Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

Data-Driven Analysis and Optimization for Urban Energy Systems Equitable Resilience

Electric vehicles (EVs) can be leveraged as power resources to support the grid operation in challenging scenarios, e.g., natural disasters or health crises such as the COVID-19 pandemic. This paper aims to enhance equity of power resilience in urban energy systems by means of strategic allocation of EV charging infrastructure. We first use data-driven approaches to infer the relationships between communities' power resilience equity and available EV charging infrastructure as well as other prominent social-demographic factors. This inference leads to the development of a machine learning model for power resilience inequity prediction. We further develop an optimization frame-work that jointly considers equitable resiliency and resource utilization to guide the optimized EV charging infrastructure allocation across the city. Case studies demonstrate the capability of the devised approach in enhancing power resilience equity in marginalized communities. © 2023 IEEE.

A Mission-Oriented Flight Path and Charging Mechanism for Internet of Drones.

In addition to traditional battery exchange services and stationary charging stations, researchers have proposed wireless charging technology, such as decentralized laser charging or drone-to-drone charging in flight, to provide power to drones with insufficient battery electricity. However, the charging methods presented in the literature will inevitably cause drones to wait in line for charging during peak hours and disrupt their scheduled trips when the number of drones grows rapidly in the future. To the best of our knowledge, there have been no integrated solutions for drone flight path and charging planning to alleviate charging congestion, taking into account the different mission characteristics of drones and the charging cost considerations of drone operators. Accordingly, this paper provides adaptive charging options to help drone operators to solve the above-mentioned problems. Drones on ordinary missions can use conventional battery swap services, wired charging stations, or electromagnetic wireless charging stations to recharge their batteries as usual, whereas drones on time-critical missions can choose drone-to-drone wireless charging or decentralized laser charging deployed along the fight paths to charge the batteries of drones in flight. Notably, since fixed-wing drones have larger wing areas to install solar panels, they can also use solar energy to charge during flight if the weather is clear. The simulation results exhibited that the proposed work reduced the power load of the power grid during peak hours, met the charging needs of each individual drone during flight, and cut down the charging costs of drone operators. As a result, an all-win situation for drone operators, drone customers, and power grid operators was achieved.

An Electric Vehicle Assisted Charging Mechanism for Unmanned Aerial Vehicles

The global greenhouse effect and air pollution problems have been deteriorating in recent years. The power generation in the future is expected to shift from fossil fuels to renewables, and many countries have also announced the ban on the sale of vehicles powered by fossil fuels in the next few decades, to effectively alleviate the global greenhouse effect and air pollution problems. In addition to electric vehicles (EVs) that will replace traditional fuel vehicles as the main ground transportation vehicles in the future, unmanned aerial vehicles (UAVs) have also gradually and more recently been widely used for military and civilian purposes. The recent literature estimated that UAVs will become the major means of transport for goods delivery services before 2040, and the development of passenger UAVs will also extend the traditional human ground transportation to low-altitude airspace transportation. In recent years, the literature has proposed the use of renewable power supply, battery swapping, and charging stations to refill the battery of UAVs. However, the uncertainty of renewable power generation cannot guarantee the stable power supply of UAVs. It may even be very possible that a large number of UAVs need to be charged during the same period, causing congestion in charging stations or battery swapping facilities and delaying the arranged schedules of UAVs. Although studies have proposed the method of that employing moving EVs along with wireless charging technology in order to provide electricity to UAVs with urgent needs, the charging schemes are still oversimplified and have many restrictions. In addition, different charging options should be provided to fit the individual need of each UAV. In view of this, this work attempts to meet the mission characteristics and needs of various UAVs by providing an adaptive flight path and charging plan attached to individual UAVs, as well as reducing the power load of the renewable power generation during the peak period. We ran a series of simulations for the proposed flight path and charging mechanism to evaluate its performance. The simulation results revealed that the solutions proposed in this work can be used by UAV operators to fit the needs of each individual UAV.

Various charging Methods in Electric Vehicle Application: A Review

During the covid pandemic, air quality has improved due to prolonged lockdown conditions. Hence according to the international energy agency, about 22% of environmental pollution is contributed by the transportation sector. Electric vehicles help in reducing the contribution towards carbon emission and help in mitigating the fossil fuel crisis and also promotes sustainable transportation. To enhance the growth of electric vehicle, charging infrastructure and range anxiety issues in the long drive has to be resolved. This paper reviews the various charging methods available for an electric vehicle. Some charging methods are wired and wireless charging, solar-powered, battery-swapping, vehicle-to-grid and vehicle-to-vehicle charging. A comparative study of these methods is tabulated. Based on the limitation of each method the optimum charging method for a vehicle is adapted for a particular application. © 2023 IEEE.

Selection of a Photovoltaic Carport Power for an Electric Vehicle

The increasing number of electric vehicles is forcing new solutions in the field of charging infrastructure. One such solution is photovoltaic carports, which have a double task. Firstly, they enable the generation of electricity to charge vehicles, and secondly, they protect the vehicle against the excessive heating of its interior. This article presents the functioning of a small carport for charging an electric vehicle. Attention is drawn to the problems of selecting the peak power of the photovoltaic system for charging an electric vehicle. An economic and energy analysis is carried out for the effective use of photovoltaic carports. In this article, we present the use of the Metalog family of distributions to predict the production of electricity by a photovoltaic carport with the accuracy of probability distribution.

A Simulation-Heuristic Approach to Optimally Design Drone Delivery Systems in Rural Areas

In recent years, drone delivery has become one of the most widely adopted emerging technologies. Under the current Covid-19 pandemic, drones greatly improve logistics, especially in rural areas, where inefficient road networks and long distances between customers reduce the delivery capacity of conventional ground vehicles. Considering the limited flight range of drones, charging stations play essential roles in the rural delivery system. In this study, we utilize simulation to optimize the drone delivery system design, in order to minimize the cost of serving the maximum capacity of customers. As facility siting is usually difficult to optimize, we propose a novel simulation-heuristic framework that continuously improves the objective to find near-optimal solutions. In addition, we conduct a case study using real-world data collected from Knox County, Tennessee. The results suggest that the proposed approach saves over 15% on total costs compared with the benchmark. © 2022 IEEE.

Charging after Lockdown: The Aftermath of COVID-19 Policies on Electric Vehicle Charging Behaviour in The Netherlands

During the COVID-19—related lockdowns (2020–2022), mobility patterns and charging needs were substantially affected. Policies such as work from home, lockdowns, and curfews reduced traffic and commuting significantly. This global pandemic may have also substantially changed mobility patterns on the long term and therefore the need for electric vehicle charging infrastructure. This paper analyzes changes in electric charging in the Netherlands for different user groups during different phases of the COVID-19 lockdown to assess the effects on EV charging needs. Charging needs dropped significantly during this period, which also changed the distribution of the load on the electricity grid throughout the day. Curfews affected the start times of charging sessions during peak hours of grid consumption. Infrastructure dedicated to commuters was used less intensively, and the charging needs of professional taxi drivers were drastically reduced during lockdown periods. These trends were partially observed during a post–lockdown measuring period of roughly 8 months, indicating a longer shift in mobility and charging patterns. © 2023 by the authors.

Monetization of digital newspapers: Experimentation with brand extension and buying influence

With digitization, journalistic companies tested out different ways of creating value through the capabilities that technology provides. Online newspapers applied the experience they had with their print versions to brand extensions, aiming to build revenue models that went beyond the classic models for advertising and charging for content. Their goal was to leverage brand value by expanding their name to a new product. This study focuses on Unidad Editorial, which under-took various initiatives testing the power of the digital brand elmundo.es and expanding the boundaries of the business. We use information obtained from 23 semistructured in-depth interviews with executives from the early days of their online business to the present, providing a comprehensive picture. Elmundo.es's experimentation with brand extensions and other non-advertising revenue sources helps us understand digital media's shift towards charging for content –which the Covid-19 pandemic provided a favorable environment for– in their search for alternatives to compensate for the decline in advertising revenue. New brand extension projects, for which brand strength and consistency with the original product are determinative, remain open. In addition, these new practices will be subject to the journalistic companies' investment capabilities. © 2023, El Profesional de la Informacion. All rights reserved.

Developing a solution algorithm for the autonomous electric vehicle routing problem considering the mixed fleet

In recent years, some phenomena such as the COVID-19 pandemic have caused the autonomous vehicle (AV) to attract much attention in theoretical and applied research. This paper addresses the optimization problem of a heterogeneous fleet that consists of autonomous electric vehicles (AEVs) and conventional vehicles (CVs) in a Business-to-Consumer (B2C) distribution system. The absence of the driver in AEVs results in the necessity of studying two factors in modeling the problem, namely time windows in the routing plan and different compartments in the loading space of AEVs. The arrival and departure times of the AEV at the customer’s location must be pre-planned, because, the AEV is not able to decide what to do if the customer is late at this point. Also, due to increasing the security of the loads inside the AEVs and the lack of control of the driver during the delivery of the goods, each customer should only have access to his/her orders. Therefore, the compartmentation of the AEV’s loading area has been proposed in its conceptual model. We developed a mathematical model based on these properties and proposed a hybrid algorithm, including variable neighborhood search (VNS) via neighborhood structure of large neighborhood search (LNS), namely the VLNS algorithm. The numerical results shed light on the proficiency of the algorithm in terms of solution time and solution quality. In addition, employing AEVs in the mixed fleet is considered to be desirable based on the operational cost of the fleet. Author

Electric Vehicle Charging Behavior Prediction using Machine Learning Models

The global spread of the COVID-19 pandemic has significantly impacted the electric vehicle (EV) industry. The lockdown restriction has resulted in a significant shift in the use of public charging infrastructures. This paper investigates the effects of COVID-19 on electric vehicle users' charging behavior before, after, and during COVID-19 lockdown restrictions, using the data from a public charging facility from the City of California. In this study, we performed data visualization using K-means and hierarchical clustering analysis. This work uses the vehicle's connection and disconnection time to identify common charging pattern identification and charging behavior where K-means clustering outperforms the hierarchical clustering for all three different scenarios modelled. In addition, prediction of collective charging session duration is achieved using Machine Learning Models, Random Forest and XgBoost. We achieved a mean absolute percentage error (MAPE) of 0.146 and 0.151 percent for XgBoost and Random Forest respectively. © 2022 IEEE.

Automatic Charging System for Disinfection Robots Based on Structure-Aware Semantic Mapping

Disinfection robots, which replace human efforts to disinfect the environment, are becoming popular due to the ongoing impact of COVID-19. To address the existing problems of imperfect and costly automatic charging systems for disinfection robots, this paper designs an automatic charging system for disinfection robots based on structure-Aware semantic mapping, which optimizes the automatic charging scheme for robots and integrates LIDAR and infrared modules to achieve the goal. Firstly, the data is associated with the charging pile's priori information through structure perception, and the identified semantic information is mapped into the local map of the robot SLAM. Then the infrared module is used to adjust the position of the charging port to align with the charging pile, and TOF laser distance measuring function is also added to avoid damage to the charging pile from the disinfection robot. In 50 times of simulation experiments, our proposed automatic charging system achieves an accurate alignment rate of 96%. © 2022 IEEE.

Photovoltaic systems with secondhand batteries of electric vehicles and their social implementation in times of COVID-19

As people spend more time at home during the COVID-19 pandemic, residential electricity consumption is increasing. To prevent global warming, the increased electricity consumption should be covered by renewable energy, e.g. photovoltaic (PV) power generation. However, PV systems are unstable power sources due to weather conditions;it is necessary to stabilize the residential PV power supply by using batteries. Now, electric vehicles (EV) are beginning to spread, especially in emerging countries. The EV batteries are useful for stabilizing the residential PV power supply. The idea of V2X, using EVs as batteries for buildings, is well-known but still a work in progress. Instead of V2X, we started an experiment based on another idea that we use the secondhand EV lithiumion batteries to stabilize the residential PV power supply. Since 2017, the experiment has been conducted in Yamaguchi in order to evaluate the self-sufficiency of a PV system with the stationary battery, which reused secondhand EV lithium-ion batteries as its components. Analysis of the experimental data revealed that the self-sufficiency of PV system was improved by up to 1.75 times with the stationary storage battery compared to the case without the stationary storage battery. © 2021 IEEE.

Location Selection of Charging Stations for Electric Taxis: A Bangkok Case

The transition from ICE to BEV taxis is one of the most important methods for reducing fossil fuel consumption and air pollution in cities such as Bangkok. To support this transition, an adequate number of charging stations to cover each area of charging demand must be established. This paper presents a data-driven process for determining suitable charging locations for BEV taxis based on their characteristic driving patterns. The location selection process employs GPS trajectory data collected from taxis and the locations of candidate sites. Suitable locations are determined based on estimated travel times and charging demands. A queueing model is used to simulate charging activities and identify an appropriate number of chargers at each station. The location selection results are validated using data from existing charging services. The validation results show that the proposed process can recommend better locations for charging stations than current practices. By using the traveling time data that take the current traffic condition into account, e.g., via Google Maps API, we can minimize the overall travel time to charging stations of the taxi fleet better than using the distance data. This process can also be applied to other cities.

Design and Fabrication of a Vaccine Refrigeration Box

Lately, vaccination has been a widespread action to fight the pandemic, requiring the vaccines to be sent out to various places at various distances while staying at optimal quality, usually using cooler boxes. However, today's cooler boxes use ice instead of having a continuous refrigeration system. Thus, this paper presents a vaccine refrigeration box design and fabrication that uses a modified thermoelectric system as the refrigeration system, a conventional and solar charging system as its charging system, and a Bluetooth temperature sensor. From the result of the experiment, the box maintained full functionality for 210 minutes after being charged using a solar charging system. In terms of fully charging the power source, the conventional charging system takes 4 hours and 8 hours using the solar charging system. © 2022 IEEE.

EV Charging Behavior Under Lockdown: A Multi-Method Comparison

The COVID-19 pandemic has affected human behavior drastically in various ways, including commuter patterns and traffic volumes. This paper investigates how the COVID-19 outbreak has changed the user habits and utilization patterns at public electric vehicle service equipment (EVSE). More than 7,300 charging sessions collected at 54 public Level 2 charging stations across the State of Rhode Island were analyzed using a multi-method approach comparing charging events from two time periods, before and during the pandemic. The study shows that charging behavior has changed significantly since the COVID-19 outbreak. We found that the energy consumption, charging duration, distance from home, and charging frequency decreased significantly during the pandemic. Additionally, the study discovered a relationship between the observation period and the day of the charging session. During the pandemic, charging on Sundays has become significantly more important for users than charging between Monday and Friday. We provide important insights for policymakers about how the COVID-19 pandemic has changed electric vehicle user charging behavior and demand. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Public Acceptability of Environmentally Linked Congestion and Parking Charging Policies in Greek Urban Centers

Nowadays, urban centers face the challenge to upgrade life quality by reducing traffic congestion, air pollution emissions and road casualties. Transport charging policies applied in cities at a quick pace are a key tool for sustainable mobility. However, public acceptability is an important precondition to be adopted for such policies. In this context, the scope of this paper is the investigation of the public acceptability of environmentally linked urban charging policies in Greek urban centers. Specifically, the paper’s objective is the investigation of Greek drivers’ acceptability of the implementation of a congestion charging policy and a parking charging policy with the charging being adjusted according to the Euro class and technology of the vehicle in favor of less polluting cars. A structural equation model (SEM) was developed using data from a questionnaire survey which provided a sample encompassing 733 respondent drivers from three main urban centers of Greece: Athens, Thessaloniki and Volos. Several statistical relationships were detected and quantified correlating the two examined urban environmental charging policies with five latent unobserved variables. Based on the results, public acceptability of environmental congestion charging policies and the public acceptability of environmental parking charging policies were found to be positively correlated with each other, meaning that a driver who supports one environmentally linked transport charging policy is more likely to support the other one as well. The environmental sensitivity and high commuting profiles of drivers are influential factors that positively affect the acceptability of the two examined transport charging policies’ implementation in Greek urban centers. Analysis has also shown that younger, higher-educated respondents and females are more likely to accept the environmental charging policies under consideration.

Review on Integrated On-Board Charger-Traction Systems: V2G Topologies, Control Approaches, Standards and Power Density State-of-the-Art for Electric Vehicle

This article reviews the different topologies compatible with V2G feature and control approaches of integrated onboard charger (iOBC) systems for battery electric vehicles (BEVs). The integrated topologies are presented, analyzed, and compared in terms of component count, switching frequency, total harmonic distortion (THD), charging and traction efficiencies, controllability, reliability and multifunctionality. This paper also analyzes different control approaches for charging and traction modes. Moreover, the performance indices such as setting time, rise time, overshoot, etc., are summarized for charging and traction operations. Additionally, the feasibility of a Level 3 charging (AC fast charging with 400 Vac) of up to 44 kW iOBC is discussed in terms of converter efficiencies with different switching frequencies and switch technologies such as SiC and GaN. Finally, this paper explores the power density trends of different commercial integrated charging systems. The power density trend analysis could certainly help researchers and solution engineers in the automotive industry to select the suitable converter topology to achieve the projected power density.

Technical Coordination of Aggregated Electric Vehicle Charging and Residential Loads at the Medium Voltage Level

Despite the coronavirus pandemic, the market share of electric vehicles (EV) has increased in recent years. For this reason, planning of new charging stations and active operation of charging stations have become more important. Studies about the integration of electric vehicles are very common, but the parameters and models adopted in these studies are simplified and analyses are made by making contradictory general assumptions. Therefore, there is a need for electric vehicle studies to be carried out with more detailed and realistic parameters. This study includes a wide range of normal and fast charging sessions and the housing demand of several customers. An aggregated charging management solution is developed, to keep the overall demand below the maximum limit at the medium voltage transformer level. This study provides a way to determine the suitability of the infrastructure or the integration challenges in the areas where the installation of parking and charging stations is aimed and proposes solutions. Initial simulation results show peaks at specific time intervals, especially in evening hours and these peaks cause overloads. To solve this problem two different methods were used. The first method is the random selection method, the second method is the sorted selection method. After the solutions are applied, high load values decrease, both worked successfully but the sorted selection method was more flexible and obtained more usable results. On the other hand, random method generally gave mix of good and bad results. © 2022 IEEE.

Variable Frequency Control for Constant Current Constant Voltage Inductive Wireless EV Charging System

EV market has risen despite COVID-19 pandemic. Wireless EV charging is safer operating, saves the environment, and is convenient with automated charging. Inductive wireless EV Charging (IPT) should be fewer power electronics components to improve the system's efficiency. This paper presents the fewer components constant current - constant voltage (CC-CV) IPT using frequency adaptive frequency control technique that does not need a primary dc-dc converter. The proposed CC-CV IPT charging building block consists of the front-end rectifier, CC-CV frequency controlled inverter, IPT coils, secondary high-frequency rectifier, and step resistive load battery simulator. The load current and voltage signals are detected and feedback to the PI control block that controls the frequency of the PWM gate drive of the inverter. The simulation and experimental results show the operating mode of 6A CC and 102 V CV chargings were performed. The wireless EV charging can be operated for the CC and CV chargings using variable frequency control. © 2022 IEEJ-IAS.