Characteristics Of Electric Vehicles Research Articles

A Study on the Factors Influencing Consumer Buying Behaviour towards Electric Vehicles among University Students in Anand City

This study aims to investigate the different factors influencing consumer buying behaviour towards Electric Vehicles (EVs). The research was mainly based on primary data and selected populations as university students of Anand, Gujarat and also used secondary data sources to collect information regarding EVs. This study bridges the research gap and selects 225 respondents to the convenience sampling method. Frequency distribution, percentage, mean, standard deviation and various statistical methods such as the reliability test, KMO and Bartlett’s test, factor analysis, and non-parametric test were carried out. The result extracted six influencing factors for consumer buying behaviour towards EVs such as high maintenance, reliability, EV features, inflation, environment, and cost-benefit. The use of EVs by the majority of respondents confirms that there is a potential for future expansion. The present study helps customers/consumers know about purchasing EVs benefits and the EVs industry. It is believed that the findings make an important contribution to EV industries, policymakers, future researchers, and those who want to purchase EVs.

Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks

The extensive integration of electric vehicles (EVs) into power grids has raised safety concerns. Meanwhile current EVs charging loads forecasting models mainly focus on single-step predictions, neglect load sparsity and underutilize residuals, limiting their accuracy. To tackle these issues, a multi-step short-term forecasting model, specifically designed to predict charging loads at public EVs charging stations using historical data, is proposed. The model begins with a preprocessing phase to handle missing data and outliers. Subsequently, an Autoformer variant is intruduced, which initially integrates feature extraction block leveraging the Temporal Convolutional Network (TCN) to adeptly capture temporal dynamics. This is further enhanced with a Discrete Cosine Transform (DCT)-based frequency enhancement block to learn sparsity and periodicity features of EVs charging loads by frequency mapping. In addition, an advanced error correction block, comprising Bayesian optimization algorithm, Convolutional Neural Networks, Multi-Head Self-Attention, and Gated Recurrent Units (BCMG), is incorporated to refine initial prediction results. Above all, the TCN-DCT enhanced Autoformer-BCMG (TDformerBCMG) model is proposed. Finally, a comprehensive experimental analysis is conducted on ACN-data for EVs charging load forecasting in 1–3 h steps. It is demonstrated that TDformerBCMG not only is effective and superior in each component, but also achieves a reduction in Mean Absolute Error by 20.94 % to 74.05 % compared to existing models. Moreover, stability is evidenced across 50 repeated trials, with the standard deviation for the Coefficient of Determination varying between 8.5844e-3 and 3.1580e-2. Further discussion on application analysis is provided. Consequently, TDformerBCMG offers a viable methodology and establishes a new benchmark for power system implementation.

A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework

Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction.

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

With increasing penetration of distributed generators (DG), the uncertainty and intermittence of renewable energy has brought new challenges to the economic dispatch and promotion of environment sustainability of microgrids. Active loads, especially in electric vehicles (EVs), are thought to be an efficient way to deal with the uncertainty and intermittence of renewable energy. One of the most important features of EVs is that their demand will vary in response to the electricity price. How to determine the real-time charging price to guide the orderly charging of EVs and operate with an uncertain renewable energy output represents an important topic for the microgrid operator (MGO). To this end, this paper formulates the optimal pricing and robust dispatch problem of the MGO as a Stackelberg game, in which the upper level minimizes the MGO’s cost, while the lower level minimizes the charging cost of each EV. In the problem, the approximate linear relationship between the node voltage and equivalent load is modeled, and the approximate linear expression of the node voltage security constraint is derived. Using dual optimization theory, the robust optimal dispatch model is transformed into a linear programming model without uncertain variables. Then, the Stackelberg game model is transformed into a mixed integer linear program by using the duality theorem of linear programming. Finally, the effectiveness of the proposed method is proved by simulation within the modified IEEE33-bus system.

IDADET: Iterative Double-Sided Auction-Based Data-Energy Transaction Ecosystem in Internet of Vehicles

In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EV), making the data acquisition scenario gradually extend to the Internet of Vehicles (IoV), and thus the corresponding operational rules and economic feasibility need to be fully investigated there. In this paper, we focus on a general IoV-oriented data acquisition market that consists of a data center, multiple EVs, multiple roadside units (RSUs), and a market operator (broker), with the objective of social welfare maximization (SWM) by identifying the optimal data task allocation. However, due to the inherent information asymmetry and fragmentation in such a market, it is not feasible to solve the SWM problem directly. To this end, we propose an iterative double-sided auction (IDA) mechanism, which leverages the self-interested feature of RSUs and EVs to decompose the SWM problem, enabling every participant to make decisions in a distributed manner under the broker’s coordination. A complete set of operational rules covering the data task allocation, bidding, payment, and reimbursement are elaborately designed to achieve SWM, and energy is adopted as the pricing “currency”, such that an IDA-based Data-Energy Transaction (IDADET) ecosystem is established in IoV. We verify the economic feasibility of the proposed IDADET ecosystem by showing its convergence and desirable properties of individual rationality, budget balance, incentive compatibility, and economic efficiency. In addition, considering the psychological effects of practical market participants, we make amendments to the operational rules of the IDADET ecosystem from the behavioral economics perspective, aiming to ensure its long-term well-functioning. Extensive numerical results are presented to show the performance of the IDADET ecosystem and demonstrate its advantages in terms of economic properties, operational feasibility, fast convergence, and market social welfare.

Exploring the value of electric vehicles to domestic end-users

Owing to the recent ban on the sales of new petrol and diesel cars in the United Kingdom (UK) by 2030, combined with the UK’s commitment to net-zero emission of greenhouse gases by 2050, a projected increase in the growth rate of electric vehicles (EVs) is inevitable. In recent years, there has been an increase in the adoption of EVs, but not at a rate sufficient to meet net-zero targets. Although benefits do exist for current EV owners, barriers such as the availability of charging infrastructure, total cost of ownership, battery costs, amongst others still present a challenge for the required adoption rate. In this work, we therefore aim to address some of these barriers, specifically the total cost of ownership and battery costs, by exploring the value a range of EVs on the market give to domestic end-users with different usage classes. Using a techno-economic-environmental mixed integer linear optimisation model which considers local energy demands, retail electricity tariffs, local renewable energy generation and battery degradation, potential benefits for EVs adopters are analysed from a cost or Carbon dioxide (CO2) minimisation objective. This model adopted considered a range of vehicle types – EVs and non-EVs – and properties, installed PV sizes, and user travel behaviour classes, and results showed that although EVs have a relatively higher purchase costs, total cost values are comparable, in some cases cheaper, when compared with conventional non-EVs. EV users further gain from environmental benefits through a reduction in the CO2 emitted irrespective of the user’s desired goal. A dominance analysis was also carried out to determine the order of importance of key input variables to the optimisation model in predicting costs and CO2 emission quantities. The results obtained are helpful to end-users in prioritising EV features during purchase based on personal goals of cost or carbon emissions reduction.

Electric vehicle optimum charging-discharging scheduling with dynamic pricing employing multi agent deep neural network

Electric Vehicles (EVs) are environmentally friendly. Extensive progress makes EVs popularly deployed and adopted. Once EVs are connected to the smart grid, EVs can act as both variable load and energy supply systems. One major challenge in EV deployment is the management of charging stations with minimum waiting time and reduced EV customer electricity prices. Considering dynamic pricing and various EV features could provide optimum scheduling. To address this issue, we proposed dynamic pricing and optimized scheduling as constrained by a Markov decision process. The solution is obtained by a novel Multi-Agent Deep Neural Network (MADNN). A numerical experiment was conducted with real-time data using the Nissan Leaf model EV. The proposed MADNN uses queuing model and obtained the highest saving rate of 18.45% and an average profit of 340.5 $/kWh with a network convergence time of 520 s. This obtained result validates the effectiveness of the proposed EV scheduling algorithm.

Attitudes of Drivers towards Electric Vehicles in Kuwait

Although researchers have started to examine the landscape of electric vehicles (EVs) around the world, very little research has examined this phenomenon in Kuwait. In addition, limited research has explored it among drivers. Kuwait constitutes a very promising market for EVs because there is a need to lower GHG emissions and improve the air quality in Kuwait. This study therefore explored the attitudes of conventional car internal combustion engine (ICE) drivers towards EVs in Kuwait, particularly identifying attributes, features, enablers, and barriers of EVs that are considered important by potential consumers in Kuwait. This study utilized a mixed method approach in terms of quantitative data and qualitative data from a sample of 472 drivers to accomplish the main objectives of this study. The study showed that more than half of participants would buy an EV within the next 3 years, and they would buy if several conditions were met. That includes a cheaper purchase price with the assistance of policies controlled by the government along with the availability of suitable infrastructure for EVs relating to charging stations, fast lanes, and free parking spaces. More than 40% of participants would also seriously start thinking about buying an EV if the gas/fuel prices increased by between 50 and 199%. More than 40% of participants thought that EVs are safe in relation to fire and car crashes. Furthermore, approximately half of participants would pay 6–20% more for an EV that is both environmentally friendly and much quicker than gasoline cars. In addition, participants would also prefer EVs over gasoline cars in the future for their environmental, economic, and technological values. More importantly, the study yielded many significant findings, such as the demanded and preferred features of EVs and reflections on the readiness of the Kuwaiti market.

Coupled dispatching of regional integrated energy system under an electric-traffic environment considering user equilibrium theory

With large-scale electric vehicles (EVs) connected to the distribution network, the charging demand of EVs aggravates the peak-to-valley difference, which brings massive pressure to the power system. How to environmentally optimize the system considering the features of EVs’ charging demand has become a mainstream issue. To tackle this challenge, a low carbon economic dispatch model for the integrated energy system is proposed under an electric-traffic environment based on user equilibrium (UE) theory. This model considers carbon emission and multiple energy demand responses. First, to optimize the spatio-temporal distribution of the EV charging demand, a semi-dynamic traffic assignment (SDTA) model is constructed. This model considers vehicle travel, road congestion, and the influence of traffic flow in one interval to guide EVs to select an optimal route. Then, for reducing the carbon emission and promoting the reliability of the integrated energy system, a ladder carbon trading model and a demand response model for three different types of loads are proposed. Finally, a co-optimization system of traffic network, distribution network, and natural gas network is employed to illustrate the validity of the proposed model. The proposed model is demonstrated on the regional integrated energy system (RIES). The simulation results show that the peak-to-valley difference can be obviously relieved by introducing the SDTA model. In the meantime, via reasonable dispatching of the carbon trading, demand response, and Vehicle-to-grid (V2G) service, the multiple energy demands can be alleviated, ensuring the low carbon and economical operation of the RIES.

Optimal Planning of Intelligent Parking IoT Using Interval Particle Swarm Optimization Algorithm in the Presence of Fuel cell and Electrolyzer as Hydrogen Storage System

Recently, the application of the intelligent parking lot (IPL) in the power market has been exponentially increasing to decrease the greenhouse gasses, the pollution, and to decrease the deviation cost of the energy production based on electric vehicles (EV). IPLs uses charge and discharge features of EVs to exchange the energy in the upstream grid. This paper study on a new interval-analysis based optimal solution of an IPL by considering the interval uncertainties for the price of upstream gird value. The method based on using an interval-based particle swarm optimization algorithm to optimize an interval objective function with lower and upper limitations with a single-valued output. Simulation results of the presented procedure are compared with a deterministic mixed-integer linear programming to show its superiority. The results show that deviation cost has been decreased up to 10.74% while average cost has been raised into 5.17% which demonstrates the methods high performance in decreasing the average cost of IPL and the reliability of the intelligent parking lot in the presence of uncertainties derived from the upstream grid.

Consumer Knowledge about Electric Buses in Underserved Communities in Huntsville, Alabama

The major offshoot of innovation of electric vehicles (EVs) is sustainability. Given the prevailing challenges of carbon emissions from automobiles and the consequent effects on climate change, the introduction of EVs is timely to serve as an effective mechanism for reversing global warming. However, knowledge of EVs is not well distributed among the general population. This study is therefore aimed to analyze the familiarity of Americans with the basic features of EVs. One hundred respondents were selected from two underserved communities in Huntsville, Alabama, using a simple sampling technique. Nonetheless, only 71 residents returned the filled questionnaires. Data were analyzed using frequency counts and descriptive statistics. Findings revealed that the respondents were generally aware of EVs, but EVs are not yet common on their roads. The respondents also signaled low preferences for EVs. The purchase price, charging stations, and limited supply are the candid explanations for why EVs are not a priority in the respondents’ choice of cars. To bolster peoples’ taste toward EVs, the study, therefore, concludes that government authorities and city planners should popularize incentives among the people in underserved communities.

Consumer Motivation by Using Unified Theory of Acceptance and Use of Technology towards Electric Vehicles

The transport sector is the leading source of growing greenhouse gas (GHG) emissions globally. To consider environmental degradation aspects due to transport, electric vehicles (EVs) have the prospect to lead road transport to electric mobility from conventional petroleum vehicles. Despite various eco-friendly benefits, the EV market penetration ratio is very low, especially in developing countries. The primary reason for low penetration is consumer limited motivation and knowledge about the EVs features. This paper uses a unified theory of acceptance and technology (UTAUT) model to assess consumer motivation and environmental knowledge towards EVs. This research used convenience random sampling to collect data and analyzed the results using the Partial Least Squares (PLS) method on the example of 199 respondents from Malaysia. The study results revealed that factors identified in the motivational context significantly influence consumer intentions to purchase EVs. Perceived environmental knowledge and technophilia have been included in UTAUT from a motivational perspective. Furthermore, a significant relationship between effort expectancy, social influence, technophilia, perceived environmental knowledge, and purchase intention towards electric vehicles has been observed, without performance expectancy. The study findings serve to inform policymakers and automakers to formulate effective marketing strategies to enhance consumer motivation, knowledge, and value creation for EVs in a sustainable era. Ultimately, the policies will help to encourage consumers to buy eco-friendly vehicles that will help reduce transport carbon emissions and attain sustainable development goals (SDGs).

An Overview of IoT-Enabled Monitoring and Control Systems for Electric Vehicles

As 5G technology becomes operational and continues to expand, smart cities become a reality that is transforming urban life at a rapid pace. Electric Vehicles (EV) and automated driving, equipped with Battery Energy Storage Systems (BESSs), are expected to dominate public transportation in smart cities. While new technologies can facilitate efficiency and reduce the costs in a city, they can also present challenges. This paper provides an overview of the technical challenges of real-time monitoring and control of Energy Storage Systems (ESSs) for EVs in smart cities. It also covers the Internet-of-the-Things (IoT) technology that can be utilized to address the challenges and improve the efficiency of Battery Management Systems (BMS). Autonomous Wireless Sensor Networks (WSNs) in smart cities provide the infrastructure to support advanced EV features, such as self-parking. IoT sensors can also be used to determine the State-of-Charge (SoC) in EVs by data-driven methods and cloud computing services.

Optimal scheduling of vehicle-to-Grid power exchange using particle swarm optimization technique

Electric vehicles (EV) are the inevitable future of the transportation industry considering the current energy scenario and all the incentives and benefits they provide. Nonetheless, EVs exhibit a novel characteristic of a distributed energy storage device (ESD) owing to their large onboard batteries. This feature of EV can be utilized to provide many ancillary services by the virtue of vehicle-to-grid (V2G) operation. One such service is the minimization of load variation on the grid. Two key challenges, concerning the practical execution of the V2G operation, are the availability of EVs for V2G operation and their ever-changing State of Charge (SOC). Due to the mobility of EVs as a transportation medium, their availability is highly uncertain. This paper addresses these issues by introducing a scheduling scheme for V2G power exchange by considering the stochastic nature of EV grid connectivity. This is done by first developing an optimization algorithm using the particle swarm optimization technique, with input data that best represents the stochastic nature of EV availability. Then, the performance of the algorithm is evaluated by conducting several case studies. The results obtained for various case studies by performing simulations are represented and elaborated. Finally, the statistical analysis of the results signifies that the proposed V2G scheduling scheme can substantially flatten the load profile. Apart from the algorithm itself, another novel nature of the paper lies within the wide range of analyses carried out to study the effect of such scheduling schemes. These studies include the effect on the SOC level of different EVs, the effect on price fluctuation, time complexity analysis of the algorithm, etc.

Incentive regulation for electric vehicles sharing with marketing promotion and technical innovation

Electric vehicles (EVs) sharing with advantages in emission reduction and resources saving is supported by government subsidies. However, many incentive policies overlook that EVs sharing platforms have private information on technical capability and promotion effort levels. Based on incentive regulatory theory, this paper considers the industry feature of EVs sharing and establishes an incentive regulation model to study how the government can effectively incentive platforms to improve promotion effort as well as to make more technical innovation under different scenarios of information, and numerical results are presented to validate the model. The findings may bring useful references for policy makers in the process of designing incentive policy portfolios.

Simultaneous Distributed Generation and Electric Vehicles Hosting Capacity Assessment in Electric Distribution Systems

This work presents a strategy, from the perspective of the distribution system operator (DSO), that aims at simultaneously estimating the maximum penetration levels of renewable-based distributed generation (DG) and electric vehicles (EVs) that can be accommodated into an electric distribution system (EDS). To estimate such capacity, operational resources such as generation curtailment and controllable features of EVs can be managed to ensure the safe operation of the EDS and avoid infeasible operational conditions. Through a multi-period representation, the proposed strategy models the variability in demand consumption and DG power production. In addition, driving patterns of EV owners and energy requirements of EVs, obtained through probability density functions, are incorporated in this representation. Inherently, the problem is represented as an optimization model, and to determine its solution, an algorithm based on the metaheuristics greedy randomized adaptive search and tabu search (GRASP-TS) is developed. The applicability of the planning strategy is assessed on a 33-bus EDS under different test conditions and the numerical results show that higher penetrations of EVs and renewable-based DG can be accommodated without impacting the safe operation of the EDS. The results also demonstrate that by controlling the power draw by EV aggregators, an increase of 9% can be obtained in the DG installed capacity compared to the case of uncontrolled charging of EVs. In addition, the scalability of the proposed approach is studied using two distribution systems, the 83-bus system and the 135-bus system, where the results show that the convergence of the algorithm is achieved in a few iterations.

Parallel Computing and Multicore Platform to Assess Electric Vehicle Hosting Capacity

The impact generated by the massive connection of electric vehicles (EVs) in the distribution networks needs to be evaluated through methodologies that consider the stochastic behavior of the EV connections. These methodologies must allow obtaining representative results of annual simulations, considering different charging scenarios, with high resolution of time steps. This kind of analysis implies a combinatorial explosion of case studies with a high computational cost. This article presents a methodology aimed at reducing the simulation time required to determine the EV hosting capacity (HC), understood as the maximum EV penetration level that a distribution network could host before affecting its operating limits, through the co-simulation of hardware and software. Monte Carlo methods and quasi-static time series simulation concepts are used to model the connection features of EVs. The simulation and implementation are developed using parallel computing, and MATLAB and OpenDSS software. The methodology is tested on the IEEE 123 nodes system which shows the dependence of the HC indicator with the location and type EV chargers. Finally, the benefits of parallel computing in the proposed methodology are exposed to get a significant reduction in the execution time to evaluate the HC in the IEEE 123 nodes system.

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.

Mesoscopic model framework for estimating electric vehicles’ energy consumption

Promoting the usage of electric vehicles (EVs) is viewed as an effective way to develop sustainable transportation. Accurate energy consumption estimation is the key to plan charging infrastructure and provide intelligent service for EV customers, which can further accelerate the popularity of EVs. In this paper, to make a compromise between the model accuracy and data intensity, a mesoscopic model framework for estimating EVs’ energy consumption is established based on real driving condition data. First, the energy consumption characteristics of EVs are analyzed from both statistical and physical standpoints to reveal the energy consumption mechanism of EVs. Then, with the explicit consideration of the regenerative braking under decelerating mode, which is a distinctive feature of EVs, the required regenerative braking power is given special treatment and is incorporated into the modelling framework. Finally, an energy consumption factor model based on the average travel speed is established to associate the finer scale energy consumption on a link-by-link level. The proposed model is validated against with field measurements, and the results indicate the model has a good predictive ability. This study provides a more complete manner for considering unique energy consumption characteristics in EVs’ energy consumption estimation under real-world driving conditions.

Optimal Decision-Making to Charge Electric Vehicles in Heterogeneous Networks: Stackelberg Game Approach

Electric vehicles (EVs) are designed to improve the efficiency of energy and prevent the environment from being polluted, when they are widely and reasonably used in the transport system. However, due to the feature of EV’s batteries, the charging problem plays an important role in the application of EVs. Fortunately, with the help of advanced technologies, charging stations powered by smart grid operators (SGOs) can easily and conveniently solve the problems and supply charging service to EV users. In this paper, we consider that EVs will be charged by charging station operators (CSOs) in heterogeneous networks (Hetnet), through which they can exchange the information with each other. Considering the trading relationship among EV users, CSOs, and SGOs, we design their own utility functions in Hetnet, where the demand uncertainty is taken into account. In order to maximize the profits, we formulate this charging problem as a four-stage Stackelberg game, through which the optimal strategy is studied and analyzed. In the Stackelberg game model, we theoretically prove and discuss the existence and uniqueness of the Stackelberg equilibrium (SE). Using the proposed iterative algorithm, the optimal solution can be obtained in the optimization problem. The performance of the strategy is shown in the simulation results. It is shown that the simulation results confirm the efficiency of the model in Hetnet.