Electric Vehicle Users Research Articles

Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands

The gradual replacement of conventional-fuel vehicles by electric vehicles (EVs) in recent years provides a growing incentive for the collaborative optimization of power distribution networks (PDNs) and urban transportation networks (UTNs). However, the implementation of a centralized optimization model for PDNs and UTNs is currently unrealistic due to the requirement for establishing information privacy barriers between the two independently operated networks. The present work proposes a predict-then-optimize (PTO) framework that combines Spatiotemporal Graph Attention Network (STGAT) with enhanced queueing theory to achieve accurate prediction of EV charging demands. Building upon this, it efficiently coordinates optimization between the PDN and UTN, while preserving the information privacy of both independently operated networks. Specifically, the proposed STGAT model predicts vehicle flow by simultaneously exploiting the dynamic temporal and spatial dependencies in the historical traffic data of the target UTN. It then converts the predicted flow into spatiotemporal EV charging demands using an enhanced queuing model that considers the service capacity constraints of charging stations (CSs) and the behavior of EV users. Subsequently, the predicted EV charging demands are integrated into a multi-period PDN scheduling model. The results of numerical computations based on an IEEE 33-bus PDN and real-world traffic flow datasets demonstrate that the scheduling results provided by the proposed approach differ by only 0.5% compared to results obtained when applying actual EV charging demands.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

Electric Vehicle Charging Load Demand Forecasting in Different Functional Areas of Cities with Weighted Measurement Fusion UKF Algorithm

The forecasting of charging demand for electric vehicles (EVs) plays a vital role in maintaining grid stability and optimizing energy distribution. Therefore, an innovative method for the prediction of EV charging load demand is proposed in this study to address the downside of the existing techniques in capturing the spatial–temporal heterogeneity of electric vehicle (EV) charging loads and predicting the charging demand of electric vehicles. Additionally, an innovative method of electric vehicle charging load demand forecasting is proposed, which is based on the weighted measurement fusion unscented Kalman filter (UKF) algorithm, to improve the accuracy and efficiency of forecasting. First, the data collected from OpenStreetMap and Amap are used to analyze the distribution of urban point-of-interest (POI), to accurately classify the functional areas of the city, and to determine the distribution of the urban road network, laying a foundation for modeling. Second, the travel chain theory was applied to thoroughly analyze the travel characteristics of EV users. The Improved Floyd (IFloyd) algorithm is used to determine the optimal route. Also, a Monte Carlo simulation is performed to estimate the charging load for electric vehicle users in a specific region. Then, a weighted measurement fusion UKF (WMF–UKF) state estimator is introduced to enhance the accuracy of prediction, which effectively integrates multi-source data and enables a more detailed prediction of the spatial–temporal distribution of load demand. Finally, the proposed method is validated comparatively against traffic survey data and the existing methods by conducting a simulation experiment in an urban area. The results show that the method proposed in this paper is applicable to predict the peak hours more accurately compared to the reference method, with the accuracy of first peak prediction improved by 53.53% and that of second peak prediction improved by 23.23%. The results not only demonstrate the high performance of the WMF–UKF prediction model in forecasting peak periods but also underscore its potential in supporting grid operations and management, which provides a new solution to improving the accuracy of EV load demand forecasting.

Research on Sustainable Form Design of NEV Vehicle Based on Particle Swarm Algorithm Optimized Support Vector Regression

With the growing emphasis on eco-friendly and sustainable development concepts, new energy vehicles (NEVs) have emerged as a popular alternative to traditional fuel vehicles (FVs). Due to the absence of an internal combustion engine, electric vehicles (EVs) do not require a front air intake grille, allowing for a more minimalist and flexible design. Consequently, aligning EV styling with users’ visual cognition and emotional perception is a critical objective for automakers and designers. In this study, we establish the mapping relationship between users’ emotional cognition and NEV styling design based on experimental data. We introduce Particle Swarm Optimization Support Vector Regression (PSO-SVR) into the perceptual engineering (KE) research process to predict user emotions using Support Vector Regression (SVR). To optimize the three hyperparameters (penalty coefficient C, RBF kernel function parameter γ, and insensitivity loss coefficient ε) of the SVR model, we utilize the Particle Swarm Optimization (PSO) algorithm. The results indicate that the proposed PSO-SVR model outperforms traditional SVR and BPNN models in predicting NEV user emotions. This model effectively captures the nonlinear relationship between battery electric vehicle (BEV) morphological features and users’ emotional cognition, providing a novel method for enhancing NEV design. The results of this research are expected to drive design innovation and technological advancement in the new energy vehicle industry, contributing to the achievement of the ambitious goal of global eco-friendliness and sustainable development.

What do battery electric vehicle (BEV) users think of the charging infrastructures launched in expressway service areas? Evidence from China

ABSTRACT The emergence of electric vehicles (EV) presents new opportunities for transportation decarburization and sustainable transportation in cities worldwide. However, previous research has primarily focused on EV charging infrastructures within urban areas, with limited attention to those launched in expressway service areas. Addressing this gap is crucial in alleviating EV users’ range anxiety on expressway journeys. This study investigates the BEV users’ reuse behavior toward charging infrastructures on expressways. Focusing on the BEV users who had used the charging infrastructures on the expressway, the structural equation modeling and the multi-group analysis are employed to reveal the effect of psychological factors on BEV users’ reuse intention and explore the heterogeneity across different socio-demographic groups. Results reveal that Attitude and Subjective Norm drive the reuse intention. Perceived Risk has an indirect negative effect on reuse intention. Attitude has a more significant effect on reuse intention among elder users, high-frequency users, and low remaining State of Charge (SOC) users. This paper offers new insights for charging infrastructures’ planning and operation in expressway service areas.

"Next-Generation Four-Port Isolated Pv Ev Charger with Integrated Ac and Dc Charging Solutions"

This research introduces a pioneering 4-port isolated photovoltaic (PV) based electric vehicle (EV) charger designed to support both alternating current (AC) and direct current (DC) charging. The integration of four ports enhances charging flexibility and efficiency, catering to the evolving needs of electric vehicle users. The system harnesses solar energy through photovoltaic panels, ensuring a sustainable and eco-friendly charging solution. The isolation mechanism ensures safe and reliable charging by preventing ground loops and minimizing electrical hazards. By accommodating both AC and DC charging, the charger adapts to various EV models, offering a versatile and future-proof solution. This research contributes to the advancement of renewable energy integration into transportation infrastructure, promoting cleaner and more resilient mobility options in the growing electric vehicle market. DOI: https://doi.org/10.52783/pst.798

Prize-collecting Electric Vehicle routing model for parcel delivery problem

Electric Vehicle (EV)-based parcel delivery is getting much more popular these days due to EVs being eco-friendly and sustainable. One well-known problem in the literature to study last-mile delivery via EVs is the Electric Vehicle Routing Problem (EVRP). One limitation of EVRP is that it assumes all customers must be visited directly by EVs. This forces companies to purchase or utilize additional EVs, increasing operational costs for EV utilization and routing. To overcome this limitation, the present work introduces the Prize-collecting EVRP with Time Windows (PC-EVRP-TW), utilizing multiple EVs for the home-delivery service of high-priority customers (customers with high loyalty or premium memberships), leaving the rest for the next day or outsourcing to a third-party shipper. It also ensures a minimum number of served demands to maintain service quality. PC-EVRP-TW is modeled by mixed-integer linear programming and solved by a problem-specific hybrid metaheuristic aiming to minimize EVs’ route and usage cost and maximize collected profits (prizes) while satisfying EVs’ load and battery capacity, task completion, and time window constraints. Analyses were performed on the optimal solution of PC-EVRP-TW. The results show that the company could achieve significant route cost savings, up to 22% compared to EVRP-TW. Additionally, remarkable reductions in EV usage costs, 6%, 16%, and 18%, were observed for various-size instances of PC-EVRP-TW. These findings highlight the potential for practitioners and managers to realize substantial cost savings and optimize EV usage by selectively serving a prioritized subset of customers daily, particularly when facing high utilization costs and the inability to serve all customers directly.

Am I really willing to use my electric vehicle sustainably? A study on the charging preferences of electric vehicle users

As, in most countries, electricity cannot be efficiently and practically stored at a systemic level, charging EVs during peak hours implies that additional energy has to be generated (relying almost exclusively on fossil sources) to cover the additional demand during that time window. This article reports the results of an SP-study, in which EV-owners were confronted with the option of charging their EVs at home (for a fixed known price) or at a publicly accessible charging station with charging price variability (as well as other features of EV charging). The results of the behavioral experiment show that EV-users exhibit a high willingness to accept alternative compensations for not charging EVs during peak-hours and that small monetary incentives as well as shorter access times, a guaranteed charging place, or fast charging could all efficiently promote a shift in the charging patterns toward a more sustainable behavior.

Electric vehicle charging load prediction based on graph attention networks and autoformer

AbstractWith the widespread popularity of electric vehicles in the domestic market, large‐scale electric vehicle user data has been collected and stored. Highly accurate user‐level charging load prediction has a wide range of application scenarios and great business value. However, most existing EV load prediction methods are modelled from the charging station perspective, ignoring the user's travel habits and charging demand. Therefore, this paper proposes a temporal spatial neural network based on graph attention and Autoformer to predict electric vehicle charging load. Firstly, the urban map of Wuhan is rasterized. Then, driving and charging data from the user level are aggregated into the raster module according to the time sequence, and a spatio‐temporal graph data structure of user travel trajectory is constructed. Finally, the temporal spatial neural network is used to construct the EV charging load prediction model from the user's perspective. The experimental results show that, compared with other baseline prediction methods, the proposed method effectively improves the accuracy of the EV charging load prediction model by fully exploiting the distribution of EV user clusters in time and geographic space.

Cooperative V2G-enabled vehicle-to-vehicle sharing in energy and reserve markets: A coalitional approach

The dynamics of electric vehicles (EVs) charging significantly influence the current power system dynamics. However, with advancements in battery technology and charging infrastructure, EVs can also serve as energy storage systems through vehicle-to-grid (V2G) technology. This opens up possibilities for novel approaches, such as a coalition-based V2G-enabled vehicle-to-vehicle (V2V) energy and reserve sharing mechanism. Unlike traditional transactive energy models that often under-utilize EVs due to mismatches with smaller renewable outputs and peak loads, the proposed cooperative V2V sharing mechanism aims to maximize the use of EVs’ charging and discharging capabilities. It forms a grand coalition of EV users to optimize energy and reserve market participation. The model introduces mathematical formulations to describe how EVs collaborate in both energy and reserve markets. It ensures fairness and stability in pay allocations among users within the cooperative framework. The theoretical foundation includes proof of balance in the coalition approach and a two-stage imputation method to achieve fair and optimal payoff distribution. This minimizes incentives for users to defect from the coalition, ensuring stability. To address scalability challenges inherent in coalition formation problems, a decomposition algorithm is proposed. This algorithm enhances efficiency in solving problems that grow exponentially with the number of users. The effectiveness and superiority of this approach are validated through applications to various community energy systems of different sizes. The proposed plan can increase 22.21% of the total payoff in 10-users case and 22.39% in 30-users case. The computation time scales near-linearly with the number of users, although the computation scales exponentially with it. These demonstrations highlight its capability in modeling and solving complex energy sharing scenarios.

Driving Change to Electric Vehicles: A Comprehensive Study of Electric Vehicle User Segmentation and Charging Behaviors

Driving Change to Electric Vehicles: A Comprehensive Study of Electric Vehicle User Segmentation and Charging Behaviors

An Investigation of factors Influencing electric vehicles charging Needs: Machine learning approach

Decarbonization of the world is greatly contributed to by the recent technological advancements that have fostered the development of electric vehicles (EVs). The EVs relieve transportation dependence on natural fossil fuels as an energy source. More than 50 % of the petroleum products produced worldwide are estimated to be used in the transportation sector, accounting for more than 90 % of all transportation energy sources. Consequently, studies estimate thatthe transportation sector produces about 22 % of global carbon dioxide emissions, posingsignificant environmental issues. Thus,using EVs, particularly on road transport, is expected to reduce environmental pollution. To accelerate EV development and deployment, governments worldwide invest in EV development through various initiatives to make them more affordable. This research aims to investigate the changing needs of EV users to establish factors to be considered in the selection of charging demands using machine learning, usingan extreme gradient boosting model. The model reached high accuracy, with an R2-Score of 0.964 to 1.000 across all predicted needs. The model performance is greatly affected by age, median income, education, and car ownership. High values of people with high income, high education, and age between 35–54 years show a positive contribution to the model’s performance, contrary to those with 65+, low income, and low education attainment. The outcomes of this research document factors that influence EV charging needs; therefore, it provides a basis for decision-makers and all stakeholders to decide where to locate EV charging stations for usability, efficiency, sustainability, and social welfare.

A heuristic-fuzzy improved virtual synchronous generator control strategy for charging station frequency regulation

To make use of charging station (CS) frequency regulation (FR) capability to get a better FR effect, a CS virtual synchronous generator (VSG) frequency control strategy based on the heuristic-fuzzy logic controller (FLC) is proposed in this paper. First, a CS VSG control architecture with heuristic-FLC is designed. Second, considering electric vehicle (EV) users' charging demands, a CS FR capability evaluation model based on the constant current-constant voltage (CC-CV) charging process and a V2G power response model based on frequency up/down-regulation participation factor are designed. Furthermore, a heuristic-FLC optimized by genetic algorithm (GA) is designed to improve the FR effect of the VSG control strategy. Simulation results show that the proposed strategy can make full use of the CS FR capability and get a better FR effect. In addition, the V2G power response model proposed in this paper can achieve the orderly responses of EVs in a CS under different scenarios.

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

PV integrated multi-leg powered constant quasi-dynamic charging system for low-speed vehicles

The global adoption of electric vehicles (EVs) is gaining momentum as countries strive to achieve sustainable development goals. Establishing charging infrastructure is of paramount importance to promote increased EV usage. The cost and weight of the batteries are factors that reduce the sales volume and popularity of EVs. A dynamic charging system has also been developed to enhance the driving range of EVs and mitigate the need for heavy storage requirements. The energy demand of charging infrastructure is increasing day by day. Many countries are installing solar arrays along the roadside to meet the power demand of highway lighting applications and achieve sustainable development goal (SDG) 7. To further enhance this system, this manuscript proposes integrating PV technology with the dynamic charging system. The PV arrays and energy storage system (ESS) collaborate to power the dynamic charging system. A multi-leg inverter is employed to energize the charging couplers, while a resonant network improves the power transfer capability of the couplers. On the receiver side, a resonant network ensures the delivery of constant voltage and constant current by tuning the network topology. The 3.3 kW five-legged inverter is developed to energize the four double-D-shaped charging couplers. The common DC bus delivers 350 V to the inverter, and the charging system delivers 350 V, 8.85 A to the EV batteries.

Are Residents Willing to Pay for Electric Cars? An Evolutionary Game Analysis of Electric Vehicle Promotion in Macao

This study uses an evolutionary game model to analyze the interplay between Macao residents’ willingness to purchase electric vehicles (EVs) and the government’s promotion strategies. It assesses the effectiveness of incentives like tax exemptions and price reductions. Despite these initiatives, challenges such as high initial costs, limited vehicle range, and long charging times continue to hinder the widespread adoption of EVs in Macao. Government subsidies increase the appeal of EV purchases, but if not managed carefully, they risk creating dependency. Simulation analysis shows that an active purchasing strategy by Macao residents can stabilize the model’s development. However, to achieve wider market penetration and environmental goals, this study highlights the need for the government to align subsidies with market dynamics and for residents to increase their environmental awareness. This study outlines actionable strategies for policy-makers, emphasizing the importance of infrastructure improvements and financial incentives in promoting electric mobility. Policy-makers should focus on expanding the network of charging stations to enhance the convenience and viability of EV usage. Additionally, implementing targeted financial incentives, such as subsidies or tax breaks, can lower the cost barrier for potential EV buyers, thereby increasing the attractiveness and adoption of electric vehicles.

Experimental Investigation of a Distributed Architecture for EV Chargers Performing Frequency Control

The demand for electric vehicle supply equipment (EVSE) is increasing because of the rapid shift toward electric transport. Introducing EVSE on a large scale into the power grid can increase power demand volatility, negatively affecting frequency stability. A viable solution to this challenge is the development of smart charging technologies capable of performing frequency regulation. This paper presents an experimental proof of concept for a new frequency regulation method for EVSE utilizing a distributed control architecture. The architecture dynamically adjusts the contribution of electric vehicles (EVs) to frequency regulation response based on the charging urgency assigned by the EV users. The method is demonstrated with two Renault ZOEs responding to frequency fluctuation with a combined power range of 6 kW in the frequency range of 50.1 to 49.9 Hz. The results confirm consistent power sharing and effective frequency regulation, with the system controlling the engagement of the EVs in frequency regulation based on priority. The delay and accuracy analyses reveal a fast and accurate response, with the cross-correlation indicating an 8.48 s delay and an average undershoot of 0.17 kW. In the conclusions, the paper discusses prospective improvements and outlines future research directions for integrating EVs as service providers.

Sizing Methodology of Dynamic Wireless Charging Infrastructures for Electric Vehicles in Highways: An Italian Case Study

The necessity of pushing the road mobility towards more sustainable solutions has become of undeniable importance in last years. For this reason, both research and industry are constantly investigating new technologies able to make the usage of battery electric vehicles(BEV) as accessible and usable as traditional internal combustion engine vehicles (ICEV). One of the most limiting issues concerns the short range of electric vehicles, which complicates their use for long distances, such as for highway travels. A promising solution seems to be the “charge-while-driving” approach, by exploiting the inductive dynamic wireless power transfer (DWPT) technology. Nevertheless, such systems show different issues, first of all, high investment and maintenance costs. Furthermore, it is not clear how extensive a potential dynamic wireless charging infrastructure needs to be to make a real advantage for electric vehicle drivers. As a consequence, the aim of this paper is to introduce a new methodology to estimate the number and length of wireless charging sections necessary to allow the maximum number of electric vehicles to travel a specific highway without the need to stop for a recharge at a service area. Specifically, the methodology is based on a algorithm that, starting by real traffic data, simulates vehicle flows and defines the basic layout of the wireless charging infrastructure. This simulator can provide a decision support tool for highway road operators.

Analysis of charging tariffs for residential electric vehicle users based on Stackelberg game

Establishing a rational pricing mechanism for electric vehicles (EVs) can incentivize users to engage in charging activities, foster positive consumption behaviors, and indirectly benefit the grid by increasing load demand. Therefore, this paper conducts a Stackelberg game study between the grid and EV users by utilizing real charging and discharging data of EV users in F city. First, the price elasticity of EV users' electricity consumption behavior is analyzed. Second, the Stackelberg game model is constructed, multiple tariff schemes are designed to calculate the equilibrium tariff of the game, and the benefits of the grid and EV users are analyzed in comparison with the tariff policy that will be implemented in F city. Lastly, different seasonal scenarios are constructed to extend the applicability of the research results. It is found that Scheme 3 effectively incentivizes charging load growth, increases cross-subsidies and transmission and distribution fees, and protects the level of charging and discharging costs for residents; furthermore, lowering charging and discharging tariffs during seasons of high price elasticity for residents to increase demand is an optimal strategy for grid pricing.

Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China

The electrification of transportation is profoundly reshaping human society and presenting new challenges in terms of travel modes, infrastructure development, and energy supply. Given the potential for large-scale scheduling of electric logistics vehicles (ELVs), it is crucial to thoroughly analyze the usage characteristics and establish reliable models. This study examines the usage patterns and charging behaviors of 29 ELVs in Shenzhen, China, encompassing 34,856 trips and 14,464 charging events. Furthermore, behavior-time probability density models were constructed based on an improved Gaussian mixture model (GMM), which avoids the fitting error caused by misclassification of time series data across time nodes. The article also provides a comprehensive analysis of other statistical findings related to the travel and charging activities of ELVs. The conclusions drawn from this research can serve as valuable references for industries involved in infrastructure construction, power grid management, battery virtual aggregation, and similar sectors.