Electric Vehicle Charging Research Articles

Regional Analysis and Evaluation Method for Assessing Potential for Installation of Renewable Energy and Electric Vehicles

Many countries are adopting renewable energy (RE) and electric vehicles (EVs) to achieve net-zero emissions by 2050. The indicators of RE and EV potentials are different. Decision-makers want to introduce RE and EVs; however, they need a method to find suitable areas. In addition, this is required in the time-series analysis to provide a detailed resolution. In this study, we conducted a time-series analysis in Japan to evaluate suitable areas for the combined use of RE and EVs. The results showed the surplus RE areas and shortage RE urban areas. The time-series analysis has quantitatively shown that it is not enough to charge EV batteries using surplus RE. Moreover, a ranking methodology was developed for the evaluation based on electric demand and vehicle numbers. This enables the government’s prioritization of prefectures and the prefectures’ prioritization of municipalities according to their policies.

Discovering Electric Vehicle Charging Locations Based on Clustering Techniques Applied to Vehicular Mobility Datasets

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today’s roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city’s vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome.

Renewable sources-based efficient electric vehicle charging system through power forecasting and charge scheduling

Renewable sources-based efficient electric vehicle charging system through power forecasting and charge scheduling

Adaptive energy management with machine learning in hybrid PV-wind systems for electric vehicle charging stations

Adaptive energy management with machine learning in hybrid PV-wind systems for electric vehicle charging stations

Novel Droop-Based Techniques for Dynamic Performance Improvement in a Linear Active Disturbance Rejection Controlled-Dual Active Bridge for Fast Battery Charging of Electric Vehicles

Electric vehicles (EVs) are rapidly replacing fossil-fuel-powered vehicles, creating a need for a fast-charging infrastructure that is crucial for their widespread adoption. This research addresses this challenge by improving the control of dual active bridge converters, a popular choice for high-power EV charging stations. A critical issue in EV battery charging is the smooth transition between charging stages (constant current and constant voltage) which can disrupt converter performance. This work proposes a novel feedforward control method using a combination of droop-based techniques combined with a sophisticated linear active disturbance rejection control system applied to a single-phase shift-modulated dual active bridge. This combination ensures a seamless transition between charging stages and enhances the robustness of the system against fluctuations in both input voltage and load. Numerical simulations using MATLAB/Simulink R2024a demonstrated that this approach not only enables smooth charging but also reduces the peak input converter current, allowing for the use of lower-rated components in the converter design. This translates to potentially lower costs for building these essential charging stations and faster adoption of EVs.

A robust sliding mode control strategy for DC voltage stabilization in solar powered electric vehicle charging station

ABSTRACT Integrating DC power sources and AC grid in an electric vehicle charging station through converters can introduce oscillations, potentially leading to system instability. This paper explored the utilization of a sliding mode controller in a bidirectional DC-DC buck-boost converter for DC bus voltage control within the charging station. This controller aims to promptly respond to power fluctuations, offering robustness against variations in system parameters, external disturbances, and uncertainties. Control strategies play a critical role in mitigating DC link voltage fluctuations and ensuring power stability. The system entails a photovoltaic array employing maximum power point tracking for optimal energy harnessing. In addition, a bidirectional buck-boost converter is employed to effectively manage battery charging in buck mode and discharging in boost mode. A comparative analysis between Proportional-Integral control and sliding mode control incorporating a washout filter was conducted. This assessment is simulated and validated using MATLAB/Simulink, and the Sim Power System toolbox. The proposed model offers a comprehensive solution for integrating solar power, battery storage, and grid-based charging by enhancing overall system efficiency and reliability.

Universal Soft Switching Dual Active Bridge Converter for Electric Vehicle Charging Infrastructure towards Sustainable Electric Transportation

This paper introduces a modified Dual Active Bridge (DAB) converter integrated with an LLC2 resonant converter tailored for Electric Vehicle (EV) charging infrastructure, emphasizing a unidirectional power flow. The converter implements a two-stage power conversion process, leveraging the DAB’s suitability for high-power applications characterized by enhanced efficiency, low voltage stress, and universal soft switching (ZVS) across all switches. Distinguishing itself from conventional isolated and non-isolated DC-DC converters, the DAB-LLC2 configuration is systematically elucidated in terms of its operating principle, soft switching characteristics, steady-state attributes, output features and crucial design parameters. A meticulous design of a 3kW DAB-LLC2 converter is conducted using MATLAB/Simulink software and the comprehensive results are documented, providing valuable insights into the converter’s performance and applicability for EV charging infrastructure for sustainable electric transportation.

An Optimization Strategy for EV-Integrated Microgrids Considering Peer-to-Peer Transactions

The scale of electric vehicles (EVs) in microgrids is growing prominently. However, the stochasticity of EV charging behavior poses formidable obstacles to exploring their dispatch potential. To solve this issue, an optimization strategy for EV-integrated microgrids considering peer-to-peer (P2P) transactions has been proposed in this paper. This research strategy contributes to the sustainable development of microgrids under large-scale EV integration. Firstly, a novel cooperative operation framework considering P2P transactions is established, in which the impact factors of EV charging are regarded to simulate its stochasticity and the energy trading process of the EV-integrated microgrid participating in P2P transactions is defined. Secondly, cost models for the EV-integrated microgrid are established. Thirdly, a three-stage optimization strategy is proposed to simplify the solving process. It transforms the scheduling problem into three solvable subproblems and restructures them with Lagrangian relaxation. Finally, case studies demonstrate that the proposed strategy optimizes EV load distribution, reduces the overall operational cost of the EV-integrated microgrid, and enhances the economic efficiency of each microgrid participating in P2P transactions.

Adaptive output feedback control of a bidirectional AC/DC totem‐pole PFC converter for wireless charging of electric vehicles

AbstractThis paper deals with the control problem of a single‐phase bidirectional alternating current/direct current (AC/DC) totem‐pole power factor correction (PFC) converter intended to be used in wireless power transfer (WPT) chargers for electric vehicles (EVs). The converter is operating as a bridgeless boost rectifier with the PFC functionality during grid to vehicle (G2V) mode and as a PFC inverter during vehicle to grid (V2G) mode. Firstly, the operating principle is presented. Secondly, an overall mathematical model governing all the operating modes of the converter is elaborated. Thirdly, due to the nonlinearity of the studied system, a nonlinear controller–based Lyapunov technique is designed to achieve the following control objectives: (i) unity power factor (UPF) during both power transfer modes G2V and V2G, (ii) regulation of the DC‐link voltage, and (iii) asymptotic stability of the closed‐loop system. Further, as the developed control law requires the measurement of all voltages and currents, an adaptive observer–based Kalman‐like technique is designed to estimate all the required signals and parameters. The performances of the proposed output feedback control technique are validated through several simulation scenarios showing that all control objectives are achieved. It should be noted that the use of the Kalman observer reduces the complexity of the systems as it requires only two measurements (grid voltage and current) making it an advantageous solution over existing solutions where three measurements are generally used. Furthermore, due to the low‐pass filtering behavior of the observer and its susceptibility to estimate the converter's parameters, the robustness of the controller and its insensitivity to the parameter uncertainties are greatly improved.

An Ultra‐High Voltage AC/DC Isolated Matrix Converter Applied to V2G Electric Vehicle Charging Piles

ABSTRACTIn recent years, in order to alleviate global environmental problems, renewable energy power generation and the electric vehicle industry have been vigorously promoted by many countries. During this background, many scholars have proposed the development of vehicle‐to‐grid (V2G) systems to alleviate the impact of renewable energy generation on grid quality, and electric vehicle charging piles are the core part of realizing V2G systems. This article proposes an ultra‐high voltage AC/DC isolated matrix converter applied to V2G electric vehicle charging piles, which can achieve bidirectional flow of energy, and proposes the control strategy that can reduce the current stress of the device. In addition, the ultra‐high voltage module of the proposed topology can provide wide voltage range and high voltage charging power for different types of electric vehicles. The proposed topology and control strategy are verified by simulation.

The Control Strategies for Charging and Discharging of Electric Vehicles in the Vehicle–Grid Interaction Modes

In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy exchanges between them. The key component for implementing V2G technology is the bidirectional AC/DC converter. This study concentrates on the non-isolated bidirectional AC/DC converter, providing a detailed analysis of its two-stage operation and creating a mathematical model. A dual closed-loop control structure for voltage and current is designed based on nonlinear control theory, along with a constant current charge–discharge control strategy. Furthermore, midpoint potential balance is achieved through zero-sequence voltage injection control, and power signals for the switching devices are generated using Space Vector Pulse Width Modulation (SVPWM) technology. A simulation model of the V2G system is then constructed in MATLAB/Simulink for analysis and validation. The findings demonstrate that the control strategy proposed in this paper improves the system’s robustness, dynamic performance, and resistance to interference, thus reducing the effects of large-scale, uncoordinated electric vehicle charging on the power grid.

Hunter–Prey Optimization Algorithm for Optimal Allocation of PV, DSTATCOM, and EVCS in Radial Distribution Systems

ABSTRACTThis research article instigates a seminal approach for optimizing reactive power in renewable energy sources (RES) and electric vehicles (EVs) coalescing distribution systems, using the innovative Hunter–Prey Optimization (HPO) algorithm in conjunction with DSTATCOM as a reactive power compensator. The proposed methodology aims to minimize losses, enhance voltage stability, and improve overall system performance by simultaneously optimizing reactive power flows in photovoltaic RES (PV_DG), EV charging stations (EVCS), and DSTATCOMs within the distribution system. Simulations carried on IEEE‐33, IEEE‐69, and IEEE‐118 test bus systems in MATLAB environment demonstrate that the HPO‐based approach achieves a 91.47% and 96.61% reduction in real power losses and an improvement in voltage profile with a minimum voltage value of 0.991 and 0.994 p.u. (respectively for IEEE‐33 and 69 bus systems), compared to traditional algorithms. These results highlight the lofty performance of the HPO method, effectively addressing the challenges posed by the integration of RES and EVs along with DSTATCOM.

Dynamic Wireless Charging of Electric Vehicles Using PV Units in Highways

Transitioning from petrol or gas vehicles to electric vehicles (EVs) poses significant challenges in reducing emissions, lowering operational costs, and improving energy storage. Wireless charging EVs offer promising solutions to wired charging limitations such as restricted travel range and lengthy charging times. This paper presents a comprehensive approach to address the challenges of wireless power transfer (WPT) for EVs by optimizing coupling frequency and coil design to enhance efficiency while minimizing electromagnetic interference (EMI) and heat generation. A novel coil design and adaptive hardware are proposed to improve power transfer efficiency (PTE) by defining the optimal magnetic resonant coupling WPT and mitigating coil misalignment, which is considered a significant barrier to the widespread adoption of WPT for EVs. A new methodology for designing and arranging roadside lanes and facilities for dynamic wireless charging (DWC) of EVs is introduced. This includes the optimization of transmitter coils (TCs), receiving coils (RCs), compensation circuits, and high-frequency inverters/converters using the partial differential equation toolbox (pdetool). The integration of wireless charging systems with smart grid technology is explored to enhance energy distribution and reduce peak load issues. The paper proposes a DWC system with multiple segmented transmitters integrated with adaptive renewable photovoltaic (PV) units and a battery system using the utility main grid as a backup. The design process includes the determination of the required PV array capacity, station battery sizing, and inverters/converters to ensure maximum power point tracking (MPPT). To validate the proposed system, it was tested in two scenarios: charging a single EV at different speeds and simultaneously charging two EVs over a 1 km stretch with a 50 kW system, achieving a total range of 500 km. Experimental validation was performed through real-time simulation and hardware tests using an OPAL-RT platform, demonstrating a power transfer efficiency of 90.7%, thus confirming the scalability and feasibility of the system for future EV infrastructure.

Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction.

Analysis and control design for input‐series output‐parallel multi‐channel inductive power transfer system

AbstractTo realize high‐power inductive power transfer (IPT) for fast charging of electric vehicles (EVs), an input‐series output‐parallel (ISOP) multi‐channel IPT system is analysed in this paper, and an output control strategy based on single neuron controller is proposed to improve the stability of the system. Firstly, the steady‐state operating conditions of ISOP‐IPT system is analysed based on different compensation networks which shows that the constant‐voltage‐output compensation networks are more suitable for the proposed circuit structure. Then, to improve the voltage equalization between channels, an open‐loop control method combining parameter design and phase‐shift control is proposed against different coil misalignments. At the same time, based on the single neuron controller, the system output closed‐loop control is realized without the need of accurate system modelling. Finally, a three‐channel ISOP‐IPT experimental system was constructed, which operated stably at 2.9 kW with a power efficiency of 93.38%. The system closed‐loop control with control time of 22 ms is realized. The voltage equalization control offset is less than 1%.

Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops

Electric vehicle (EV) drivers considering long-distance trips still face range anxiety due to the limited range of EVs and the scarcity of charging stations. Thus, it becomes important to ensure the feasibility of the selected route and determine an optimal charging strategy. As a crucial aspect of decision support for EV drivers, this study proposes a mixed integer linear programming (MILP) approach for the EV charging strategy problem (EVCSP), incorporating a piecewise linear approximation technique to address the challenges posed by nonlinear charging times. The proposed optimization model, namely CSPM determines where, when, and how much to charge an EV for a specified route to minimize travel time and cost. The solution time of large-scale test problems and a case study on Türkiye reveal the robustness and reliability of the CSPM. Furthermore, two multi-objective optimization methods (the weighted sum and the lexicographic method) are applied to the case study, and the results are analyzed. The results indicate that the travel cost is more sensitive to the selected charging strategy, with a range of 46.09 % across the applied charging strategies, whereas travel time remains more resilient, with a maximum fluctuation of 19.77 %. A comparative analysis with a full charging strategy reveals that the CSPM reduces the travel time by 60.1 % and improves the cost efficiency by 105.72 %.

Electric vehicle charging station demand prediction model deploying data slotting

Accurate prediction of energy requirement at charging station is essential for optimizing infrastructure usage, ensuring grid stability, and minimizing operational cost. Literatures suggest deployment of machine learning techniques to forecast the station demand. One major challenge associated with development of machine learning models is the inherent uncertainty in electric vehicle charging behaviour that includes variations in charging patterns, user preferences, and vehicle types. The conventional pre-processing techniques fail to dislodge nonlinearities and highly random patterns that include very low or zero-charging. Employing such techniques affects the model's forecast accuracy. This article performs data-slotting during pre-processing stage and then selects the best among 1-h, 2-h, 3-h and 4-h slots, to frame the feature vectors. The 4-h data with minimum variance is suggested to frame the dataset. Four distinct datasets, comprising different combination of average and total demands as predictor and response respectively are considered. The created dataset is deployed in Random Forest, Categorical Boosting, Extreme Gradient Boosting and Light Gradient Boosting models. The article recommends Categorical Boosting Regression model with least mean absolute error, mean square error and root mean square error of 0.0726, 0.0112, and 0.1059 respectively. Furthermore, the use of feature vector comprising of aggregated load for prescribed slots and the response representing the aggregated demand is observed to provide the least prediction error by the suggested model. The suggested model fed by the proposed feature vector offers significant advantage to charging station operator by enhancing the operational efficiency while performing resource and cost management with strategic planning.

Design and analysis of a high-efficiency bi-directional DAB converter for EV charging

The escalating demand for electric vehicles (EVs) arises from apprehensions regarding fossil fuel depletion and the ecological repercussions of conventional combustion engines, propelling the transition towards environmentally sustainable alternatives. EVs are conventionally comprised of a powertrain, battery management system, and communication infrastructure, with the battery playing a pivotal role. Achieving an efficient EV battery charger necessitates the implementation of a proficient charging algorithm and a high-power converter capable of adeptly regulating battery parameters. Among the array of available options, a bi-directional DC/DC converter is often employed for this purpose. This study predominantly focuses on the Bi-Directional Dual Active Bridge Converter with Single-Phase Shift Control. It is renowned for attaining a broad voltage range through transformer turn ratio adjustments. Its controllable parameters, such as phase shift ratio and duty cycle, bolster its versatility. Moreover, due to its input–output isolation and minimal passive elements, this converter exhibits superior efficiency due to its soft-switching capabilities. The analytical framework encompasses steady-state and dynamic assessments, small-signal modeling, and system transfer function analysis, all of which contribute to formulating an optimized controller design. Additionally, the study conducts simulations of various battery charging techniques, including constant current (CC), constant voltage (CV), and CCCV mode, employing a 1.5 kW Dual Active Bridge DAB-based charger through MATLAB/SIMULINK. Furthermore, voltage mode control simulations for a 5 kW DAB converter augment the study's insights into effective EV charging strategies.

Evaluating the accessibility of on-street household electric vehicle charging stations in London: Policy insights from equity analysis across emission zones

Evaluating the accessibility of on-street household electric vehicle charging stations in London: Policy insights from equity analysis across emission zones

Game theoretic operation optimization of photovoltaic storage charging station considering uncertainty and carbon trading

With the advancement of energy conservation and emission reduction efforts, the orderly charging of electric vehicles and the operation of photovoltaic-storage-charging stations associated with electric vehicles have become increasingly important topics. This study constructs an optimization model for the operation of stations under the synergy of electricity and carbon markets from a game theory perspective. Firstly, Latin hypercube sampling and Monte Carlo sampling are employed to handle the uncertainties in photovoltaic output and the stochastic nature of electric vehicles charging. Secondly, a ladder-type carbon trading mechanism is introduced, and a charging optimization model based on Stackelberg game theory is developed to describe the benefit interaction between charging stations and electric vehicles users. In this model, the upper level represents the charging station operator aiming to maximize joint electricity and carbon revenue while minimizing load fluctuations, whereas the lower level represents electric vehicles users aiming to maximize consumer surplus. Finally, a genetic algorithm nested with mixed-integer linear programming is used to solve the optimization model. The simulation results validate the model's effectiveness and superiority. The results indicate that, compared to centralized optimization methods, the Stackelberg game mechanism can increase consumer surplus by 119.40 %, reduce carbon emissions by 217.92 %, and achieve a win-win situation for both parties. Compared to a fixed carbon trading value, the ladder-type carbon trading mechanism can reduce carbon emissions by 23.84 % and smooth load fluctuations.