the biggest disadvantages of electric vehicles, which have gained popularity due to reasons such as high energy efficiency and low emissions, is the lack of sufficient charging stations and limited access to suitable charging options. For this reason, in the study, the criteria and their weights that are effective in determining the location of electric vehicle charging stations were determined using FUCOM, which reduces the number of pairwise comparisons, allows less subjective data to be used during the comparison, and provides reliable results because it checks the consistency with the mathematical model. Then, the determined criteria were used in the overlap analysis in GBS to select the most suitable sites that can be used in daily life. The data obtained were compared using the hybrid Best-Worst Method (BWM)-GIS and the Analytical Hierarchy Process (AHP)-GIS. All three hybrid methods determined that the southeast part of the European side and the southwest part of the Anatolian side were the most suitable areas. Therefore, it was seen that the hybrid FUCOM-GIS method should be preferred due to its advantages. In addition, the proposed method was compared with the current EVCS data and the suitability of the method was emphasized.

Feasibility Analysis of an Electric Vehicle Charging Station with Solar Energy and Battery Storage

Feasibility Analysis of an Electric Vehicle Charging Station with Solar Energy and Battery Storage

Energy management in integrated energy system with electric vehicles as mobile energy storage: An approach using bi-level deep reinforcement learning

The integrated energy system with electric vehicle charging station via vehicle-to-grid aims to offer a proactive solution for low-carbon development of both energy and transportation sectors. However, achieving optimal energy efficiency with minimal operational costs in such a complex system is challenging due to the high randomness of electric vehicle travel patterns. This work proposes a reinforcement learning-based energy management framework to optimize the coordinated scheduling of integrated energy and vehicle-to-grid. The problem is modeled as a Markov Decision Process and optimized using the bi-level soft actor-critic algorithm, where the upper-level agent supervises the scheduling of the integrated energy system, and lower-level agent sets the electric vehicles’ charging and discharging strategies based on upper-level decision. Numerical simulations demonstrated that by adopting a bi-level reinforcement learning approach, the proposed algorithm effectively enhances energy exchange between integrated energy and electric vehicle charging station, reducing operational costs by 8 % compared to other multi-agent algorithms. Furthermore, when compared to the Mixed-Integer Linear Programming model, the proposed algorithm achieves similar optimality with significantly less computation time, maintaining a margin of error of approximately 4 %. Additionally, integrating electric vehicles as mobile energy storage within this framework can lead to a further 10 % reduction in operating costs.

High-power three-phase interleaved parallel NPC structure DC-DC converter modeling and simulation

Abstract In fields such as DC microgrids, renewable energy generation, and electric vehicle charging stations, three-phase NPC structure DC-DC converters play an irreplaceable role, constituting a significant part of modern power electronics technology. Mathematical models are established based on various operating modes of IGBT switching devices under PWM control, encompassing input voltage, output voltage, phase currents, average current, and duty cycle. Simulation models are constructed on the MATLAB platform to confirm the correctness of these mathematical models. Closed-loop control strategies are implemented using proportional-integral (PI) controllers. This control strategy serves to confirm the precision of the mathematical models.

Optimizing EV charging stations: a simulation-based approach to performance and grid integration

This study addresses the optimization of electric vehicle (EV) charging stations, focusing on enhancing performance and grid integration through a comprehensive simulation approach. By employing advanced simulation tools in Simulink® and MATLAB®, alongside electrical installation planning with SIMARIS®, we meticulously analyze the charging process, infrastructure requirements, and their implications on the power grid. Our results demonstrate significant improvements in charging station efficiency and reliability, highlighting the effectiveness of our proposed control strategies and harmonic mitigation techniques. Notably, the integration of renewable energy sources emerges as a pivotal factor in reducing operational costs and carbon emissions, furthering the sustainability of EV charging solutions. The research delineates the environmental benefits, emphasizing the reduction of greenhouse gas emissions and enhancement of urban air quality, pivotal in the global shift towards cleaner transportation modes. This work contributes valuable insights into the design and grid integration of EV charging stations, offering a scalable model for future infrastructure development. It serves as a critical resource for engineers, policymakers, and stakeholders in the realm of electric mobility, advocating for a strategic transition to EVs supported by robust and efficient charging infrastructure.

Research on the optimal scheduling strategy of electric vehicle charging and switching station based on an artificial bee colony algorithm

Research on the optimal scheduling strategy of electric vehicle charging and switching station based on an artificial bee colony algorithm

Prediction and analysis of relative error in electric vehicle charging stations based on an improved ConvFormer model

In order to address the issue of metering inaccuracies in charging stations that directly affect the development of electric vehicles, a prediction method for the relative error of charging stations based on the ConvFormer model is proposed. The model combines Convolutional Neural Networks (CNN) with Transformer models in parallel, significantly improving the prediction accuracy. First, charging station data is preprocessed using forward interpolation and normalization methods, and the dataset is transformed into a dataset of input relative errors. Then, a neural network with an improved unidirectional convolutional and attention combination for time-series forecasting is constructed, and common regression performance evaluation metrics, MAE (Mean Absolute Error) and MSE (Mean Squared Error), are selected for evaluation. Finally, based on seven days of charging station data, the relative error of charging stations for the next 24 h is predicted, and compared to traditional Transformer and LSTM (Long Short-Term Memory) time-series models. The results show that the improved model yields the lowest values for both MAE and MSE, with a 47.30 % reduction in MAE compared to the Transformer model and a 38.06 % reduction compared to LSTM, and a 66.94 % reduction in MSE compared to the Transformer model and approximately 62.32 % reduction compared to LSTM.

The Design Concept of Integrated Pedestrian Sidewalk with Solar Plants as a Form of Green Campus Development at the Bali Land Transportation Polytechnic

Abstract An important aspect of creating a viable campus environment is providing safe and comfortable walking paths. Proper walking paths give some advantages for students, including improved physical and mental health, transportation efficiency, and increased Student satisfaction and happiness. The pedestrian sidewalk area at Bali Land Transportation Polytechnic (Poltrada Bali) covers 3800 m2, experiencing high pedestrian activity but lacking protective roofing to enhance pedestrian comfort. Based on the pedestrian sidewalk area, a solar power plant as support for the green canopy construction combined with shading vines is possible. The generated electrical energy serves as the power source for the Public Electric Vehicle Charging Stations (PEVCS) in the campus parking lot as a support for the electric vehicle acceleration program in Bali province. In addition to PEVCS, energy extraction surplus is used as alternative energy for street lighting facilities in the campus area. In this study, the design concept of a shaded pedestrian sidewalk has been carried out, with multiple benefits as solar plants using Delphi as a variable identification and validation tool, and SketchUp software to visualize design shapes aesthetically. The results show that a pedestrian comfort level of 94% was obtained and the maximum power generation of 934,800 Wp, thus it was able to meet the 887,832 W campus operational power reserve needs, street and garden lights of 9,728 W, and the power requirements of two PEVCS units of 22,000 W with a surplus of 15,240 W electric power.

A Solar-Wind based EV Charging Station Model using MATLAB Simulink

Abstract Transportation is becoming more and more necessary every day. One such form of transportation that has been around for a century is the electric vehicle (EV). It has less of an effect on pollution, climate change, and resource depletion because it requires less resources. Many people find it challenging to charge their electric cars in off-grid locations, which lowers the uptake of EVs. In this scenario, a charging station powered by locally available renewable energy sources like solar, wind, biogas, and tidal energy would be a better option. In this paper an attempt has been made to examine a charging station model from renewable energy sources Solar and Wind. The solar-wind energy-based charging system drastically cuts down on the amount of fossil fuels used to generate electricity, which also minimizes emissions of CO2 and carbon-related pollutants. Wind and solar energy are used in conjunction and are complementary to one another. A charging controller which takes into account State of Charge (SOC), the number of linked EVs, and the arrival time of those EVs is used. The power transfer will be managed by this controller. By carefully considering each level, including the solar plant, wind plant, battery storage system, and controlling technology, the charging station is modeled in the best possible way. Simple model is used to describe each stage of the charging station, and designed with appropriate mathematical formulae. By understanding the demand for charging and pairing it with the appropriate energy storage system and auxiliary devices, this charging model can be used in off-grid locations.

Comparative harmonic elimination techniques for supraharmonic reduction in microgrid

In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems with electric vehicle (EV) charging stations, this research compares several harmonic elimination approaches. The increasing deployment of EVs has led to the integration of EV charging stations within MG systems, presents challenges in maintaining a high power quality (PQ). Voltage distortions and SH emissions are caused due to non-linear loads and the intermittent nature of EV charging, which have an effect on the performance and dependability of the MG. In order to solve these problems, multilevel converters (MLCs) are used to produce high-quality waveforms. MLCs use harmonic elimination methods to cut down on SH emissions, which improves the PQ overall. Sinusoidal pulse width modulation (PWM), selective harmonic elimination (SHE), space vector modulation (SVM), and random-PWM (RPWM) techniques are among the harmonic elimination methods compared and analyzed. The results will enable the selection of the most appropriate strategy for minimizing voltage distortion and SH emission in MG systems, while providing valuable insights into the effectiveness of each method.