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The rising demand for electrical energy, coupled with the running down of conventional energy sources, has prompted vast research into renewable energy sources (RES) for power generation. Wind and solar photovoltaic energies are among the most critical resources, despite their susceptibility to fluctuating climatic conditions. Power electronics have been helpful in the integration of renewable energy sources (RESs) with grid, and multilevel inverters (MLIs) have arisen as critical components for the effective utilization of energy from RES and the enhancement of power quality. This paper provides an extensive overview of the latest developments in MLI topologies and how they have been applied in different industries. Optimal MLI designs for various applications will be identified by a thorough investigation of performance parameters including component count, total harmonic distortion (THD), and overall efficiency. Additionally, the paper explores the role of MLIs in diverse sectors including RES, microgrids, and residential applications. By synthesizing insights from recent literature and performance evaluations, this review aims to provide valuable guidance for researchers and practitioners in selecting the most suitable MLI topology for specific applications.

Integrating renewable energy resources into the existing generation capacity can handle the additional load resulting from the EV charging system, even when the majority of the EVs are charged during off-peak hours. This paper comprehensively analyzes EV charging methods with a particular focus on both grid-based direct charging and the utilization of renewable energy sources. However, the most practical approach for EV charging is through large-scale grid-based renewable energy stations. This study outlines the significant challenges facing the application of EV chargers. Overcoming these challenges is vital for the widespread adoption of new technologies such as Wireless power transfer charging systems in the EV sector. Wireless charging technology still faces several issues, such as energy loss during transmission, alignment between the coils, and quick and safe power delivery. One of the significant challenges facing wireless chargers is the need to optimize efficiency while ensuring convenient and reliable charging as outlined in this study. Different wireless charger configurations are presented for stationary EV charging systems by incorporating photovoltaic systems into charging stations and supervisory bases. The application of series-series wireless charger system as an emerging technology for EV chargers is analyzed and simulated as a recommended solution for EV wireless chargers. The system overall efficiency is approximately 98%. The charging system can operate reliably by mitigating the effects of load and grid disturbances.

The transportation sector is one among the key sources of greenhouse gas emissions (GHGs) leading to climate change and global warming. Energy transition through electrified transportation is one of the solutions to tackle the issues. Electric vehicles (EVs) offer significant environmental and economic advantages against the conventional Internal Combustion Engine (ICE) vehicles. EVs are called mobility loads and their connectivity to the utility grid for charging is unpredictable. The large penetration of such unpredictable loads into the utility grid will lead to undesirable impacts on the utility service. This paper highlights the importance of managing and optimizing the charging schedules. The optimization of EV charging has diverse aspects, and the perspectives of EV charging differ among consumers, aggregators, and utility services. Proper planning and management of EV charging is essential to achieve harmony amongst these stakeholders. A comprehensive review on the objectives of electric vehicle charging optimization from various perspectives is presented and discussed in this paper. EV charging optimization techniques including mathematical programming, meta heuristics algorithms and machine learning techniques are explored. The main objectives, constraints, strength, and limitations of different charging optimization techniques are analyzed in detail. A brief discussion on the communication strategies for data exchange in EV charging framework is presented and the need for a communication security constrained EV charging scheduling is also emphasized.

Electric Vehicles (EVs) are crucial in reducing greenhouse gas emissions and combating climate change. However, their adoption is hindered by range anxiety, high costs, and inadequate charging infrastructure. Battery Management Systems (BMS) are essential for EV efficiency, but current systems face limitations such as restricted computational resources and non-updatable software. Cloud computing offers a promising solution by providing enhanced computational power and storage. This paper reviews recent research on cloud-based BMS frameworks and applications, in addition to available industry solutions. The review is coupled with a review of the recent cloud developments, combining both reviews to reach a list of research gaps and suggested future work. The findings emphasize the need for improved online learning applications, connectivity, and security. Future work should focus on integrating recent cloud developments to enhance BMS functionality and reliability.

Electric vehicles (EVs) wireless charging systems have several different coil types, and each coil type may not be compatible with the others, leading to interoperability issues. Additionally, misalignment may occur between the transmitter and receiver during parking. The interoperability and misalignment tolerance problems of wireless charging for EVs need to be addressed. To solve these problems, a novel asymmetric bipolar coil structure with interoperability and misalignment tolerance is proposed. In this paper, four alternative structures with interoperability and misalignment tolerance characteristics are firstly proposed based on existing coupling structures, and an optimal coupling structure is finally selected through finite element analysis (FEA) of these four structures. An experimental prototype was also fabricated to validate this proposal. The effectiveness of the proposed asymmetric bipolar coil is verified through calculations, simulations, and experiments. The final overall transmission efficiencies obtained are all higher than 85%, regardless of the unipolar, bipolar, and quadrupolar coil structures were the transmitting coils.

The growth in energy consumption and pollution has led to increase the utilization of clean energy resources and electric vehicles (EVs), making the issue of network uncertainty a crucial concern. A multi-energy system offers an optimal approach to enhance the reliability, flexibility, and efficiency of an energy dispatch system through utilizing diverse energy generation resources. In this study, a new design for an optimal load distribution system, considering cost factors and CO2 reduction, is discussed. The model includes different energy generation players, such as combined heat and power units (CHP), gas boilers, water pumps, heat storage units, hydrogen storage systems (HSS), photovoltaic arrays (PV), and wind turbines (WT), to create a broad model for analysis and examination. The model takes into account the random charging consumption of EVs and uncertainties in renewable energy generation enabling a comprehensive assessment and analysis over future energy price uncertainties. Using demand response (DR) program, electrical and thermal systems are modeled and employed, and the effects of the water pump and HSS are discussed. An improved version of particle swarm optimization (PSO) is also used to address the optimization problem. The obtained results validate the performance of the proposed approach by showcasing a synchronized charge/discharge mode for EVs, while simultaneously minimizing the total cost.

In recent years, electric vehicles (EVs) have attracted a lot of attention and are rapidly growing trends today due to their ability to compete with and overtake fossil fuel-powered vehicles among all electrified transportation tools. The primary focus of EV evolution is on improving the driving range and optimizing energy consumption, both of which are essential demands achieved through improved and developing motor performance. The brushless direct current (BLDC) motor is one of the most superior choices for dynamic applications in EVs, providing the highest power density and capability of any motor. BLDCs are gaining popularity in EVs not only for their high performance in speed and position controls but also for meeting the requirements of smooth torque maintenance with torque ripple mitigation. This paper provides an overview of various advanced control strategies, such as field-oriented control (FOC), direct torque control (DTC), intelligent control (IC), controlling input voltage (CIV), current shaping techniques (CST), model predictive control (MPC), and sliding mode control (SMC). These strategies are used to regulate the torque ripples produced in BLDC motors, aiming to achieve energy-efficient EV performance for various applications. Additionally, it describes a cloud-based control system coupled with electric motor drives to address challenges in predicting current vehicle conditions for improved power distribution between the motor and battery systems. Furthermore, recent concerns and difficulties in advancing torque ripple mitigation control strategies are highlighted, with comparisons and discussions for future EV research. Finally, an evaluative study on BLDC motor drive in EVs with different control strategies reveals its significance and potential outcomes.

According to the United States environmental protection agency (EPA), every burned gallon of gasoline generates 8.87 Kg of CO2. The pollution created by vehicles’ fuel consumption has been one of the primary sources of environmental contamination that can lead to more climate changes and global warming. Thus, science and technology have converged on the idea that reducing fuel consumption benefits the environment and human health. One of the ideas for reducing fuel usage is deploying hybrid electric vehicles (HEVs) and electric vehicles (EVs) using renewable energy as alternatives to gasoline. One of the main issues with EV batteries is that over operational time the battery health degrades and ultimately becomes unsafe to use. It is crucial that safety issues be addressed by researchers and battery manufacturers. Assessing and predicting battery health has been a high-priority research topic to attempt to mitigate the danger introduced by EV batteries. Although various techniques have been developed to estimate and predict the battery’s state of health (SOH), they do not cover all degradation scenarios that may affect the battery’s lifetime. In addition, the models used in estimating and predicting the battery’s lifetime need to be improved to provide a more accurate battery health state and guarantee battery safety while in use by an EV. Even though all types of EV batteries face similar issues, this paper focuses on Li-ion EV batteries. The main objectives of this paper are 1) to present various Li-ion battery models that are used to mimic battery dynamic behaviors, 2) to discuss the degradation factors that cause the battery lifespan to be degraded, and to become unsafe, 3) to provide a review of the estimation and prediction techniques used for Li-ion battery SOH and remaining useful life (RUL) estimation along with a discussion of their advantages and limitations, and 4) to provide recommendations for improving Li-ion battery lifetime estimation. This paper represents a concise source of information for battery community researchers to help expedite beneficial and practical outcomes to improve EV battery safety.

Car exhaust is one of the most common causes of ozone hole aggravation, electrical vehicles (EVs) represent a promising solution to avoid this problem. Despite the benefits of EVs, their random charging behavior causes some difficulties regarding the electric network performance, such as increased energy losses and voltage deviations. This paper aims to achieve the proper scheduling of the EVs charging process, avoid its negative impacts on the network, and satisfy the EVs users’ requirements. The EVs charging process is formulated as an optimization problem and solved using particle swarm optimization. The optimization problem formulation considers the EV arrival and departure times and the state of charge required by the user. Different strategies such as separated, accumulated, and ranked strategies with continuous or interrupted fixed charging have been applied to solve the uncoordinated EVs charging problem. These strategies are extensively tested on the modified IEEE 31 bus system (499-node network), using the combination of both Open DSS and MATLAB m-files. The simulation results confirm the effectiveness of the proposed accumulated ranked strategy with interrupted fixed charging in improving the overall power system performance. The achieved improvements include minimizing: the peak power consumed, the peak power losses, and the voltage drop. Moreover, the cost of the EVs charging in most of the feeders has been decreased to a satisfying value. A comparison between the proposed strategy and some previously reported strategies has been performed to ensure the technical and economic enhancement of the proposed strategy.

A suitable energy management scheme and integrating renewable energy resources (RERs) can significantly increase energy efficiency and the stability of future grids operation. This work modeled a household energy management comprising a microgrid (MG) system and demand response programs (DRPs). Residential loads with price-based tariffs are introduced to reduce peak load demands and energy costs. For incorporating the uncertainties in RERs, their stochastic nature is modeled with a probabilistic method. This paper proposes a joint optimization approach for the optimal planning and operation of grid-connected residential, rural MG integrated into renewable energy and electric vehicles (EVs) in view of DRPs. The investigation focuses on energy saving of residential homes under different DRPs and RERs integration. The EVs are integrated into MG by including photovoltaic (PV), wind turbine (WT), fuel cell (FC), and diesel engines (DEs). A multi-objective optimization problem has been formulated to minimize the operating cost, pollutant treatment cost, and carbon emissions cost defined as C1, C2, and C3, respectively. The load demand has been rescheduled because of three DRPs, i.e., critical peak pricing (CPP), real-time electricity pricing (RTEP), and time of use (TOU). Further, the EV load has also been analyzed in autonomous and coordinated charging strategies. Using a judgement matrix, the proposed multi-objective problem is transformed into a single-objective problem. The results of an artificial bee colony (ABC) algorithm are compared with the particle swarm optimization (PSO) algorithm. The simulation analysis was accomplished by employing ABC and PSO in MATLAB. The mathematical model of MG was implemented, and the effects of DRPs based MG were investigated under different numbers of EVs and load data to reduce different costs. To analyze the impact of DRPs, the residential, rural MG is implemented for 50 homes with a peak load of 5 kW each and EV load with 80 EVs and 700 EVs, respectively. The simulation results with different test cases are formulated while analyzing the tradeoff between ABC and PSO algorithms. The simulation analysis shows that multiple DRPs, EVs, and RERs offered a substantial trade-off.

A novel control approach based on Linear Parameter Varying (LPV) is designed for Electric Vehicles (EVs) to address the crucial challenge of torque derating. A high fidelity Interior Permanent Magnet Synchronous Motor (IPMSM) based EV is modelled by taking into account the influence of temperature. A closed loop torque compensation control architecture with LPV based Field Oriented Control (FOC) has been designed to mitigate the thermal effects. In order to curtail deteriorating of FOC due to parameter variation, a robust LPV observer has been proposed to estimate the degraded Permanent Magnet (PM) flux linkage and instantaneous torque production caused by uncertainties in stator resistance arises due to thermal effects. A LPV gain scheduling robust current controller for inner control loop of FOC has also been designed which ensures internal stability. The LPV observer-controller gains are computed by solving Linear Matrix Inequalities (LMIs) using convex optimization and singular value decomposition. The experiments simulations have been performed for EV operation against the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) class 3, New European Driving Cycle (NEDC) and Federal Test Procedure (FTP) driving cycles considering thermal effects in Matlab/Simulink. The efficacy of proposed control architecture for managing the derating torque of IPMSM based EV is demonstrated through the validity of results and detailed data analysis. The LPV based FOC is compared with conventional FOC control counterpart. The comparative experiments simulation results show that the overall Mean Square Error (MSE) value is exponentially reduced which establish superiority of the proposed control scheme.

This work presents a battery-ultracapacitor hybrid energy storage system (HESS) for pulsed loads (PL) in which ultracapacitors (UCs) run the pulse portion of the load while the battery powers the constant part of the load. Energy stored in UC depends upon the square of its voltage that’s why an active parallel hybrid topology with two bidirectional converters (BDC) is employed here that assigns UC a separate BDC to control its voltage between an upper and lower limit as per-requisite. The other converter is connected between the battery and DC-link to provide a distinct control of the battery. A MATLAB/Simulink model is developed to minimize the factors affecting the battery life such as capacity rate (C-rate), depth of discharge (DoD), and temperature rise due to PL by controlling the operating modes of the BDCs in which voltage of the UC and state of charge (SoC) of the battery are control variables. A constant output voltage across the load is maintained through feedback control. A detailed comparative analysis, among battery-alone, UC-alone, and battery-UC hybrid modes is performed which signifies that the proposed system is 55% lower in cost than its counterparts. The analysis shows that the proposed HESS reduces 50% capacity of a lead-acid battery, which is otherwise necessary to withstand the pulsating loads. Moreover, the performance of the HESS is also tested for electric vehicle (EV) load by hybridizing a high-voltage lithium-ion battery pack with a UC bank which confirms its suitability for EV applications.

Uncontrolled electric vehicle (EV) charging loads at a workplace could result in hefty demand charges. Several studies in the literature have focused on mitigating these demand charges at workplaces using aggregator-based centralized control and consensus-based distributed control. Though each model has successfully mitigated the demand charges they all show some limitations. Aggregator-based centralized solver is computation and time intensive while consensus-based distributed control is communication intensive. Other works with algorithms exploring demand charge mitigation also often rely on large amounts communications. These algorithms include water filling algorithms, multiobjective optimization, and a fuzzy logic controller. The machine learning-based (ML) approach investigated in this study seeks to eliminate both drawbacks using the model trained on optimization results obtained with existing methods. The investigation of ML-based controller in this study is not only found to be superior in performance due to speed and accuracy metrics compared to existing control approaches but has also provided the promising resilient approach to deal with communication failure and cybersecurity threats.

Recently, electric vehicles (EVs) have proven remarkable impacts on reducing pollution and their suitability for utilizing renewable energy sources (RESs) instead of fossil fuels. The continuously increasing number of installed EVs has encouraged their employment for performing additional functionalities in utility grids. This paper provides study for the impacts of EV integration approaches on enhancing the operation of utility grids. The proposed study considers the targeted renewable energy powered utility grids with installed energy storage devices. Wind energy has been utilized as a case study for the RES with considering its stochastic characteristics and its continuous fluctuations with wind speed. Additionally, the superconducting magnetic energy storage system (SMES) device is chosen due to its promising long-life operation. The various aspects of point of common coupling (PCC) voltage fluctuations, reactive power support, leveling active power of the on-peak period, and power losses are considered in the proposed study. Moreover, fuzzy logic controller (FLC) systems have been proposed for controlling EVs and the SMES device. The energy management between the installed EVs, SMES, wind, and utility grid side is achieved through the coordination of the FLC systems. The results show that reduced peak and mean values of total power losses and line active power are obtained by the proposed coordination method. Moreover, improved profiles of the PCC voltage, reactive power support, active power leveling, and sustainable and reliable power supply are obtained by the proposed method for wide operating ranges and modes of operation.

Several issues have been risen due to the recent vast installations of renewable energy sources (RESs) instead of fossil fuel sources in addition to the replacement of electric vehicles (EVs) for fuel-powered vehicles. Mitigating frequency deviations and tie-line power fluctuations has become driving challenge for the control design of interconnected power systems. RESs represent continuously varying power generators due to their nature and dependency on the environmental conditions. In this context, this article presents a new modified hybrid fractional order controller for load frequency and EVs control in interconnected power systems. The new controller combines the benefits of two widely employed fractional order controllers, including the FOPID and TID controllers. In addition, a new practical application of recent artificial ecosystem optimization (AEO) method has been proposed in this article for determining simultaneously the optimum controller parameters. The proposed controller and optimization method are validated on two areas interconnected power system with different types of RESs and with considering the natural characteristics of sources, EVs and load variations. Obtained simulation results verify the superior performance of the proposed controller and optimization method for achieving high mitigation of frequency fluctuations and tie-line power deviations, increased robustness, enhanced system stability over a wide range of parameters uncertainty and fast response during transients.

Due to the enormous increase in the interconnection of electric vehicles (EVs) into utility power grids, more and more challenges are arising in modern power systems. To handle these challenges, many attempts have been proposed to achieve the satisfactory performance of EV charging/discharging, the improvement of utility power grids operation, and an adaptive coordination strategy for the EV–grid connection. Energy storage systems (ESSs) may play a vital role in this process by injecting/absorbing their energy into/from the grid, including EVs. This paper proposes an adaptive coordination method based on fuzzy logic control (FLC) for EVs and superconducting magnetic energy storage (SMES) to enhance the charge and discharge processes of EVs during off and on peak loading periods. FLC is utilized to organize the charging/discharging process for both EVs and SMES. The state of charge (SoC) of SMES and EVs is considered as the input for EVs’ FLC besides the electricity price (EP) signal. To verify the effectiveness of the proposed coordination method, uncertain numbers of EVs, the rate of EP, and daily residential load profile are included in this study. The results demonstrate that the proposed coordination method can improve the performance of EV charging/discharging. Moreover, the cooperative operation with SMES and the reliability improvement of the utility power are preserved using the proposed coordination strategy.

Energy transition from traditional generation sources into new renewable energy generation has become essential for facing climate changes. However, increased penetration levels of renewable energy sources (RESs) make power systems subjected to low inertia problems. Moreover, the continuously growing numbers of electric vehicles (EVs) have made the substantial need for their contribution in power systems. Therefore, this paper proposes a combined fractional-order controller using the parallel combination of tilt-integral-derivative with filter (TIDF) and hybrid fractional-order (HybFO) controllers for robust frequency regulation in interconnected power systems. The proposed controller is advantageous in combining the merits of two fractional-order controllers that result in more robust and effective load frequency control (LFC) at wide range and different types of disturbances. Furthermore, a new application of marine predator optimization algorithm (MPA) is proposed for simultaneously determining the optimum controller parameters in the different power system areas. The existing EVs contribute in performing additional functionality in power systems. EVs help in damping out the frequency and tie-line power oscillations in the proposed work. The two-area interconnected power system is selected as a case study with the installed photovoltaic (PV), and wind generations in addition to distributed EVs among areas. The obtained results show the superiority and suitability of the proposed controller over the traditional controllers in the literature. Additionally, the effectiveness of the MPA is validated and compared with recent meta-heuristic optimization algorithms.

As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. One of the key challenges, however, is the strain on power grid infrastructure that comes with large-scale EV deployment. The solution to this lies in utilization of smart scheduling algorithms to manage the growing public charging demand. Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. Researchers have focused on using historical charging data for predictions of behavior such as departure time and energy needs. However, variables such as weather, traffic, and nearby events, which have been neglected to a large extent, can perhaps add meaningful representations, and provide better predictions. Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. The best predictive performance is achieved by an ensemble learning model, with SMAPE scores of 9.9% and 11.6% for session duration and energy consumptions, respectively, which improves upon the existing works in the literature. In both predictions, we demonstrate a significant improvement compared to previous work on the same dataset and we highlight the importance of traffic and weather information for charging behavior predictions.

Owing to spatiotemporal flexibility of electrical vehicles (EVs), EVs will introduce new synergies between the urban traffic system (UTS) and power distribution system (PDS). As a mobile charging load, arranged EV charging can not only provide more flexibility for power system optimization, but also influence the traveling time and other mobility-related characteristics of EVs. However, there are few studies that consider the impact of the coupling facility (such as parking lot with charging facilities) on UTS reliability, as well as the reliability of UTS and PDS (referred to as PTS). Therefore, it is necessary to extend the original research scope to incorporate the traffic system simulation into the collaborative analysis of the coordinated PTS. This paper first develops a new simulation framework for the coordinated UTS and PDS coupled with commercial charging lots (CLs). Then, a new method is illustrated for measuring the reliability of a coordinated UTS and PDS. At last, a novel unified PTS reliability index considering both UTS and PDS reliability indices are presented.

The interest in electric-powered transportation has been increased because of problems such as depletion of fossil fuel resources and environmental issues caused by internal combustion engines. The market for various kinds of small E-mobility devices (EMDs), such as electric vehicles (EVs), E-bikes, segways, and mini-boards has been growing rapidly. Of these, the distribution of the small electric vehicles called personal mobility devices (PMDs), which can address problems such as traffic issues and environmental pollution, is expected to gain momentum. However, unlike the widely used charging stations for EVs, charging systems for PMDs are insufficient. The large size of dedicated PMD charging adapters makes them inconvenient to transport. Therefore, to make PMDs more attractive, it is necessary to deploy charging stations that can charge all types of PMDs. The present study proposes a new charging system that can respond to the needs of PMDs having various voltages and implement low charging current ripple. The proposed multi-level charging system can share power by subdividing the power supply to handle the charging requirements of various PMDs. In order to verify the validity of the proposed charging system, we performed simulations and conducted experiments by building a prototype charging system.

Electrification of the transportation sector can play a vital role in reshaping smart cities. With an increasing number of electric vehicles (EVs) on the road, deployment of well-planned and efficient charging infrastructure is highly desirable. Unlike level 1 and level 2 charging stations, level 3 chargers are super-fast in charging EVs. However, their installation at every possible site is not techno-economically justifiable because level 3 chargers may cause violation of critical system parameters due to their high power consumption. In this paper, we demonstrate an optimized combination of all three types of EV chargers for efficiently managing the EV load while minimizing installation cost, losses, and distribution transformer loading. Effects of photovoltaic (PV) generation are also incorporated in the analysis. Due to the uncertain nature of vehicle users, EV load is modeled as a stochastic process. Particle swarm optimization (PSO) is used to solve the constrained nonlinear stochastic problem. MATLAB and OpenDSS are used to simulate the model. The proposed idea is validated on the real distribution system of the National University of Sciences and Technology (NUST) Pakistan. Results show that an optimized combination of chargers placed at judicious locations can greatly reduce cost from $3.55 million to $1.99 million, daily losses from 787kWh to 286kWh and distribution transformer congestion from 58% to 22% when compared to scenario of optimized placement of level 3 chargers for 20% penetration level in commercial feeders. In residential feeder, these statistics are improved from $2.52 to $0.81 million, from 2167kWh to 398kWh and from 106% to 14%, respectively. It is also realized that the integration of PV improves voltage profile and reduces the negative impact of EV load. Our optimization model can work for commercial areas such as offices, university campuses, and industries as well as residential colonies.

Lithium-ion batteries(LIBs) have been used in electric vehicles(EVs) because of its high energy density and no pollution. As one of the important parameters of battery management system(BMS), accurately estimating the state-of-charge (SOC) can ensure the energy distribution and safe use of the battery. Therefore, in order to obtain accurate SOC estimation, this paper improves the estimation accuracy of SOC from four aspects. Firstly, to overcome the dependence of the model on the internal parameters of the battery, this paper uses the least squares support vector machine (LSSVM) to establish the battery model. The current, voltage, temperature are used as input vectors to estimate the SOC. Besides, the parameters of LSSVM are determined by a grey wolf optimizer(GWO). The GWO can improve the ability of LSSVM model by finding the global optimal solution. Thirdly, in order to improve the estimation accuracy of SOC, a novel LSSVM model based on the sliding window is proposed. The SOC estimated at the previous time, together with voltage, current and temperature measured at the current time are selected as the input vectors by sliding window method to improve the SOC accuracy. Finally, the effectiveness of the proposed model is verified under different driving conditions at different temperatures by comparing with other estimators. The comparison results indicate that the SOC estimation error(MAE) can be controlled within 1%, the root mean square error (RMSE) decreases from 0.89% to 0.22%, which are verified the effectiveness and robustness of the model.

The proposed work focuses on the power enhancement of grid-connected solar photovoltaic and wind energy (PV-WE) system integrated with an energy storage system (ESS) and electric vehicles (EVs). The research works available in the literature emphasize only on PV, PV-ESS, WE, and WE-ESS. The enhancement techniques such as Unified Power Flow Controller (UPFC), Generalized UPFC (GUPFC), and Static Var Compensator (SVC) and Artificial Intelligence (AI)-based techniques including Fuzzy Logic Controller (FLC)-UPFC, and Unified Power Quality Conditioner (UPQC)-FLC have been perceived in the existing literature for power enhancement. Further, the EVs are emerging as an integral domain of the power grid but because of the uncertainties and limitations involved in renewable energy sources (RESs) and ESS, the EVs preference towards the RES is shifted away. Therefore, it is required to focus on improving the power quality of the PV-WE-ESS-EV system connected with the grid, which is yet to be explored and validated with the available technique for enhancing power quality. Furthermore, in the case of the bidirectional power flow from vehicle-to-grid (V2G) and grid-to-vehicle (G2V), optimal controlling is crucial for which an electric vehicle aggregator (EVA) is designed. The designed EVA is proposed for the PV-WE-ESS-EV system so as to obtain the benefits such as uninterruptible power supply, effective the load demand satisfaction, and efficient utilization of the electrical power. The power flow from source to load and from one source to another source is controlled with the support of FLC. The FLC decides the economic utilization of power during peak load and off-peak load. The reduced power quality at the load side is observed as a result of varying loads in the random fashion and this issue is sorted out by using UPQC in this proposed study. From the results, it can be observed that the maximum power is achieved in the case of PV and WE systems with the help of the FLC-based maximum power point tracking (MPPT) technique. Furthermore, the artificial neural network (ANN)-based technique is utilized for the development of the MPPT algorithm which in turn is employed for the validation of the proposed technique. The outputs of both the techniques are compared to select the best-performing technique. A key observation from the results and analysis indicates that the power output from FLC-based MPPT is better than that of ANN-based MPPT. Thus, the proper and economical utilization of power is achieved with the help of FLC and UPQC. It can be inferred that the EVs can play a vital role in imparting the flexibility in terms of power consumption and grid stabilization during peak load and off-peak load durations provided that the proper control techniques and grid integration are well-established.

This article investigates the effects of high penetration levels of Electric Vehicle (EV) charging on power distribution transformers and proposes a new solution to minimize its negative impacts. There has been growing concern over Greenhouse Gas (GHG) emissions within the transportation sector, which accounts for about 23% of total energy-related carbon-dioxide emissions. The main solution to this problem is the electrification of vehicles. However, large scale integration of EVs into existing grid systems poses some challenges. One major challenge is the accelerated aging of expensive grid assets such as transformers. In this article, a demand response mechanism based on the thermal loading of transformers, is proposed. The proposed solution is modeled as an optimization problem, where a new time of use (ToU) tariff is used to shift the EV load considering the thermal loading of transformers, thereby minimizing their accelerated aging. The simulation results show that the accelerated aging of transformers can be reduced without augmenting the existing grid.

This paper presents the comparative studies of two permanent magnet (PM) motors, which are modular-stator outer-rotor flux-switching permanent-magnet (MSOR-FSPM) motor and conventional outer-rotor flux-switching permanent-magnet (COR-FSPM) motor. The differences in structure and design principles between the two types are compared. Then, a 2D finite element method (FEM) is used to analyze the basic electromagnetic performances. The magnetic field distribution, no-load back-EMF, cogging torque, and electromagnetic torque are presented and compared. Furthermore, a comprehensive comparison of the motor performances which are important for electric vehicles is investigated. The theoretical analysis and FEM results predict that the MSOR-FSPM motor has superior capability compared to that of the COR-FSPM motor, including fault tolerance capability, and field weakening capability.

The impending environmental issues and growing concerns for global energy crises are driving the need for new opportunities and technologies that can meet significantly higher demand for cleaner and sustainable energy systems. This necessitates the development of transportation and power generation systems. The electrification of the transportation system is a promising approach to green the transportation systems and to reduce the issues of climate change. This paper inspects the present status, latest deployment, and challenging issues in the implementation of Electric vehicles (EVs) infrastructural and charging systems in conjunction with several international standards and charging codes. It further analyzes EVs impacts and prospects in society. A complete assessment of charging systems for EVs with battery charging techniques is explained. Moreover, the beneficial and harmful impacts of EVs are categorized and thoroughly reviewed. Remedial measures for harmful impacts are presented and benefits obtained therefrom are highlighted. Bidirectional charging offers the fundamental feature of vehicle to grid technology. In this paper, the current challenging issues due to the massive deployment of EVs, and upcoming research trends are also presented. It is envisioned that the researchers interested in such areas can find this paper valuable and an informative one-stop source.