Electric Vehicle Users Research Articles

Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development

ABSTRACT This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm.

Numerical Investigation of Thermal Management of a Large Format Pouch Battery Using Combination of CPCM and Liquid Cooling

As electric vehicles (EVs) gain market dominance, ensuring safety during the battery usage is crucial. This paper presents a new thermal management approach to address the battery heat accumulation challenge through a novel combination of composite phase change material (CPCM) with liquid cooling systems. An optimised hybrid cooling model is developed to evaluate the proposed battery thermal management system (BTMS) under high-temperature and high-power conditions. Benchmark studies are conducted to assess the impact of inlet position, inlet flow rate, and flow channel distribution on the cooling performance to achieve a uniform temperature distribution within the battery. The optimised BTMS, consisting of a five-cell battery pack, demonstrates a maximum temperature of 41.15 °C and a temperature difference of 4.89 °C in a operating condition at 36 °C with a discharge rate of 3 C. The BTMS outperforms the initial model, reducing the maximum temperature by 1.5%, temperature difference by 5%, and liquid fraction by 13%, with a slight (1.3%) increase in weight. The cooling performance is most efficient at a liquid flow rate of 0.1 m/s, minimising energy consumption. The proposed BTMS with CPCM-3 is also sufficient enough to keep the battery pack under a thermal runaway event. Overall, the theoretical simulation highlights the BTMS’s ability to effectively control battery temperatures and temperature differences, ensuring safe operation during high-temperature and high-power conditions in practical EV usage.

Dynamic pricing strategy for electric vehicle charging stations to distribute the congestion and maximize the revenue

Electric vehicle (EV) charging station (CS) congestion is highly dependent on the EV owner’s behavior and their selected CS as charging choice. A fixed pricing strategy causes some CSs to be congested with EVs waiting in line to charge, while there are some other CSs with available electric plugs, which adversely affects both charging station operator (CSO) revenue and EV users’ welfare. To solve this problem, this paper presents a dynamic pricing strategy aimed at conducting EVs from congested CSs to the uncongested ones through controlling the charging prices of CSs at different times. The problem is formulated as a scenario-based stochastic optimization with the objective of maximizing overall revenue of the CSO. Moreover, an attraction function model is developed to quantify the charging choice of the EV owners by considering the effective parameters of CSs in EV charging choice decisions. QGIS software is used in this work to formulate a realistic modeling for CS locations and EV routes to calculate the distances from EVs to CSs. Three scenarios are designed to evaluate the performance of the proposed framework and to compare the results with the fixed pricing approach. The results indicate that the proposed dynamic pricing strategy mitigates the congestion of CSs while facilitating an increased number of charged EVs up to 48% as well as increasing the overall revenue of the CSO.

Locating and sizing charging station in multi-period to promote electric vehicles adoption in urban areas

The layout of public fast charging stations (CSs) often fails to meet the charging demand of electric vehicle (EV) users, resulting in charging anxiety. To address this issue and promote the widespread use of EVs in urban areas, this paper presents a multi-period locating and sizing optimization model under dynamic demand. The model incorporates GPS trajectory extraction data to identify potential charging demand points and considers the mobile load characteristics of EVs for fusion modeling. A multi-period change formula is proposed to reflect the interaction between public fast CSs and EV market penetration, incorporating charging opportunity and waiting time satisfaction as measures of charging service quality. To solve the model, a heuristic algorithm combining genetic algorithm (GA) and k-medoids clustering algorithm is designed. We apply the model to an actual case of Shenzhen and find that increasing investment in charging infrastructure can boost EV market penetration in the later planning period. Moreover, the gap between charging opportunity and waiting time satisfaction tends to narrow as the planning progresses. Our findings highlight the importance of optimizing the location and size of public fast CSs to improve the charging service quality and promote the adoption of EVs.

A reliable optimal electric Vehicle charging stations allocation

This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. In the second stage, a multi-objective problem is formulated to minimize power losses, voltage deviation, and enhance system reliability. To achieve this, charging station zones are determined by clustering the transportation network into groups, and statistical simulations of EV user behaviour are conducted numerous times based on probability. To obtain the optimal solution for the investigated system, which consists of a 25-node transportation network interconnected with the IEEE 69 bus distribution network, the Galaxy Gravity Optimization technique is applied. The results obtained from this study illustrate how the placement of charging stations influences reliability indices, power loss, and voltage deviation.

Comparing Mini and full-size electric vehicles in China: Evidence from user heterogeneity and satisfaction asymmetry

Improving user satisfaction with electric vehicles (EVs) is crucial for their widespread diffusion and the development of sustainable transportation. Although numerous studies have investigated the impact of EV service factors on satisfaction, the nonlinear and asymmetric effects have been largely neglected. This study aims to fill this gap by comparing the factor differences in user perceptions, satisfaction asymmetries, and improvement priorities among heterogeneous adopters within two EV segments: Mini EVs and full-size EVs. Two common methods, the regression with dummy variables (RDV) and the Importance Grid (IG), are utilized to examine satisfaction asymmetry and determine which one is more appropriate for describing the relationship between service factors and overall satisfaction. Empirical evidence from EV segments in China indicates that the RDV method is more suitable than the IG method for characterizing factor attribute and asymmetry. Female, multi-vehicle, and daily users report higher levels of satisfaction and importance for service factors compared to male, single-vehicle, and weekly users. Based on the RDV method, the results emphasize the importance of improving charging satisfaction to enhance the overall satisfaction of Mini EV adopters, particularly for those who have multiple vehicles and frequent travel demands. For males and daily users of full-size EVs, it is critical to enhance vehicle performance and technology. This study not only expands the limited application of satisfaction asymmetry to the transportation field but also provides policy implications for Mini EV diffusion.

Enhancing the utilization of renewable generation on the highway with mobile energy storage vehicles and electric vehicles

The growth of electric vehicles (EVs) and renewable generation on the highway will magnify the imbalance between the energy supply and traffic electricity demand. Reshaping EV charging loads to address the above imbalance is challenging. Scheduling mobile energy storage vehicles (MESVs) to consume renewable energy is a promising way to balance supply and demand. Therefore, leveraging the spatiotemporal transferable characteristics of MESVs and EVs for energy, we propose a co-optimization method for the EV charging scheme and MESV scheduling on the highway, considering locational marginal price, renewable generation, and EV users’ benefit. Furthermore, by building a time–space–energy model for MESVs and EVs, we develop a bi-level optimization model to simulate the interaction between EV users and the highway operator (HO). In detail, the upper-level model optimizes EV charging pricing and MESV scheduling strategies to maximize HO profit, while the lower-level model considers EV users’ characteristics to minimize their charging-parking costs. The column-and-constraint generation algorithm is adopted due to the objective conflict between two levels. The study shows a 32.0% increase in renewable energy utilization and a reduction of 3190.1 kWh in electricity purchased from the main grid, promoting environmental and economic sustainability for the HO.

RFID Based EV Charging Station (SOLAR)

As the number of Electric Vehicles (EVs) proliferates, the significance of charging infrastructure escalates, necessitating solutions that balance the demands of the local distribution grid and EV users. This paper proposes the integration of an RFID system for user identification and charging authorization within a smart charging infrastructure, facilitating charge monitoring and control. The RFID technology offers a cost-effective means to identify and authorize vehicles for charging, ensuring efficient charging operations while adhering to grid constraints and fulfilling the requirements of EV drivers. Charging protocols are voltage-based, and the system enables remote monitoring of charging levels via an Internet of Things (IoT) enabled server, providing accessibility from any location at any time.

Maximizing EV profit and grid stability through Virtual Power Plant considering V2G

The electrification of transportation through the widespread adoption of electric vehicles (EVs) has raised substantial concerns within the realm of power grid operations. This concern predominantly stems from the elevated electricity demand brought about by the surging population of EVs, consequently exerting strain on the power grid infrastructure which can be reduced with vehicle-to-grid (V2G) technology integration. To address this issue, this paper delves further into the realm of grid integration by introducing a Virtual Power Plant (VPP) concept to enhance the synergy between EVs and power grid. This study aims to compare different realistic objectives, ranging from total active power loss and voltage drop minimization to EV profit maximization and then optimize the balance between the distribution grid power quality and VPP profit through bi-level modeling. The presented model is devised as mixed-integer quadratically constrained programming (MIQCP) and incorporates Temporal Convolutional Network (TCN) based forecasting to handle the uncertain behavior of the residential loads using historical data. The experiments are conducted in IEEE 33-Bus and real-world 240-Bus distribution networks. The results indicate that enabling bidirectional power flow between the grid and VPP can yield significant profits for EV users while only marginally impacting the active power loss, approximately around 5%. This validation underscores how V2G not only presents various advantages for power system operators but also benefits EV users simultaneously.

Refueling convenience and range satisfaction in electric mobility: Investigating consumer willingness to use battery swap services for electric vehicles

Battery swap service (BSS) is a game-changing innovation for achieving electric vehicle (EV) refueling, driving range satisfaction, and convenience. BSS is an avenue for swapping a worn-out EV battery for a pre-charged one, eliminating the long charging times of batteries. However, consumer adoption of BSS is overlooked, lacking empirical support. BSS is encouraged as it makes clean ‘fuel’ readily available for EV use. Consumer willingness to patronize BSS is pivotal to EV usage, sustainable transport, and pollution mitigation. Consequently, the innovation diffusion theory is extended with attitude, perceived risk, green self-identity, and task-technology fit to investigate consumer willingness to use BSS. Utilizing a partial least squares-structural equation model (PLS-SEM), 618 responses were analyzed. The outcome revealed that relative advantage, compatibility, attitude, green self-identity, and task-technology fit positively impacted willingness to use BSS. Perceived risk had a significant adverse effect on willingness to use BSS. Attitude mediated the impact of relative advantage and task-technology fit. Also, perceived risk negatively affected attitude. Further, ANOVA analysis confirmed the impact of consumer demography on the willingness to use BSS. Policies involving swapping standardization and synchronization, public sensitization, monetary incentives, and battery performance enhancement are recommended for enhancing BSS acceptance, usage, and proliferation.

Dynamic charging as a complementary approach in modern EV charging infrastructure

Whether the future of transportation is going to be electric or not is no longer a question. Electric vehicles (EVs) offer several benefits toward global sustainability. However, without a variety of charging infrastructures that cover diverse forthcoming charging needs, the speed of vehicle electrification may be slow and limited. In this study, we investigate the synergistic benefits of traditional charging stations and an emerging alternative, i.e., wireless dynamic charging roads, focusing on their impact on the daily commute of EV users. We center our evaluation on quantifiable metrics, chiefly time and convenience, and deploy computer simulations utilizing authentic transportation datasets from New York City, USA. Our findings underscore that integrating both charging stations and charging roads can substantially alleviate detours for EV users and remarkably reduce additional charging time. Our research provides evidence to encourage researchers, EV manufacturers, urban infrastructure planners, and policymakers to explore future charging infrastructures for EVs, with a notable emphasis on wireless dynamic charging roads.

Optimizing smart microgrid performance: Integrating solar generation and static VAR compensator for EV charging impact, emphasizing SCOPE index

The rapid global increase in electric vehicle (EV) usage, driven by its low CO2 emissions, uncomplicated maintenance, and minimal operating costs, has prompted extensive research in the field of electric vehicle charging station (EVCS). The integration of EVCS into the current distribution grid poses challenges due to potential power losses and voltage variations beyond acceptable limits. This complexity is heightened by the growing penetration of randomly dispersed solar-based distributed generation (SDG) and battery energy storage system (BESS). To address these challenges, distribution static VAR compensator (DSVC) systems have been introduced, offering benefits such as enhanced power transfer capacity, improved voltage regulation, and increased system security without requiring extensive infrastructure upgrades. This study offers SCOPE, a novel multi-objective framework that unifies the optimization goals of minimizing real power loss, lowering bus voltage variation, maximizing system voltage stability, minimizing system operating costs, and mitigating CO2 emissions. The EVCS problem is optimized within this multi-objective framework utilizing an improved bald eagle search algorithm (IBESA). The proposed model accounts for vehicle to grid (V2G) capabilities and the actual driving patterns of users over a 24-h horizon. The formulation of a smart microgrid (SMG) structure is based on modifying the standard IEEE 33-bus test radial distribution network (RDN), comprising three interconnected SMGs serving residential, commercial, and industrial users. The optimization approach based on IBESA is utilized to optimize both the siting and capacity of EVCS as well as renewable energy sources (RESs). The findings show that SDG and DSVC are effective at lowering the SCOPE index, highlighting the advantages of the suggested approach.

Rush to Charge, Dead to Drive: Application of Deadline Rush Model to Electric Vehicle User's Charging Behavior.

This work aims to estimate the portion of electric vehicle (EV) users who exhibit procrastination-like behavior, almost equivalent to an "empty" battery, before they decide to charge their vehicles. There is a human tendency to procrastinate when a deadline approaches. Human behavior in the presence of deadlines has been studied in different fields to evaluate individuals' performance or organizational efficiency and effectiveness. However, this phenomenon has not been investigated among EV users. This study explores users' procrastination-like behavior among 69 Rhode Island public charging stations' data representing 70,611 charging events. The Deadline Rush Model is incorporated to model frequent users' charging profiles. To conduct a robust estimation, the Bayesian Mixture Model is implemented. With the selection of an informative prior, the Bayesian Mixture Model estimated that almost one-third of frequent users procrastinate charging. The majority of procrastination-like users have small battery sizes. Although procrastination-like users need to charge when they arrive at a location, that might not necessarily be true for a plug-in hybrid; thus, systematically, they can clog the system for other users whose needs are more pressing. Understanding unique and unexplored charging behaviors among EV users is beneficial to EV infrastructure stakeholders in reducing the adoption threshold by providing a reliable and ubiquitous charging network. The findings identify a different kind of demand on the EV infrastructure than previously modeled and can directly influence future decision-making criteria in terms of planning to optimize to accommodate EV drivers with different charging behaviors.

SPAGHETTI: a synthetic data generator for post-Covid electric vehicle usage

The Covid-19 pandemic has resulted in a permanent shift in individuals’ daily routines and driving behaviours, leading to an increase in remote work. There has also been an independent and parallel rise in the adoption of solar photovoltaic (PV) panels, electrical storage systems, and electric vehicles (EVs). With remote work, EVs are spending longer periods at home. This offers a chance to reduce EV charging demands on the grid by directly charging EV batteries with solar energy during daylight. Additionally, if bidirectional charging is supported, EVs can serve as a backup energy source day and night. Such an approach fundamentally alters domestic load profiles and boosts the profitability of residential power systems. However, the lack of publicly available post-Covid EV usage datasets has made it difficult to study the impact of recent commuting patterns shifts on EV charging. This paper, therefore, presents SPAGHETTI (Synthetic Patterns & Activity Generator for Home-Energy & Tomorrow’s Transportation Investigation), a tool that can be used for the synthetic generation of realistic EV drive cycles. It takes as input EV user commuting patterns, allowing for personalised modeling of EV usage. It is based on a thorough literature survey on post-Covid work-from-home (WFH) patterns. SPAGHETTI can be used by the scientific community to conduct further research on the large-scale adoption of EVs and their integration into domestic microgrids. As an example of its utility, we study the dependence of EV charge state and EV charging distributions on the degree of working from home and find that there is, indeed, a significant impact of WFH patterns on these critical parameters.

Multilayered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. Lastly, case studies and a comparative analysis prove the efficacy of the proposed framework.

Simulation and Fabrication of Hybrid Electric Bicycle with Multi Charging Mode

The use of electric vehicles actively contributes to environmental conservation. Lack of EV charging infrastructure is one of the most common challenges associated with the electric vehicles. The conventional method for the electric propulsion system deals with the single mode of charging techniques. The alternative battery charging techniques helps to enhance the usage of electric vehicles. In this work hybrid electric bicycle is designed with multi charging method. In addition to the conventional wall charger, a solar panel and dynamo is used to charge the battery. Increasing the performance and efficiency by means of BLDC motor. The entire propulsion system is connected with the PI controller which calculate the speed and relevant voltage needed for the drive. The performance of the proposed method is evaluated through simulation using MATLAB/ Simulink software.

Edge computing‐based optimal dispatching of charging loads considering dynamic hosting capacity

AbstractOwing to the rapid increase in electric vehicle integration and the uncoordinated charging behaviour of electric vehicles, the overloading risk of distribution transformers has deteriorated. This impact caused by large‐scale electric vehicle integration can be effectively reduced through the orderly guidance of electric vehicle charging behaviours. Here, a dispatching strategy for charging loads is proposed to address the problems of the uncoordinated charging demand in electric vehicles and overloading risk of distribution transformers in residential areas. First, an edge‐side dynamic index of the electric vehicle hosting capacity is proposed to guide the optimal dispatching of charging loads. Subsequently, an optimal dispatching model of the charging loads is established based on edge computing. The edge‐side dispatching strategy for the charging loads is then further updated considering the participation willingness of electric vehicle users. Finally, the effectiveness of the proposed control strategy is validated using a modified residential distribution network in Tianjin. The results show that the proposed strategy can effectively decrease the overloading risk of distribution transformers while realizing the efficient operation of electric vehicles on the edge side.

Charging and Discharging Optimization of Vehicle Battery Efficiency for Minimizing Company Expenses Considering Regular User Travel Habits

With the increasing popularity and development of electric vehicles, the demand for electric vehicle charging is also constantly increasing. To meet the diverse charging needs of electric vehicle users and improve the efficiency of charging infrastructure, this study proposes an optimization strategy for electric vehicle charging and discharging. This method considers both the user’s travel mode and the operational efficiency of the charging pile. Firstly, a probability model based on travel spatiotemporal variables and Monte Carlo algorithm were used to simulate the travel trajectory of electric vehicles, providing a data foundation for optimizing the charging and discharging schemes of electric vehicles. Then, with the dual objective of minimizing the operating costs of charging piles and user charging costs, a linear programming model was constructed to optimize the charging and discharging strategies of electric vehicles. Finally, the model was validated using an apartment building as an example. The results indicate that, under the normal travel habits of users, with the goal of minimizing company expenses, the annual cost of the company reaches its minimum at a certain number of charging piles. When the cost of electric vehicle users dominates the objective function, they will pay more attention to battery degradation, significantly reducing their willingness to participate in discharge.

Blockchain-enabled electric vehicle orderly charging optimization

The increasing prevalence of electric vehicles (EVs) urges charging station operators (CSOs) to expand their networks. However, currently CSOs operate independent EV charging service platforms separately, which poses inconvenience for EV users and hinders the efficient utilization of charging infrastructure. This paper introduces a consortium blockchain-enabled EV charging service platform (CB-EVCSP), which leverages novel orderly charging guidance models (OCGMs) encoded as smart contracts to integrate and coordinate the efforts of CSOs. The CB-EVCSP not only mitigates trust and security concerns but also enhances the utilization of charging facilities. Both multinomial logit (MNL) and mixed MNL models are developed to characterize the interaction between charging station recommendations and EV users’ choice behaviors, where a coefficient of variation based fairness criterion is used to ensure distribution fairness. We provide equivalent reformulations and design efficient constraint generation algorithms. Numerical experiments are conducted to validate the effectiveness of the proposed models and algorithms.

Insights into Household Electric Vehicle Charging Behavior: Analysis and Predictive Modeling

In the era of burgeoning electric vehicle (EV) popularity, understanding the patterns of EV users’ behavior is imperative. This paper examines the trends in household charging sessions’ timing, duration, and energy consumption by analyzing real-world residential charging data. By leveraging the information collected from each session, a novel framework is introduced for the efficient, real-time prediction of important charging characteristics. Utilizing historical data and user-specific features, machine learning models are trained to predict the connection duration, charging duration, charging demand, and time until the next session. These models enhance the understanding of EV users’ behavior and provide practical tools for optimizing the EV charging infrastructure and effectively managing the charging demand. As the transportation sector becomes increasingly electrified, this work aims to empower stakeholders with insights and reliable models, enabling them to anticipate the localized demand and contribute to the sustainable integration of electric vehicles into the grid.