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Renewable energy‐based electric vehicle (EV) charging systems have become increasingly popular in recent years, particularly in commercial and industrial environments. This study looks at a broad‐spectrum bidirectional buck boost DC to DC converter employing solar photovoltaic (PV) technology. This combination is intended for usage in vehicle to grid (V2G) and grid to vehicle (G2V) modes for electric car applications. In both operating modes, the converter attains a high‐voltage transfer gain ratio (VTGR) without any theoretical output voltage restrictions. It provides evidence on suitable nature for EV applications with versatility, redundancy, and practicality. The theoretical evaluation was verified by the development and testing of a 500 W experimental model. A peak efficiency of 97.8% and 97.4% in V2G and G2V modes is indicated by the data, confirming the usefulness and efficacy of this integrated strategy. These results show that the bidirectional buck boost DC to DC converter, when combined with the suggested integration of PV and EV, improves V2G‐G2V operations in EV charging systems and is both practicable and feasible. [ABSTRACT FROM AUTHOR]

The profitable commercialization and swift growth in electrified transportation requires load management controllers. Effective load management controllers can reduce the peak load on the power grid, balance the load demand and improve the stability of the power grid. Several techniques are available to optimize the load, including battery swapping, vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-home (V2H) approaches. This paper addresses effects of rising adoption of electric vehicles (EVs) and the potential for an increased load on the distribution system, which requires load optimization to ensure sustainable development. This paper offers a comprehensive review of all possible energy management controllers used in EVs for load optimization and power split controllers for splitting power between the battery and super-capacitor in hybrid energy storage systems (HESS) to increase battery life span. These controllers are classified into three categories, fuzzy logic controller, voltage-based controller and soft computing-based controller, with their constraints, advantages and disadvantages and finally sum up with their current challenges and future recommendation. The paper also reveals the wireless charging with their pros and cons. The findings have implications for the design and implementation of controllers for EVs to ensure their sustainable development and reduce fossil fuel usage. This study will be highly helpful to the researchers, industrialist and investors as a ready literature of energy management controllers for EVs, with information of their recommendations and challenges. [ABSTRACT FROM AUTHOR]

The transportation sector is undergoing a profound transformation, shifting from fossil fuel reliance to electric and hybrid semi-electric alternatives. In response, European countries are implementing novel concepts like electrified highways for trucks and buses, bridging the gap between traditional and electric mobility. This paper centers on the management of electric vehicle (EV) charging infrastructure within industrial zones, crucial nodes for charging networks due to their concentrated economic activity and vehicular movement. The study delves into optimal strategies for deploying charging stations in these zones, considering factors such as station placement, capacity planning, and integration with smart grids to ensure efficient and accessible EV charging. Moreover, the research extends its focus to the integration of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies, illustrating their potential within industrial zones. In our research, we have developed algorithms tailored for the infrastructure of industrial zones, focusing on the integration of storage systems and the charging and discharging dynamics of electric vehicles (EVs). Our case study, supported by numerical simulations, illustrates the outcomes of a 24-hour timeframe, where 126 vehicles were charged, and 134 were discharged. The results provide a comprehensive view of how the grid-maintained balance throughout these operations, ensuring that industrial facilities received the required power to fulfill their operational demands. [ABSTRACT FROM AUTHOR]

This paper presents an electric vehicle (EV) switched reluctance motor (SRM) drive with incorporated operation capabilities integrated into the utility grid, the microgrid, and another EV. The motor drive DC-link voltage is established from the battery through an interleaved boost/buck converter with fault tolerance. The varied DC-link voltage can improve driving performance and reduce battery energy consumption over a wide speed range. Through a well-designed current control scheme, speed control scheme, and dynamic commutation tuning scheme, the established SRM drive possesses good performance in the motor driving mode. During deceleration, the regenerative braking energy can be effectively recovered to the battery. When the EV is in idle mode, the grid-to-vehicle (G2V) charging operation can be conducted through the bidirectional switch mode rectifier (SMR) and CLLC resonant converter. Satisfactory charging performance with good line drawn power quality and galvanic isolation is preserved. Conversely, the vehicle-to-grid (V2G) discharging operation can be performed. The EV can make movable energy storage device applications. Finally, the interconnected operations of the developed EV SRM drive to vehicle and microgrid are presented. Through vehicle-to-vehicle (V2V) operation, it can supply energy to the nearby EV when the battery is exhausted and needs roadside assistance. In addition, microgrid-to-vehicle (M2V) and vehicle-to-microgrid (V2M) operations can also be conductible. The EV battery can be charged from the microgrid. Conversely, it can also provide energy support to the microgrid. [ABSTRACT FROM AUTHOR]

Recent advancements in battery technology have made them more economically viable than ever before, making them suitable for various grid-scale applications. Due to their rapid response, batteries are attractive for providing ancillary services (AS), such as frequency regulation and reserve services, to the bulk power grid. On the other hand, an electric vehicle (EV) is viewed as a moving battery; consequently, EVs are also suitable for those services. This research proposes a linear planning model for an Electric Vehicle Aggregator (EVA) within a distribution network (DN) to offer Ancillary Services (AS) to the bulk power grid. The model takes into account contract design by identifying optimal incentives or charging tariffs that Electric Vehicle (EV) owners would be willing to pay the EVA for charging their vehicles. Additionally, the model takes into account the size of the electric vehicle (EV) fleet as a crucial planning factor for EV aggregation that depends on the energy pricing set by the EVA. The proposed model is developed to maximize the overall profit of bidding capacities in the energy and AS markets while supporting the operation of an unbalanced DN by maintaining the DN limits. Simulation results and sensitivity analyses on the model have been carried out to support the investment model and investigate the change in the optimal solution across different case studies. Simulations show that the optimal charging tariff ( $\beta $ ) is ( $0.02~{\$}/kWh$ )when considering scenarios where Distribution Network (DN) limits, such as thermal and voltage constraints, are ignored for all EV participation versus $\beta $ relations. When including the DN, the optimal payoffs vary based on the relationship between the charging tariff and the number of participating EVs.

In recent days the high penetration of EVs have an impact on the load demand and it will be increased further. This results in stress on the grid and proper energy management in the charging stations. Recent development also enables smart communication between EV user and charging station. A bidirectional converter-based charging station works on V2G and G2V modes for charging the EV battery and supports the grid or isolated power station when it is needed. In this paper, a brief discussion on the previous development of bidirectional conversion is presented. A bidirectional converter is modeled and simulated in Simulink. In G2V mode, the time of energy-based tariff is used for the cost calculation, and for V2G mode, the system monitoring of the EV charging parameters is presented where the pricing varies from 11.09 Rs. To 16.27 Rs. For an hour of power consumption using a 2.54 kW charger on a probabilistic model. For V2G operation, the inverter voltage and grid voltage in synchronization is presented. An IoT-based system is initiated in Simulink using thing speak. This put the data in the cloud and further the data is monitored and fetched from the cloud using a web application.

Este artigo apresenta a análise e operação de um conversor CA-CC trifásico, bidirecional e isolado em alta frequência, de único estágio com correção do fator de potência, destinado para aplicação como carregador embarcado de baterias de veículos elétricos. Este conversor pode operar nos modos Grid-to-Vehicle (G2V) e Vehicle-to-Grid (V2G). No lado primário tem-se um conversor boost trifásico intercalado (interleaving) com modulação PWM senoidal para conexão com a rede elétrica, e na saída um barramento CC com ponto médio. O lado secundário é composto por uma topologia em ponte completa com modulação PWM convencional de razão cíclica de 50%. A transferência de potência do lado primário ao secundário é realizada por meio da técnica de defasamento angular (phase-shift), a mesma utilizada em conversores Dual-Active Bridge (DAB). Devido a defasagem natural do sistema trifásico, é possível obter transferência de potência constante, de reduzida ondulação, por meio do controle do defasamento angular também constante. Os princípios de operação do conversor são apresentados e demonstrados por análises qualitativas e quantitativas. Resultados experimentais de um protótipo de potência de 3 kW, tensão de entrada da rede elétrica de 127 V/60 Hz, tensão de saída de 300 V e frequência de comutação de 50 kHz são apresentados. Em conformidade com as análises, o protótipo doconversor apresentou fator de potência de 0,997, potência transferida entre primário e secundário constante e de baixa ondulação e dinâmica satisfatória e rápida durante ensaios de inversão do fluxo de potência.

Energy management in Micro Grids (MG) has become increasingly difficult as stochastic Renewable Energy Sources (RES) and Electric Vehicles (EV) have become more prevalent. Even more challenging is autonomous MG operation with RES since prompt frequency control is required. We provide an innovative Energy Management Strategy (EMS) for MG with grid support in this academic publication. By integrating RES and EV storage, we seek to decrease reliance on the grid. The EMS consists of three execution phases: Ranking for EV Recommendation (RER), Optimal Power Allocation (OPA) for Fleet, and EV Storage Allocation (OAES). The aim of slicing the time in to smaller in intervals is to update the energy and power scheduling in shorter intervals as per the changes are going on in the system. The period of 24 h is divided into 96 intervals (t) and storage requirements (kWh/t) are estimated based on the estimated load and RES together with the necessary storage volume. We employ three approaches that are frequently used for communication channel power allocation optimization to accomplish OAES. With two objectives: minimum network power loss plus voltage fluctuations, the Multi-Objective Optimization Problem (MOOP) is solved for each 't' based on OAES to provide the Optimal Power Flow (OPF). The Pareto-front is used to calculate the best amount of power from each fleet in each 't'. The data received from the fuzzy rule base is used in the third stage to train an intelligent Convolutional Neural Network (CNN), which has rank of EV as an output and four decision variables as inputs. The main goals in this stage are to minimize battery degradation and to make the most of it for MG support. With the aid of a MATLAB-based simulation setup and heterogeneous entities, the primary goal of EMS is examined and put into practice in an On-grid MG.

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners' disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid. [ABSTRACT FROM AUTHOR]

The storage capacity of Electric Vehicles (EVs) can be used to improve energy management in low-voltage distribution networks. The research throughout the previous decade has concentrated on several control strategies for grid auxiliary assistance via Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V). This article focuses on energy management in distribution networks using EVs, solar photovoltaic (PV), and diesel generators (DG). To begin with, Water Filling Algorithm (WFA) is utilized to disperse EV storage optimally in each zone of energy needed for load flattening. The goal here is to decrease grid reliance while minimizing overall energy costs. After that, a win–win plan is formed between the EV owner and the grid operator to lower EV charging costs and increase EV storage usage for grid support. A multi-Objective Optimization problem is formulated with two objectives: load flattening and voltage regulation. This is solved by Multi-Objective Genetic Algorithm (MOGA) where the decision variable is EV power transaction, Optimal Power Transaction (OPT). ANFIS-based EV ranking technique has been designed to achieve these two aims concurrently. The influence of Optimal Energy Distribution (OED) has been studied in different scenarios. ANFIS prioritization is investigated in several scenarios, as well as the impact on overall EV power availability and Cost of Charging (CoC). [ABSTRACT FROM AUTHOR]

An adaptable infrastructure for dynamic power control (AIDPC) of battery chargers for electric vehicles has been proposed in this work. The battery power is dynamically adjusted by utilizing flexible active load management when the vehicle is plugged in. The battery charging and discharging prototype model is developed for storing the surplus power during the off-peak period and delivering the power during the shortfall of the grid. The development and adoption of two charging strategies, namely the constant current (CC) charging and the proposed dynamic constant current (DCC) charging, has been elaborated. When the Grid to Vehicle (G2V) operation mode is active in the CC charging method, the batteries are charged from the power grid with a constant amplitude sinusoidal current and unity power factor. In the proposed method of DCC, the batteries are charged from the grid with a decreased amplitude in sinusoidal current and unity power factor, and the battery charges from the dynamic dc link voltage. The battery charging and discharging depends upon the rate of change of dc link voltage according to the load variation of the grid. The simulation and experimental results support the rapid time response of the (AIDPC) load profile of the electrical system experiences a new peak due to the CC charging method. On the other hand, the vehicle scheduling algorithm successfully draws less power from the grid when the DCC charging method is used. [ABSTRACT FROM AUTHOR]

Widespread recharging of electric vehicle (EV) batteries could lead to frequent overloads, excessive power loss, and severe voltage fluctuations, especially at the distribution-system level. These challenges can be mitigated with smart charging initiatives, in which the system operator regulates EV charging with certain technical or economic objectives, provided that the EV owners are prepared to relinquish the charging control of their vehicles. Amidst concerns regarding the potential hike in electricity bills due to domestic EV charging, cost-minimizing objectives have been identified as compelling motivation for EV owners to participate in centralized charging programs. This paper presents a dynamic strategy for smart charging that can account for the uncertainties associated with vehicle mobility. The charging scheme aims to minimize energy costs with respect to a real-time pricing tariff while fulfilling the charge requirement of all EV users. The benefits of smart charging under both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes are analyzed. Furthermore, the impact of smart charging on the distribution system is assessed in terms of system demand, distribution efficiency, and load voltage. Results indicate that the proposed technique can reduce the consumers' energy bill by roughly one third. Although the smart V2G method leads to maximum saving, the inclusion of battery degradation cost tips the balance in smart G2V's favor. Moreover, the pair of smart charging solutions improves distribution-system operation, with all the monitored metrics of power distribution showing significant improvement. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) industry is quickly growing in the present scenario, and will have more demand in the future. A sharp increase in the sales of EVs by 160% in 2021 represents 26% of new sales in the worldwide automotive market. EVs are deemed to be the transportation of the future, as they offer significant cost savings and reduce carbon emissions. However, their interactions with the power grid, charging stations, and households require new communication and control techniques. EVs show unprecedented behavior during vehicle battery charging, and sending the charge from the vehicle's battery back to the grid via a charging station during peak hours has an impact on the grid operation. Balancing the load during peak hours, i.e., managing the energy between the grid and vehicle, requires efficient communication protocols, standards, and computational technologies that are essential for improving the performance, efficiency, and security of vehicle-to-vehicle, vehicle-to-grid (V2G), and grid-to-vehicle (G2V) communication. Machine learning and deep learning technologies are being used to manage EV-charging station interactions, estimate the charging behavior, and to use EVs in the load balancing and stability control of smart grids. Internet of Things (IoT) technology can be used for managing EV charging stations and monitoring EV batteries. Recently, much work has been presented in the EV communication and control domain. In order to categorize these efforts in a meaningful manner and highlight their contributions to advancing EV migration, a thorough survey is required. This paper presents existing literature on emerging protocols, standards, communication technologies, and computational technologies for EVs. Frameworks, standards, architectures, and protocols proposed by various authors are discussed in the paper to serve the need of various researchers for implementing the applications in the EV domain. Security plays a vital role in EV authentication and billing activities. Hackers may exploit the hardware, such as sensors and other electronic systems and software of the EV, for various malicious activities. Various authors proposed standards and protocols for mitigating cyber-attacks on security aspects in the complex EV ecosystem. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are experiencing substantial investment and widespread acceptance. However, successful penetration of the global market is contingent upon the development of a strategic plan for the efficient allocation of EVs to optimal charging stations (CSs). This study combines several optimization models to systematically assign EVs to the optimal charging stations, with the goal of maximizing trading energy while simultaneously minimizing total response time. Factors taken into consideration include traveling distance, charging (V2G), and discharging (G2V) energy trading, total response time, and energy prices. The efficacy of the combined models is validated using GAMS and BARON solvers, with a focus on EV satisfaction factor, updated energy and response time, number of served EVs, and alleviation of range anxiety. The proposed models demonstrate 85% satisfaction factor for the majority of charging requests, reaching almost 99% for discharging requests. These results surpass those of contemporary models, underscoring the heightened effectiveness of the proposed approach. [Display omitted] • Optimal EV allocation balances energy trading and response time. • Comprehensive model considers mobility, V2G, G2V, response, and prices. • GAMS simulations show consistently high EV satisfaction levels. • Superior outcomes compared to COP and EVaaS models in satisfaction. • Future work targets an emergency model for EVs with low energy levels. [ABSTRACT FROM AUTHOR]

Energy management in Micro Grids (MG) has become increasingly difficult as stochastic Renewable Energy Sources (RES) and Electric Vehicles (EV) have become more prevalent. Even more challenging is autonomous MG operation with RES since prompt frequency control is required. We provide an innovative Energy Management Strategy (EMS) for MG with grid support in this academic publication. By integrating RES and EV storage, we seek to decrease reliance on the grid. The EMS consists of three execution phases: Ranking for EV Recommendation (RER), Optimal Power Allocation (OPA) for Fleet, and EV Storage Allocation (OAES). The aim of slicing the time in to smaller in intervals is to update the energy and power scheduling in shorter intervals as per the changes are going on in the system. The period of 24 h is divided into 96 intervals (t) and storage requirements (kWh/t) are estimated based on the estimated load and RES together with the necessary storage volume. We employ three approaches that are frequently used for communication channel power allocation optimization to accomplish OAES. With two objectives: minimum network power loss plus voltage fluctuations, the Multi-Objective Optimization Problem (MOOP) is solved for each 't' based on OAES to provide the Optimal Power Flow (OPF). The Pareto-front is used to calculate the best amount of power from each fleet in each 't'. The data received from the fuzzy rule base is used in the third stage to train an intelligent Convolutional Neural Network (CNN), which has rank of EV as an output and four decision variables as inputs. The main goals in this stage are to minimize battery degradation and to make the most of it for MG support. With the aid of a MATLAB-based simulation setup and heterogeneous entities, the primary goal of EMS is examined and put into practice in an On-grid MG. • In this scholarly article present a pioneering Energy Management Strategy (EMS) for MG with grid support. • To reduce grid dependency by utilizing RES and EV storage. The EMS comprises three stages of execution: Optimal Allocation EV Storage (OAES), Optimal Power Allocation (OPA) for Fleet, and Ranking for EV Recommendation (RER) • The key objectives in this stage are: min, the battery's degradation, and maximize its utilization for MG support. [ABSTRACT FROM AUTHOR]

• Charging infrastructure sharing concept is introduced for electric vehicles (EV). • A methodology is proposed to assess the optimal cost and savings for EV deployment. • The best combination of heavy duty and light duty EV deployment is recommended. • Heavy duty EV is more economical with fixed travel schedules to save energy cost. • Net metering reduces the payback time. The proliferation of electric vehicles (EVs) all around the world offers both challenges and opportunities to build a sustainable city and transportation system. Bidirectional charging capabilities at workplace charging facilities (e.g., as part of a microgrid) have made the overall economic optimization more attractive on one hand, but also more complex on the other hand. This paper investigates the cost optimization problem for bidirectional charging at a workplace microgrid connected to two different buildings to determine the optimal framework for a combination of both heavy-duty and light-duty electric vehicles (HDEV and LDEV). A deep learning-based model has been developed to forecast the 15-minute solar generation and building power consumption. Real-time travel profile data has been used to represent the temporal uncertainty of electric vehicle charging. The cost optimization problem is formulated as a Mixed Integer Programming (MIP) model which also addresses battery life degradation. Furthermore, a comprehensive economic analysis has been carried out to analyze the payback period, peak reduction, and cost savings for two different buildings at the same workplace with both on-board and off-board charger configurations. It has been found that HDEV is a better cost-effective solution in comparison to LDEV in terms of energy cost reduction and payback periods. Net metering capability leads to higher energy savings and peak reductions in most cases. [ABSTRACT FROM AUTHOR]