EV Charging Research Articles

Hybrid Charging Station for Authentic Electric Vehicle

Electric vehicles powered by batteries are becoming increasingly popular around the world. Several causes are driving this trend, including the need to reduce air and noise pollution and reliance on fossil fuels. To have a better understanding of how batteries behave in various situations. It is vital to be aware of certain battery performance factors in certain circumstances. A battery management system includes a battery fuel gauge, an optimal charging algorithm, and circuitry for cell and thermal balance. It estimates essential states and parameters of the battery system, such as battery impedance, battery capacity, state of charge, state of health, power decline, and remaining useful life, using three non-invasive measures from the battery: voltage, current, and temperature. This paper reviews several papers published regarding EV charging types, methods, BMS, state of charge

Electric Vehicle Charging Infrastructure, Standards, Types, and Its Impact on Grid: A Review

The growth of EV penetration brings numerous benefits in economic and environmental aspects, but it also presents deployment opportunities and challenges of EV charging stations. The EV owners benefit from lower fuel and operating expenses compared to ICE vehicles because of higher efficiency of electric motors reaching it as high as 60–70%. The electric vehicles are intermittent load to the grid since the number of users charging the electric vehicle at different charging station at different time. Moreover, the increasing EV penetration leads to the increase in load requirement on charging stations and will place a heavier load on the grid, necessitating the exploration of alternative resources. So, it significantly effects on power quality of the distribution grid. This charging requirement needs to be effectively managed to ensure uninterrupted energy supply for charging EVs batteries. By employing a basic charging plan, the estimated system cost per vehicle per year in Denmark is $263. Implementation of smart charging, the system cost decreases to $36 per vehicle per year, resulting in substantial savings of $227 per vehicle per year. Controlled charging methods also effectively reduce system costs by 50% and decrease peak demand. An EV fleet has the potential for cost savings in the power system, amounting to $200–$300 per year per vehicle. The aim of the review is to address the impact on power quality of the distribution grid and study the nature of EV unbalanced loads in order to minimize impact on grid efficiently by managing the resources.

Smart Management of EV Charging Stations

The shift towards Electric Vehicles (EVs) is driven by the urgent need to address environmental concerns and reduce our reliance on fossil fuels. Our project, a comprehensive Electric Vehicle Station Management System (EVSMS), aims to contribute to this transition. This survey explores the design and implementation of our EVSMS, highlighting its role in promoting the adoption of EVs over traditional fuel-powered vehicles. Our EVSMS offers features such as user registration, owner dashboards, and an admin panel, making it user-friendly for both EV owners and station operators. Through integration with the Google Maps API, users can easily locate nearby charging stations, but what makes our system stand out is its real-time station occupancy information. This feature not only aids users in finding available charging slots but also prompts them to consider alternative stations when their preferred choices are occupied. In addition to enhancing convenience, our system supports slot booking with initial payment, streamlining the charging process. Furthermore, it keeps users informed through notifications about station availability and promotions, reinforcing the advantages of EV usage. Our project emphasizes the significance of EVs as a sustainable alternative to traditional fuel-powered vehicles. By offering a user- friendly, technologically advanced EVSMS, we contribute to the broader goal of reducing carbon emissions and mitigating environmental impact, thereby making EVs a more attractive choice. This survey provides insights into our system's design and its alignment with the global transition towards sustainable, eco-friendly mobility. Key Words: Electric Vehicles (EVs), Electric Vehicle Station Management System (EVSMS), Slot Booking, Map, chatbot.

EV Charging Infrastructure Discovery to Contextualize Its Deployment Security

EV Charging Infrastructure Discovery to Contextualize Its Deployment Security

Intelligent electric vehicle charging optimization and horse herd-inspired power generation for enhanced energy management

This article focuses on optimizing electric vehicle charging in distribution networks, emphasizing technical and economic considerations. Unlike traditional methods, the proposed intelligent approach tailors each EV's charging based on specific daily trip energy requirements. Vehicle owners provide trip data to the charge management system, enabling precise charging calculations considering factors such as energy tariffs, distribution network limits, and charging levels. The paper introduces the horse herd optimization algorithm, inspired by horse herd behavior, offering advantages like reduced computational time and improved convergence in maximizing power generation, especially under shading conditions. The comparative analysis of smart EV charging under normal and fast conditions, considering various constraints and load response programs, demonstrates the proposed method's effectiveness. Numerical results reveal a 46.02 % average load reduction and a 20.53 % peak load decrease with a Load Response Program. Charging costs are optimized, with Case 2 exhibiting a 2.61 % cost reduction compared to Case 3. The study delves into charging frequencies, discharge frequencies, total unsupplied energy, and unloaded energy for each case, providing crucial insights into algorithmic performance. The horse herd optimization algorithm-based approach proves superior, offering a promising solution for efficient and cost-effective electric vehicle charging in distribution networks. Furthermore, graphical representations illustrate the algorithm's impact on charging power, energy allocation, power passing through distribution posts, and megavolt-ampere flow through network lines. These numerical and graphical analyses provide a comprehensive understanding of the horse herd optimization algorithm capabilities, emphasizing its potential to optimize power distribution, reduce costs, and enhance the resilience of residential distribution networks.

Improved Energy Management System for Wind Power-Based Microgrid with EV Charge Station

This study aims to peak power shaving and reduce the cost of energy by using improved energy management system (EMS) in a microgrid. This study has three scenarios. In the first scenario, the EV charging station and the wind turbine operate standalone. In the second scenario, a microgrid structure including an energy storage system (ESS) and an EMS is established. The threshold algorithm has been used for the energy management. In the last scenario, fuzzy logic-based EMS is used for energy management. The impact of EV charge station and wind turbine on the grid and the cost of energy are analyzed for three scenarios. The results show that the use of microgrid-connected ESS significantly reduces the cost of energy and creates a more balanced load for the grid side. It is also proved that the EMS affects the energy losses. It is revealed that when the proposed fuzzy logic-based EMS is used, the energy cost is reduced by 78% and the peak power is reduced by 70%. Thus, it is seen that the proposed EMS successfully reduces both the cost of energy and the negative impact of EVs on the grid by balancing grid side load.

Bayesian inference-based spatiotemporal modeling with interim activities for EV charging etiquette

Poor charging etiquette of Plug-in Electric vehicle (PEV) drivers, such as unplugging other PEVs and overstaying after the PEV is fully charged, will create a service bottleneck to charging resources and even impede PEV penetration. To explore the underlying linkage between PEV drivers’ interim activities and the behavior of overstaying, this study introduces an innovative framework that implements Geographically and Temporally Weighted Regression (GTWR) with a dedicated activity-based Bayesian inference module. Specifically, the stochasticity of PEV drivers’ travel behaviors is well addressed in the Bayesian inference module for travel choice modeling during charging sessions. Subsequently, the GTWR model is constructed based on predicted travel choices and expected durations of activities to capture the spatiotemporal interconnections between overstaying and activity characteristics. The entire modeling framework is further applied to a case study in Salt Lake City, Utah, and demonstrates superior adaptability in reasoning the impacts of spatiotemporal factors without survey data.

Rural Photovoltaic Storage and Charging Integrated Charging Station Capacity Allocation Strategy based on Tariff Compensation Mechanism

Background: In order to help the carbon peaking and carbon neutrality goals, the current new energy vehicle to the countryside policy for the local use of renewable energy and demand-side carbon reduction provides a good opportunity but also requires rural townships and villages of electric vehicle charging infrastructure planning ahead. However, due to the current low rural electric vehicle ownership, the charging price compensation mechanism is not yet perfect, resulting in the planning period of electric vehicle growth and the willingness to respond to the tariff compensation policy is difficult to accurately assess. Methods: This paper proposes a rural photovoltaic storage and charging integrated charging station capacity allocation strategy based on the tariff compensation mechanism. Firstly, we construct a spatial-temporal dynamic distribution model of rural EV charging load coupled with distribution network-transportation network, and on this basis, we consider the rural EV charging time-sharing tariff and tariff compensation policy incentive, and amend the EV charging load transfer model; and then we construct an optimization planning model of charging station with the goal of minimizing the cost of construction, operation and maintenance, and maximizing the charging benefit of the integrated charging station in the rural area, and obtain the optimal synergistic planning scheme under the tariff compensation mechanism in the planning period. Results: The optimal collaborative planning scheme under the electricity price compensation mechanism is obtained, and the correctness and validity of the proposed optimal planning method of the rural optical storage charging station under the electricity price compensation mechanism is verified by the example, which is of positive significance in the promotion of the charging facilities to go to the countryside in an appropriate manner, and in the stimulation of the willingness of the rural consumers in the townships to purchase vehicles. Conclusion: (1) The spatial and temporal distribution and transfer model of rural EV charging loads with price incentives is constructed by taking into account the preferential charging price policies such as rural time-sharing tariff and tariff compensation mechanism. By comparing the operating revenues of optical storage-charging integrated charging stations with and without timesharing tariffs and tariff compensation policies, we verified the incentive effect of multiple types of price incentives for the over-planning of rural electric vehicle charging facilities. (2) The proposed optimal configuration method of rural photovoltaic, storage and charging integration charging station can realize the in-situ utilization of rural renewable energy, tap the price competitiveness of photovoltaic, storage and charging integration, and weaken the cost of electricity consumption. By comparing the optimized configuration scheme with and without joint planning, it is verified that the moderate configuration of in-situ photovoltaic and energy storage equipment on the basis of the planning of charging piles brings benefits far exceeding the investment cost, and has a great role in increasing the operational efficiency of rural charging facilities.

ANN based smart sensing of short-term voltage stability assessment of modern power system

This study addresses the critical challenge of Short-term Voltage Stability (STVS) in the contemporary electric grid system, characterized by diverse static loads, induction motors, electronic loads, electric vehicle charging stations, and renewable energy sources. High-power sensing elements are identified for power system stability and transient measurements, and the existing system is explored for fault measurement and stability maintenance. The analysis employs an IEEE 14-bus dynamic model with two distinct load configurations. In the first configuration, the static load at bus 14 is replaced with a composite load, encompassing static loads, induction motors, electronic loads, and EV charging stations. For the second novel configuration at bus 14, the static load is substituted with a composite load and a solar photovoltaic (PV) generator operating in reactive power compensation mode. This addition of the PV generator compensates for elevated reactive power demand in the 14-bus system. The study conducts voltage stability analysis in two phases, evaluating the voltage profile and transmission line load ability in both scenarios during the first phase. In the second phase, STVS is simulated by briefly opening the circuit breaker in bus 2. The results reveal notable improvements in the voltage profile of all buses, a 6 % enhancement in power factor at bus 14 in the dynamic model with the novel configuration, and a 3 % increase in the loading factor of the IEEE Bus 14 system. The dynamic model incorporating a PV generator and composite load in bus 14 demonstrates superior STVS performance compared to the setup with only a composite load. The abstract concludes by highlighting the development of real-time voltage profile assessment using Artificial Neural Network (ANN) topology, leveraging results from the Continuous Power Flow (CPF) analysis of the novel model with a PV generator and composite load in the far end bus. This restructuring ensures a logical and clear organization, encompassing an introduction to the problem, a description of methods employed, presentation of results, and a succinct conclusion summarizing the key findings.

A REVIEW ON CHARGING STATION LOCATOR AND SOLUTION FOR EV AND CNG VEHICLE

A REVIEW ON CHARGING STATION LOCATOR AND SOLUTION FOR EV AND CNG VEHICLE

Energy profiles and electricity flexibility potential in apartment buildings with electric vehicles – A Norwegian case study

Energy flexibility in buildings has the potential to reduce the grid burden of neighbourhoods, yet its practical implementation remains limited. This paper presents a data-based case study from Norway, examining the electricity flexibility potential of electric vehicles, within the context of apartment building loads and PV generation. The results highlight the significant electricity flexibility potential in apartment buildings with EVs, where EV charging can be shifted in time by means of a shared energy management system. Energy profiles are presented, showing how EV charging can increase the average electricity use in apartments by a factor of 1.5 and the power use by a factor of 3.5 to 8.6. Furthermore, the study demonstrates how electricity flexibility KPIs of optimised EV charging in apartment buildings are affected by different energy tariffs, PV generation, V2G technology, and the location of the billing meters. The simulated scenarios showed a maximum reduction of peak loads of 45 %, while a maximum of 38 % of the EV charging was covered by PV generation. The study confirms that residential EV charging emerges as a viable frontrunner in the practical realization of end-user flexibility, paving the way for effective solutions in real-life applications.

Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations

Recent reports indicate that electric vehicle charging stations (EVCSs) are susceptible to remote exploitation through their vulnerable software/cyber components. More importantly, compromised EVCS can be leveraged to perform coordinated oscillatory load attacks against the interconnected power grid, leading to power grid instability, increased operational costs, and power line tripping. In this paper, an edge-based approach for the detection and localization of coordinated oscillatory load attacks initiated by exploited EV charging stations against the power grid is investigated. The edge-based detection relies on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. The proposed detection approach was evaluated by building a real-time test bed to synthesize benign and malicious data, which was generated by analyzing real-life EV charging data collected during recent years. The results demonstrate the effectiveness of the implemented approach with the Convolutional Long-Short Term Memory model producing optimal classification accuracy (99.4%). Moreover, this analysis results shed light on the impact of such detection mechanisms towards building resiliency into different levels of the EV charging ecosystem while allowing power grid operators to localize attacks and take further mitigation measures. Specifically, the detection mechanism of oscillatory load attacks is decentralized and created an effective alternative for operator-centric mechanisms to mitigate multi-operator and Man-in-the-Middle (MitM) oscillatory load attacks against the power grid. Additionally, the created test bed is leveraged to evaluate a distributed mitigation technique, which can be deployed on public/private charging stations to average out the impact of oscillatory load attacks while allowing the power system to recover smoothly within 1 second with minimal overhead. Finally, a security metric is createdto evaluate the resiliency of the ecosystem based on its design and architecture.

Frequency Design of Three-phase Active Front-End Converter with Reduced Filter in EV Chargers

Frequency Design of Three-phase Active Front-End Converter with Reduced Filter in EV Chargers

Grid Secondary Frequency Control by Fuzzy PI–PD Controller

This paper develops a new approach for controlling the frequency in a two-area power system using a fuzzy PI PD controller. Frequency stabilization is one of the important factors for any power system to operate efficiently and reliably. The fluctuating characteristics of RES, like solar PV and wind energy, due to changing atmospheric conditions, require additional reserves to compensate for fluctuations in generated power. With growing application of EVs and their charging requirement, charting huge loads may add to the power grid and lead to stability problems if not properly regulated. This paper coordinates EV charging into the secondary frequency regulation, aided by an enhanced fuzzy controller designed for dealing with fluctuating imbalances between electricity demand and generation. In this context, a control technique is suggested that incorporates the flexibility of fuzzy logic with the accurate PI and PD methods for control. The controller continuously monitors changes in frequency at each node in the power system network. The fuzzy logic works on uncertain input data in terms of prescribed linguistic variables and rules that allow it to achieve a reasoned output. The difference between the desired and actual frequencies alters the control variable through the PI component of the controller, while the PD component considers the rate of change of this error to make the system's response time better. The effectiveness of the fuzzy PI PD controller is proven through detailed simulation experiments under different operational conditions and disturbances. It can be rundown that the controller efficiently reduces these frequency deviations, which guarantees smooth and reliable operation of the two-area power system network. In addition, it can be seen that the controller has robust performance but is rather reactive to dynamical changes in system parameters. Case studies are included in the research to test the performance of the proposed control approach in reducing frequency deviations; simulation findings always prove its efficacy. This paper therefore presents a complex method of frequency regulation in power systems using fuzzy logic and combined PI PD control to deal with problems caused by RES integration and EV charging. The proposed technique enhances the frequency stability in order to enhance both the reliability and efficiency of power grid operations due to the ever-changing energy demands and conditions.

Optimizing Radial Distribution System With Distributed Generation and EV Charging: A Spotted Hyena Approach

Optimizing Radial Distribution System With Distributed Generation and EV Charging: A Spotted Hyena Approach

Physicochemical properties as a function of lomefloxacin biological activity.

Aim: The goal is to discover QSAR of Lomefloxacin as antibacterial activity. Materials and Methods: A number of lomefloxacins analogs activities were studied by program Windows Chem SW. The analogues were obtained and energy minimization was carried out through Molecular Modeling Program, the calculations were performed using General Atomic and Molecular Electronic Structure System (GAMESS) software. Results: There were six descriptions (N-quinoline more (-) ev charge, Kinetic Energy, Potential Energy, Log p, Log S, F6 charge) results have highly compatible of physicochemical properties with lomefloxacin analogs activities. It can be used to estimate the activities depending on QSAR equation of lomefloxacin analogs. Conclusions: The parameters used for calculation were depending on the quantum chemical was employed in deriving from computational study of properties and can used to predict the activities of certain analogs of Lomefloxacins as antibacterial compounds.

A method for suppressing leakage current of non-isolated integrated on-board EV battery charger

A method for suppressing leakage current of non-isolated integrated on-board EV battery charger

Non-Isolated Bidirectional Three-Phase Three-Level Wide-Output-Voltage-Range Voltage DC-Link EV Charger

Non-Isolated Bidirectional Three-Phase Three-Level Wide-Output-Voltage-Range Voltage DC-Link EV Charger

A Modified hy-DPSM for Magnetizing Inductance Inspired Modified DAB for Wide Category of EV Charger Design with Flat Efficiency Profile

A Modified hy-DPSM for Magnetizing Inductance Inspired Modified DAB for Wide Category of EV Charger Design with Flat Efficiency Profile

Data-Driven Switching Control Technique based on Deep Reinforcement Learning for Packed E-Cell as Smart EV Charger

Data-Driven Switching Control Technique based on Deep Reinforcement Learning for Packed E-Cell as Smart EV Charger