Recharging Of Electric Vehicles Research Articles

Assessing levelized cost of electric vehicle recharging in China

Assessing levelized cost of electric vehicle recharging in China

Energetic and Environmental Benefits of Residential Solar Microgeneration Added to Electric Vehicle Recharging in the City of Rio de Janeiro

Energetic and Environmental Benefits of Residential Solar Microgeneration Added to Electric Vehicle Recharging in the City of Rio de Janeiro

Reliability modeling and assessment of a community microgrid with electric vehicle charging station as a critical load

Microgrids are constituted of interconnected loads and dispersed energy resources. They increase the reliability of the power supply, decrease the cost of the per unit energy and support the green environment concept. Microgrids have gained importance due to their ability to deliver reliable energy and operate in both grid-connected and off-grid/island modes. Renewable energy sources such as photovoltaic (PV) and wind can be connected to the microgrid system to generate green energy. An electric vehicle charging station (EVCS) can be deployed in microgrids powered by renewable energy sources. An effective way to handle the rapidly rising load demand of electric vehicles (EVs) is to integrate EV recharge stations into microgrids. Due to the intermittent nature of solar PV and EV load's reliability, an evaluation of microgrids becomes challenging. Assessment of the components’ failure and their impact on the system reliability is proposed using different methods. Reliability Block Diagram (RBD), Markov Reliability Modeling (MRM) and Fault Tree Analysis (FTA) - Monte Carlo simulation are used for the Reliability Modeling of a community microgrid considering EVCS as a critical load. The mean time to failure (MTTF) is computed for the entire microgrid which includes PV, wind and fuel cell systems integrated into the grid with associated power electronic devices, transmission lines, transformers and point of common coupling (PCC). MATLAB software was used to program and study the reliability aspects of the microgrid. Using the FTA MCS technique and grid functions for 45 years with 20 % reliability, significant results were obtained in terms of a MTTF rate of 26.16 years.

Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling

Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling

Adaptive Neuro-fuzzy Inference System-Based Data-Driven Model for Optimal Recharging of Electric Vehicles and Cost Prediction in Energy Hubs

Adaptive Neuro-fuzzy Inference System-Based Data-Driven Model for Optimal Recharging of Electric Vehicles and Cost Prediction in Energy Hubs

Hydrogen reduction-based energy management strategy of hybrid fuel cell/PV/battery/supercapacitor renewable energy system

Fuel cells can be used in sustainable cities for a variety of purposes, including emergency backup power systems, electric vehicle recharging, and the production of clean, efficient energy for buildings. Additionally, fuel cells can support distributed energy systems, improving energy management, and lowering dependency on conventional energy sources. However, the slow time response of proton exchange membrane fuel cell (PEMFC) during high-level load variation is an issue that needs addressing. To solve this, battery storage and supercapacitor can be integrated with a hybrid generation system including fuel cell, photovoltaic (PV). It's important to have effective energy management strategies (EMSs) to ensure the photovoltaic (PV) array, PEMFC, batteries, and supercapacitors function optimally. An EMS distributes the load demand among these components while maintaining high efficiency and low hydrogen consumption. This paper proposes an effective EMS for a hybrid generating system DC microgrid (MG) having PV, FC, battery, and SC based on recent Harris hawks optimizer (HHO) to manage the energy between the equipment while minimizing the total hydrogen consumption and enhancing the system efficiency. The research work compared several algorithms, including external energy maximization strategy (EEMS), equivalent consumption minimization strategy (ECMS), frequency decoupling and state machine control (FDSMC), proportional integral (PI), Cuckoo search (CS), and grey wolf optimizer (GWO), with the proposed HHO. The EEMS-based HHO algorithm outperformed the others resulting in 18.95 %, 38.77 %, 47.34 %, 34.43 %, 33.9 %, and 20.38 % hydrogen consumption reduction compared to PI, FDSMC, ECMS, EEMS, CS, and GWO, respectively. Moreover, efficiency improved by 7.62 %, 7.77 %, 17.15 %, 11.21 %, 2.17 %, and 5.12 % compared to PI, FDSMC, ECMS, EEMS, CS, and GWO, respectively. The obtained findings demonstrated the robustness and efficacy of the suggested HHO-EMS in obtaining the lowest hydrogen consumption of the DC microgrid that was provided.

Challenges and Opportunities in Electric Vehicle Charging: Harnessing Solar Photovoltaic Surpluses for Demand-Side Management

The recharging of electric vehicles will undoubtedly entail an increase in demand. Traditionally, efforts have been made to shift their recharging to off-peak hours of the consumption curve, where energy demand is lower, typically during nighttime hours. However, the introduction of photovoltaic solar energy presents a new scenario to consider when synchronizing generation and demand curves. High-generation surpluses are expected during the central day hours, due to the significant contribution of this generation; these surpluses could be utilized for electric vehicle recharging. Hence, these demand-side management analyses present important challenges for electricity systems and markets. This research explores this overdemand avenue and presents a method for determining the ideal recharge curve of the electric vehicle. Consequently, with this objective of maximizing photovoltaic generation to cover as much of the foreseeable demand for electric vehicles as possible in future scenarios of the electrification of the economy, the six fundamental electric vehicle charging profiles have been analyzed. A practical scenario for 2040 is projected for the Canary Islands, estimating the potential levels of demand-side management and associated coverage. The coverage ranges from less than 20% to over 40%, considering the absence of demand-side management measures and the maximum displacement achievable through such measures.

Adaptation of Renewable Energy Resources to Wireless EV Charging Using Deep Learning

When it comes to realizing a safe, efficient, and dependable power supply. More lately, there has been a rise in the use of data-driven methods for modeling electric vehicle (EV) charging. Since this problem involves a lot of unknowns, researchers are trying to implement model-free solutions. Reinforcement learning (RL) is one of several model-free methods now in use, and has seen extensive use in EV charging control. RL is a method to machine learning that prioritizes optimizing for cumulative reward rather than individual rewards. Solar photovoltaic and wind energy both have the potential to be used in the future to generate electricity. The CDDQN technique is developed, which incorporates an act constraint into the DDQN system in order to address the charging issue with such restrictions. The CDDQN optimizes charging procedures to better predict Q values and reduce charging choosing action errors. Wind and solar energy are great options since they do not harm the environment. The layout of the charging circuit is created and evaluated in MATLAB Simulink, taking into account the changing recharging requirements of EVs. Additionally, this work explores the use of RL in EV coordinating to study and design state-of-the-art optimized EMSs that may be used for EV recharging.

From Parking Meters to Vending Machines: A Study of Usability Issues in Self-Service Technologies

This paper describes a mixed-methods usability study of seven diverse Self-Service Technologies (SSTs). SSTs mediate many of our everyday interactions with individuals, businesses, and government organizations. Parking meters, transport ticket machines, electric vehicle recharge points, and fast-food ordering kiosks are all likely familiar examples of this category of technology, promising convenient access to products and services. Despite their ubiquity, many SSTs suffer from severe usability issues, the nature of which have not been explored to date by the HCI community. This study evaluates the interactions between users and a broad sample of SSTs, details the usability issues that occurred, explores their connections and consequences, and presents a set of design considerations that may lead to their remediation.

Dynamic electric vehicle routing problem considering mid-route recharging and new demand arrival using an improved memetic algorithm

With the widespread popularity of electric vehicles (EVs) in recent years, electric vehicle routing problem (EVRP) has received considerable attention from logistics management field. However, the real-world logistics and transportation system is always time-varying, such as new customer demand arrives dynamically. This dynamic transportation environment poses a great deal of challenges upon the decision-making of EVRP since the mid-route recharging of EVs has to be considered to meet the dynamic arrival of new customer demand due to the limited battery capacity. Therefore, a dynamic EVRP (DEVRP) with mid-route recharging and new demand arrival is investigated in this study. A mathematical model considering both recharging and routing decision-makings is developed, and then an improved memetic algorithm (IMA) with adaptive local search and economical random immigrant strategy is proposed. Four peer algorithms are used to test the performance of the proposed IMA on numerous extended benchmarks. It is observed that IMA can be treated as a more effective solution tool to achieve a better routing plan as well as the improved energy efficiency of EVs.

A multi-stage sequential load restoration method for under-frequency load shedding with electric vehicles participation

As a low-cost and high-efficiency distributed energy storage resource, electric vehicles (EVs) play an increasingly prominent role in power system regulation. Since EVs are the regulation resources on the demand side, EVs should return to the initial states as soon as possible after completing the grid regulation task to avoid affecting the charging demand. Although EVs can provide a more flexible response to under-frequency load shedding (UFLS) than load shedding, the disorderly recharging of EVs during the load restoration stage may cause a significant second disturbance to the system frequency after UFLS. To ensure frequency stability and simultaneously restore the power supply, a multi-stage sequential load restoration method for UFLS with EVs participation is proposed to coordinate the priority and sequential restoration between the shedding load and recharging power of EVs. In the proposed method, the restoration willingness of EVs is quantified by the membership function, and the EV recharging optimization model with the objective function of maximizing the user’s restoration willingness satisfaction is established based on a two-stage robust under-frequency load shedding framework. Simulation results show that the proposed method can ensure system frequency stability and complete the load restoration quickly, effectively avoiding the secondary frequency drop caused by the disorderly recharging of EVs.

Greenhouse gas emissions embodied in electric vehicle charging infrastructure: a method and case study of Georgia, US 2021–2050

Electric vehicle (EV) charging infrastructure buildout is a major greenhouse gas (GHG) mitigation strategy among governments and municipalities. In the United States, where petroleum-based transportation is the largest single source of GHG emissions, the Infrastructure Investment and Jobs Act of 2021 will support building a national network of 500 000 EV charging units. While the climate benefits of driving electric are well established, the potential embodied climate impacts of building out the charging infrastructure are relatively unexplored. Furthermore, ‘charging infrastructure’ tends to be conceptualized in terms of plugs and stations, leaving out the electrical and communications systems that will be required to support decarbonized and efficient charging. In this study, we present an EV charging system (EVCS) model that describes the material and operational components required for charging and forecasts the scale-up of these components based on EV market share scenarios out to 2050. We develop a methodology for measuring GHG emissions embodied in the buildout of EVCS and incurred during operation of the EVCS, including vehicle recharging, and we demonstrate this model using a case study of Georgia (USA). We find that cumulative GHG emissions from EVCS buildout and use are negligible, at less than 1% of cumulative emissions from personal light duty vehicle travel (including EV recharging and conventional combustion vehicle driving). If an accelerated EVCS buildout were to stimulate a faster transition of the vehicle fleet, the emissions reduction of electrification will far outweigh emissions embodied in EVCS components, even assuming relatively high carbon inputs prior to decarbonization.

Implementation Strategy for DC Fast Charging Stations for Electric Vehicles

Adoption of electric vehicles (EV) has been on a rising trend in the recent past. Provision of public DC fast charging infrastructure at strategic points is one of the major drivers towards the adoption of electric vehicles. This paper develops an implementation strategy for DC fast charging stations as an element in transmission and distribution infrastructure at strategic points to supply electrical energy for the recharging of electric vehicles. Kenya Power Ltd., a distribution utility player in Kenya, is a key stakeholder in the e-mobility ecosystem an is keen on opportunities that can take up extra capacity especially off-peak hence smoothening the load curve, maximizing on the use of transmission and distribution infrastructure as well as exposure to new technology. DC fast charging stations encourage the purchase of electrical vehicles and is one-step closer to a low – carbon economy, using clean transport. A SWOT analysis was used to construct an implementation strategy for EVs charging stations. Financial viability of the project was analysed using payback period, cost-to-benefit ratio, net present value (NPV), and internal rate of return (IRR). The financial indicators suggested that the investment is profitable, hence it is worth to consider it for investment.

Renewable and Sustainable Energy Status and their Applicability in Electric Vehicle Recharge Stations

Renewable and Sustainable Energy Status and their Applicability in Electric Vehicle Recharge Stations

Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House

Some analyses state that buildings contribute to overall energy consumption by 20–40%, which, in the context of the recent geopolitical energy crisis, makes them a critical issue to study. Finding solutions for better energy management in buildings can have a significant impact on the energy sector, thus reducing EU energy dependencies and contributing to the fulfillment of the REPowerEU goals. This paper focuses on proposing a simplified model of a residential house considering the main appliances, heating and cooling, a photovoltaic system, and electric vehicle recharging. Weather and solar irradiance forecasts are taken into account. The model predicts the energy demands of a house based on online weather forecasts and the desired indoor temperature. The article also focuses on the analysis of how weather forecast uncertainty affects energy demand prediction. This model can be used to better understand and predict the energy demand of either a single house or a set of houses. A multi-objective optimization approach that takes into account the preferences of users/inhabitants is developed to provide a compromise between the price paid for the electricity and temperature comfort. The authors plan to apply the proposed model to a residential house’s real-time control system. The model will be tuned, its predictions will be tested, and it will be used for energy demand optimization.

Impacto de los Vehículos Eléctricos sobre la Red de Distribución. Análisis bajo distintos modos de operación

Electric mobility is a sustainable alternative that allows the reduction of energy consumption and the emission of polluting gases with respect to conventional mobility. There are projections that predict an increase in the use of electric vehicles. Thus, several lines of research deal with the characteristics of the integration of these new demands and the effects that will occur on electrical systems. The main goals of this paper are: (i) to determine the impact of a moderate insertion of public charging points on the network; (ii) to evaluate the penetration level of EVs of residential users for slow and semi-fast home charging modes (G2V), according to restrictions of operation variables from the grid; and (iii) to propose some management strategies for controlled recharging and the dual function of charging and supplying energy from the electric vehicles to the grid through their storage batteries (V2G). The obtained results show that the moderate incorporation of public charging points does not significantly affect the operability of the network. Furthermore, it is shown that the controlled recharging of electric vehicles manages to reduce the negative impacts on the electrical system under study, allowing higher levels of insertion and/or delaying investments in the electrical infrastructure. An operation mode with energy from the electric vehicles to the grid would allow displacing the peak generation characterized by its high levels of pollution. Nevertheless, this mode of operation makes the system more susceptible to operating within inadmissible ranges.

Optimized Convolutional Neural Network-Based Capacity Expansion Framework for Electric Vehicle Charging Station

The usage rate of electric vehicles (EVs) is gradually increasing. Recharging of EVs should be carried out repeatedly over time, and the energy needed for this is high and increasing. With the present infrastructure, we cannot supply the required energy, and therefore, we need to implement a model that expands the power grid to satisfy our energy requirements. This paper proposes a convolutional neural network-based dynamic capacity expansion (CNN-DCE) for EV charging. Flower pollination optimization algorithm (FPOA) was used to improve the hyperparameters of CNN during training. The main aim is to reduce the cost of installing additional capacity resources and to reduce the operational cost. To cope with the load growth, different capacity resources are installed at different years of the planning boundary. Five statistical indices, such as mean squared error, mean absolute error, correlation coefficient, and scatter index, are used to evaluate the performance of CNN. The capacity expansion plan in the microgrid is achieved by expanding the energy of battery energy storage systems, microturbines, and solar and wind energy systems. The queuing delay for the EVs waiting in a queue for recharging has been considered. The performance of the proposed CNN-DCE is studied and compared with three other state-of-the-art methods. The results show that the resources reduce the planning cost to 26% for the short-term planning horizon, the long-term plan has 150% of the expansion, and the wind energy system covers 48% of the expansion cost.

The Impact of Coil Position and Number on Wireless System Performance for Electric Vehicle Recharging.

Recently, most transportation systems have used an integrated electrical machine in their traction scheme, resulting in a hybrid electrified vehicle. As a result, an energy source is required to provide the necessary electric power to this traction portion. However, this cannot be efficient without a reliable recharging method and a practical solution. This study discusses the wireless recharge solutions and tests the system’s effectiveness under various external and internal conditions. Moreover, the Maxwell tool is used in this research to provide a complete examination of the coils’ position, size, number, and magnetic flux evolution when the coils are translated. In addition, the mutual inductance for each of these positions is computed to determine the ideal conditions for employing the wireless recharge tool for every charging application. A thorough mathematical analysis is also presented, and the findings clearly demonstrate the relationship between the magnet flux and the various external conditions employed in this investigation.

Optimal Recharging of EVs for Peak Power Shaving and Valley Filling using EV-Aggregator model in a Micro-grid

This work presents an optimal recharging strategy for Electric vehicles (EVs) using Quadratic Programming (QP) to flatten the peak power demand on the utility. The increase in penetration of EVs in distribution systems causes a significant increase in peak power demand due to synchronization between the peak load hours and EV charging period. The optimization technique aims to assess the effects of optimal scheduling considering the initial State-of-charge (SOC) level, Demand Side Management (DSM) functionalities that meet both the grid and EV owners’ requirements. The feasibility of the scheme is verified with two case studies using different aggregator units and the effect on the system is analyzed by determining the load demands from local utilities and EVs. The aggregator units collect data from EV users with common interests such as EV battery specification, Battery SOC, recharging period and mediate with utility operators such as Transmission system operator (TSO), Distribution system operator (DSO). Further, other parameters such as peak power reduction, peak-to-average ratio (PAR), standard deviation, and peak-to-valley differences are also compared to test the effectiveness of the implemented optimization technique. The outcomes of the study show that using load demand profile and optimal rescheduling using aggregated EVs can flatten the load curve for the utility and lower the demand charges for the end-users.

Optimization of electric vehicle recharge schedule and routing problem with time windows and partial recharge: A comparative study for an urban logistics fleet

The use of electric vehicles (EVs) is becoming more and more widespread and the interest in these vehicles is increasing each day. EVs promise to emit less air pollution and greenhouse gas (GHG) emissions with lower operational costs when compared to fossil fuel-powered vehicles. However, many factors such as the limited mileage of these vehicles, long recharging times, and the sparseness of available recharging stations adversely affect the preferability of EVs in industrial and commercial logistics. Effective planning of EV routes and recharge schedules is vital for the future of the logistics sector. This paper proposes an electric vehicle routing problem with the time windows (EVRPTW) framework, which is an extension of the well-known vehicle routing problem (VRP). In the proposed model, partial recharging is considered for the EVRPTW with the multiple depots and heterogeneous EV fleet and multiple visits to customers. While routing a set of heterogeneous EVs, their limited ranges, interdependent on the battery capacity, should be taken into consideration and all the customers' deliveries should be completed within the predetermined time windows. To deal with this problem, a series of neighbourhood operators are developed for the local search process in the variable neighbourhood search (VNS) and variable neighbourhood descent (VND) heuristics. The proposed solution algorithms are tested in large-scale instances. Results indicate that the proposed heuristics perform well as to this problem in terms of optimizing recharging times, idle waiting times, overtime of operators, compliance with time windows, number of vehicles, depots, and charging stations used.