Electric Vehicle Charging Management Research Articles

EV Charging Management: a Forecasting Model Development for Parking Time and Arrival State of Charge

In the context of optimizing electric vehicle (EV) charging and discharging management, this study addresses the critical task of predicting, for the next day, the arrival and departure times of each EV, as well as its state of charge (SoC) when accessing the parking lot. This management challenge requires the application of a robust forecasting approach to ensure accurate forecasts. In this paper, the proposed predictor is developed using the Long Short-Term Memory (LSTM) modeling technique. The model feature selection process, is carried out based on a correlation/autocorrelation matrix integrating data of different natures (weather, EV arrival and departure time, EV state of charge). Performance validation of the proposed forecasting model is carried out using the Mean Absolute Percentage Error (MAPE). The supremacy of the developed LSTM predictor is highlighted by comparing its performances with those of a Convolutional Neural Network (CNN) model. For the next day’s Arrival Time model, the LSTM demonstrates a MAPE of 1.95×10-3, the Departure Time model yields a MAPE of 5.49×10-3, and the SoC at Arrival model exhibits a MAPE of 5 × 10-2. These findings underscore the significance of the LSTM model, coupled with feature selection strategies, in enhancing the precision of EV charging and discharging management within residential contexts.

Electricity scenarios for Hungary: Possible role of wind and solar resources in the energy transition

The paper examines the compatibility of wind and solar energy resources with projections of future electricity demand in Hungary. For such, we model the national electricity system and estimate surplus generation. The model makes use of hourly distributions of electricity demand and power generation. Simulations for the year 2033 (last scenario year) suggest that 46–47% of the projected electricity consumption can be supplied by wind turbines and solar PV technology with a surplus of less than 5% of yearly consumption. A suitable capacity ratio of wind to solar PV and the management of electric vehicle charging may reduce surplus electricity. It is shown by our EnergyPLAN model that the solar PV capacity should be 1.1 times the wind power capacity which is a huge contrast to the current situation where solar PV is almost 10 times the wind power capacity in Hungary.

Electric Vehicle Charging Management Based on Deep Reinforcement Learning

A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle (EV) owners. Considering the uncertainty of price fluctuation and the randomness of EV owner's commuting behavior, we propose a deep reinforcement learning based method for the minimization of individual EV charging cost. The charging problem is first formulated as a Markov decision process (MDP), which has unknown transition probability. A modified long short-term memory (LSTM) neural network is used as the representation layer to extract temporal features from the electricity price signal. The deep deterministic policy gradient (DDPG) algorithm, which has continuous action spaces, is used to solve the MDP. The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner. Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2% compared with other benchmark methods.

Electric Vehicles Charging Management System for Optimal Exploitation of Photovoltaic Energy Sources Considering Vehicle-to-Vehicle Mode

The growing penetration of distributed renewable energy sources (RES) together with the increasing number of new electric vehicle (EV) model registrations is playing a significant role in zero-carbon energy communities’ development. However, the ever-larger share of intermittent renewable power plants, combined with the high and uncontrolled aggregate EV charging demand, requires an evolution toward new planning and management paradigms of energy districts. Thus, in this context, this paper proposes novel smart charging (SC) techniques that aim to integrate as much as possible RES generation and EV charging demand at the local level, synergically acting on power flows and avoiding detrimental effects on the electrical power system. To make this possible, a centralized charging management system (CMS) capable of individually modulating each charging power of plugged EVs is presented in this paper. The CMS aims to maximize the charging self-consumption from local RES, flattening the peak power required to the external grid. Moreover, the CMS guarantees an overall good state of charge (SOC) at departure time for all the vehicles without requiring additional energy from the grid even under low RES power availability conditions. Two methods that differ as a function of the EV power flow direction are proposed. The first SC only involves unidirectional power flow, while the second one also considers bidirectional power flow among vehicles, operating in vehicle-to-vehicle (V2V) mode. Finally, simulations, which are presented considering an actual case study, validate the SC effects on a reference scenario consisting of an industrial area having a photovoltaic (PV) plant, non-modulable electrical loads, and EV charging stations (CS). Results are collected and performance improvements by operating the different SC methods are compared and described in detail in this paper.

Evaluating smart charging strategies using real-world data from optimized plugin electric vehicles

Management of electric vehicle (EV) charging is likely to be critical in avoiding large increases in peak electricity demand and subsequent build outs of generation and distribution infrastructure. Additionally, management of EV charging can help to more effectively utilize renewable energy resources. This study analyzed charging data from a real-world pilot program, the largest household-based study to date to analyze the effectiveness of different smart charging use cases at reducing charging costs and increasing utilization of renewable energy. The study included six different use cases that featured varying optimization signals and driver incentives. Study results suggest that the optimizations are effective at shifting load from times of high grid costs and congestion, most notably during early evening hours, to times of lower grid costs in the early morning and midday. The study finds that 15–20% of charge was shifted out of any given hour and 20–30% of charge was shifted into a given hour in the most effective use cases. In addition to shifting charging across time periods, charging events were also effectively shifted to different locations during some use cases, such as from overnight at households to during the day at workplaces.

Electric Vehicles Charging Management Using Machine Learning Considering Fast Charging and Vehicle-to-Grid Operation

Electric vehicles (EVs) have gained in popularity over the years. The charging of a high number of EVs harms the distribution system. As a result, increased transformer overloads, power losses, and voltage fluctuations may occur. Thus, management of EVs is required to address these challenges. An EV charging management system based on machine learning (ML) is utilized to route EVs to charging stations to minimize the load variance, power losses, voltage fluctuations, and charging cost whilst considering conventional charging, fast charging, and vehicle-to-grid (V2G) technologies. A number of ML algorithms are contrasted in terms of their performances in optimization since ML has the ability to create accurate future decisions based on historical data, which are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Long Short-Term Memory (LSTM) and Deep Neural Networks (DNN). The results verify the reliability of the use of LSTM for the management of EVs to ensure high accuracy. The LSTM model successfully minimizes power losses and voltage fluctuations and achieves peak shaving by flattening the load curve. Furthermore, the charging cost is minimized. Additionally, the efficiency of the management system proved to be robust against the uncertainty of the load data that is used as an input to the ML system.

Electric Vehicles Charging Management for Real-Time Pricing Considering the Preferences of Individual Vehicles

The paper proposes a real-time model for electric vehicles (EVs) controlled load charging. The proposed demand-side management (DSM) of EVs is implemented based on queuing analysis with a nonhomogeneous arrival rate and charging service periods dataset. An electric vehicle model is used which is based on a statistical survey to represent the uncontrolled demand of the EVs. A probability distribution for the time at which EVs are plugged and the corresponding value of the state of charges (SOCs) are considered. The preferences of individual EVs have been fully exploited through a set of instructions to fulfill the needs of the vehicles’ owners. The designated preferences include the owner setting for both, charging price preferences (OPR), and the maximum estimated parking time duration (EPTD). The quasi-static time-series (QSTS) simulation is used to simulate real-time scenarios of the 24-h simulation period. The IEEE 123 nodes radial test feeder is analyzed with different daily load curves, EV charging scenarios, and wind power penetrations. The results show the effectiveness of the proposed DSM in avoiding excessive levels of charging with/without penetration of non-dispatchable wind power generation. The proposed DSM enables the EVs to charge with low tariff rates either at excessive renewable power generation or late evening hours with available committed bulk power plants and light loading conditions.

Review of Communication Technologies for Electric Vehicle Charging Management and Coordination

Recently, electric vehicles (EVs) have been introduced as an alternative method of transportation to help mitigate environmental issues, such as carbon emissions and fuel consumption, caused by conventional transportation systems. The implementation of effective EV charging systems is essential to motivate mass adoption of EVs. Accordingly, fast and reliable communications between the charging systems and the EVs are vital for efficient management of the charging process. Different radio access technologies (RATs) are discussed in the literature to enable communication between the highly mobile EVs and the charging subsystems, to collect and exchange information such as state of charge (SoC), users’ locations, and charging decisions between the different network entities. This information can be used to coordinate charging plans and select the optimal routes for moving vehicles. This paper presents a survey of existing literature on vehicular communications for EV charging coordination and management. The communication requirements and feasible communication technologies for vehicular communication are first discussed in details. A review of the physical layer security strategies is then presented and the role of the different RATs in EV charging coordination and management is described and studied.

Electric Vehicles Charging Management Based on Flexible Load Aggregation

As the scale of electric vehicles grows year by year, disorderly charging of EVs brings great challenges to the safe operation of the power grid and charging management of EV charging service providers. The charging service providers play the role of EV aggregators and aggregate the flexible load of EVs through centralized scheduling strategy. Take profit of EV aggregators and variance of power grid as objectives. Based on the direct scheduling pattern, a multi-objective optimal scheduling model was established and solved by Genetic Algorithm (GA). The results show that under the centralized scheduling strategy of EV aggregators, the difference between peak and valley load of electric power system is reduced, the variance of the grid is lower, the benefits of the aggregator are increased and the EV charging costs are reduced.

Electric Vehicle Charging Management Using Auction Mechanism for Reducing PV Curtailment in Distribution Systems

This paper proposes an electric vehicle (EV) charging management scheme for effective utilization of photovoltaic (PV) power based on an auction mechanism. The proposed scheme coordinates an amount of self-consumption of PV output by shifting the charging period of customers' EVs, and it reduces PV curtailment caused by voltage rise in low-voltage distribution systems (LVDSs). The auction mechanism is introduced in order to assure both the equity of benefit in each customer and the autonomy that enables customers to voluntarily participate in the EV charging management scheme. The authors perform numerical simulations based on a Japanese distribution system model and evaluate the effectiveness of the proposed scheme. The results show that our proposed scheme reduces the residential operation cost and the PV curtailment in LVDSs without an enhanced communication network even taking into consideration the effects of forecasting errors of electricity demand and PV output, and unexpected disconnection condition of several EVs.

Special issue on applications and techniques in cyber intelligence 2019

This special issue focuses on all aspects on techniques and applications in cyber intelligence research. The purpose of this special issue is to provide a forum for presentation and discussion of innovative ideas, cutting edge research results, and novel techniques, methods and applications on all aspects of cyber intelligence. It includes selected articles from 2019 International Conference on Applications and Techniques in Cyber Intelligence (ATCI2019) June 22-24, 2019 Huainan, China as well as an open call. The submitted articles were reviewed by experts from both academia and industry. After two rounds of reviewing, the 15 highest quality manuscripts were suggested for EiC to accept for this special issue. This special issue will be published by CCPE as special issues. A combination weighted clustering algorithm is proposed by Li et al1 in the study to reduce the communication overhead of the distributed cognitive network and maintain the stability of the network structure. Li2 investigates edge-forwarding index of routing in the lexicographic product network, which is used to measure the congestion of an edge. Shang et al3 presents the on-chip evolution system of combinational logic circuits by a new hybrid algorithm known as improved genetic-simulated annealing algorithm. Jianmin et al4 proposes the uniform distribution code, model identification code, standard dimension code, and attribute dimensional code. Aiming at the actual production needs of the Dadingzi mountain hydrojunction, An et al5 studies the optimal control of the flood discharge system. Xu et al6 attempts to analyze the traffic network from the perspective of vulnerability. Ding et al7 provides a basis for adjusting the transport organization plan and evacuating passenger flow in emergency. In order to solve the problems of low utilization of the shipyard unreasonable placed of the blocks and unbalanced use of flat transporters, grid search algorithm for block storage yard scheduling is proposed by Zhou et al.8 Pei et al9 establishes a customer experience evaluation index system from the perspectives of O2O takeout platform, delivery staff, and distribution process. To realize the benign interaction between electric vehicle charging and switching power stations and power grid, and the demand-side management of electric vehicle charging and switching power stations, the Monte Carlo random sampling method is used by Gan and Zheng10 to collect the battery-charged state of batteries in one day. Yang and Liu11 draws on the theory of biological immune system, combines the ontology with the growth risk of start-ups, and uses SWRL and Jess engine to build an ontology knowledge base of start-ups' growth risk to identify various risks in the growth process of start-up. Tran et al12 provides a comprehensive research on cellular network traffic prediction using exponential smoothing methods. Guo and Chen13 proposes an interval support vector domain description method through using the dynamic decreasing inertia weight particle swarm optimization. Xu et al14 present the development of a cloud-based difference detection method using big GPR data for roadbed damage detection. Lu et al15 focuses on textual cyberbullying detection because text is the most common form of social media. The guest editors would like to thank Prof. Geoffrey C. Fox who is the editor in chief of CCPE. His help and trust is the most important thing for the success of this SI. The guest editors would like to thank the reviewers for their high-quality reviews, which provided insightful and constructive feedback to the authors of the articles.

Distributed Electric Vehicles Charging Management With Social Contribution Concept

This article proposes a charging management of electric vehicles (EVs) with a newly presented EV social contribution. The EV charging problem is represented by a generalized Nash equilibrium game where each individual EV tries to minimize its charging cost while satisfying its own charging requirements and respecting the charging facility constraints. The individual EV features a social behavior to potentially contribute in shifting its charging schedule from specific intervals that have insufficient charging power. This shift in the EV schedule will allow more charging power to other EVs that admit stricter charging requirements, i.e., intervals and demands. In this way, the contributed EVs socially help others in reducing their charging costs, which is particularly important during the overload cases in the system. The proposed solution is reached iteratively in a distributed way utilizing the consensus network and found based on the receding horizon optimization framework. Both simulation and experimental results demonstrate the effectiveness and correctness of the proposed social contribution in the charging management for reducing the charging cost of EVs.

Electric Vehicle Charging and Batteries Swapping Management Strategy with Photovoltaic Generation in Business Districts

The intermittent characteristics of photovoltaic generation present a challenge in its promotion and application in urban loads on the distribution network level. Integrated construction of photovoltaic generation, battery storage stations, and electric vehicles (EVs) provides a reasonable solution for photovoltaic applications in cities. With the increase in EVs, business districts (BDs) have become popular for EV charging and parking. By considering photovoltaic power and commuter demand, a new BD framework is proposed in this paper; this includes a battery swapping area, a quick charging area, and a slow charging area. Moreover, based on a multi-agent model, an EV charging management strategy is proposed. The feasibility of the BD framework and the rationality of the energy management strategy are verified in a simulation of 96 time slots. The EV charging management strategy realizes power shifting in the slow charging area, and, with the enhanced use of photovoltaic power, the total cost of EV charging can be decreased in BDs.

Decentralized charging control strategy of the electric vehicle aggregator based on augmented Lagrangian method

Uncoordinated charging of large-scale electric vehicles can affect the safe and economic operation of distribution power system. As an intermediary between power grid and individual electric vehicle, electric vehicle aggregator can play an important role in the coordination and management of electric vehicle charging. In this paper, we develop a decentralized EV charging control strategy of the electric vehicle aggregator for scheduling the flexible charging demand of plug-in electric vehicles in residential distribution networks. First, the centralized charging control model of the electric vehicle aggregator aiming at the maximization of its revenue under the background of time of use price mechanism is established, meanwhile meeting individual charging requirements and satisfying distribution network constraints. Then a decentralized charging control strategy of the electric vehicle aggregator based on the augmented Lagrangian method and the alternating direction multiplier method is proposed. The electric vehicles can decide their charging plan locally with the decentralized strategy, which can eliminate the problem of high communication cost, low computing efficiency and privacy protection caused by the centralized charging control model in practical application. The effectiveness of the proposed strategy is evaluated with a representative distribution network model.

Cost-Effective and Ecofriendly Plug-In Hybrid Electric Vehicle Charging Management

This study explored two charging management schemes for plug-in hybrid electric vehicles (PHEVs). The PHEV drivers and the government were stakeholders who might have preferred different charging control strategies. For the former, a proposed controlled charging scheme minimized the operational cost during PHEV charge-depleting and sustaining modes. For the latter, the research minimized monetized carbon dioxide emissions from electricity generation for the PHEVs charging, as well as tailpipe emissions for the portion of PHEV trips fueled by gasoline. Hourly driving patterns and electricity data were leveraged. Both were representative of each of the eight North American Electric Reliability Corporation regions to examine the results of the proposed schemes. The model accounted for drivers’ activity patterns and charging availability spatial and temporal heterogeneity. The optimal charging profiles confirmed the differing nature of the objectives of PHEV drivers and the government; cost-effective charge should occur early in the morning, while ecofriendly charge should be late in the afternoon. Each control’s trade-offs between operation cost and emission savings are discussed for each North American Electric Reliability Corporation region. The availability of workplace and public charging was found to affect the optimal charging profiles greatly. Charging control is more efficient for drivers and government when PHEVs have greater electric range.

An Electric Vehicle Charging Management Scheme Based on Publish/Subscribe Communication Framework

Motivated by alleviating CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> pollution, electric vehicle (EV)-based applications have recently received wide interests from both commercial and research communities by using electric energy instead of traditional fuel energy. Although EVs are inherently with limited traveling distance, such limitation could be overcome by deploying public charging stations (CSs) to recharge EVs' battery during their journeys. In this paper we propose a novel communication framework for on-the-move EV charging scenario, based on the Publish/Subscribe (P/S) mechanism for disseminating necessary CS information to EVs, in order for them to make optimized decisions on where to charge. A core part of our communication framework is the utilization of roadside units (RSUs) to bridge the information flow from CSs to EVs, which has been regarded as a type of cost-efficient communication infrastructure. Under this design, we introduce two complementary communication modes of signal protocols, namely, push and pull modes, in order to enable the required information dissemination operation. Both analysis and simulation show the advantage of the pull mode, in which the information is cached at RSUs to support asynchronous communication. We further propose a remote reservation service based on the pull mode such that the CS-selection decision making can utilize the knowledge of EVs' charging reservation, as published from EVs through RSUs to CSs. Results show that both performances at CS and EV sides are further improved based on using this anticipated information.