Charging Behavior Of Electric Vehicles Research Articles

An analysis of EV charging and route choice behavior considering the effects of planning ability, risk aversion and confidence in battery in long-distance travel

The increasing demand for long-distance and intercity travel is driven by the progress in regional transportation integration and the establishment of charging infrastructure. This increase suggests that the use of electric vehicles (EVs) for long-distance trips is likely to become more prevalent in the future. Therefore, there is an urgent need for more studies on the travel behavior of EV drivers for long-distance trips. However, there are few studies looking at the travel behavior of EV drivers on long-distance trip and multiple charging during trips. To fill this gap, this study identified EV drivers’ preferences for charging and route choice in long-distance or intercity travel on the basis of a stated preference survey, with the Shandong highway network as the travel scenario. A nested logit (NL)-based hybrid choice model was used to investigate the impacts of personal attitudes, namely planning ability, risk aversion and confidence in battery, on the drivers’ charging times choice behavior. The NL structure consists of two levels, with the upper level focusing on the charging times decision process, and the lower level analyzing the choice behavior of routes with no charging, routes with charging once, and routes with charging twice. The results show that the initial state of charge, and attitudes significantly influence the decision on charging times. In addition, EV drivers who are risk-averse and lack confidence in the battery tend to charge, whereas drivers with strong planning ability prefer to charge once in long-distance travel. Charging and resting simultaneously is the best way to meet the needs of charging and resting, and reduce the time lost while driving. These results provide insights on drivers’ travel planning and charging infrastructures planning for long-distance trips.

Research on the analysis strategy of electric vehicle charging demand based on multi-correlation daily scenario generation

Abstract With the continuous increase of the penetration rate of OFELECTRIC vehicles, their high random charging loads exert a more significant effect on the stability of the electrical system. Simultaneously, the past charging patterns of electric vehicles indirectly influence the possible future charging activities of these vehicles. Therefore, in order to characterize the correlation between the predicted daily charging behavior of electric vehicles and its historical daily charging behavior, this article uses scene generation theory to fully mine the original multi-correlation Internal characteristics of electric vehicle charging behavior among the original multi-correlation day charging scenario set (OMCDCSS), generating massively generating multi-correlation day charging scenarios (GMCDCS) similar to the probability distribution of OMCDCSS. Secondly, the related scene set (RSS) is screened in the generation of multi-correlation day billing scenario (GMCDCSS) on the basis of weighted Spearman rank (SR). Get the forecast result of electric vehicle load interval.

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.

Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

Analysis of Spatiotemporal Characteristics and Influencing Factors of Electric Vehicle Charging Based on Multisource Data

Amid the global shift towards sustainable development, this study addresses the burgeoning electric vehicle (EV) market and its infrastructure challenges, particularly the lag in public charging facility development. Focusing on Wuhan, it utilizes big data to analyze EV charging behavior’s spatiotemporal aspects and the urban environment’s influence on charging efficiency. Employing a random forest regression and multiscale geographically weighted regression (MGWR), the research elucidates the nonlinear interaction between urban infrastructure and charging station usage. Key findings include (1) a direct correlation between EV charging patterns and urban temporal factors, with notable price elasticity; (2) the predominant influence of commuting distance, supplemented by the availability of fast-charging options; and (3) a strategic proposal for increasing slow-charging facilities at key urban locations to balance operational costs and user demand. The study combines spatial analysis and charging behavior to recommend enhancements in public EV charging infrastructure layouts.

Optimal pricing strategies for distribution system operator in coupled power-transportation system

The power grid and transportation network are coupled by the charging behavior of electric vehicles. Based on the coupled power-transportation network model, this paper first analyzes the effect of the distribution system operator’s (DSO) electricity selling price on guiding the charging behavior of electric vehicles in the transportation network and then builds the DSO’s optimal pricing formulation. Considering the competition between multiple charging network operators (CNOs), this paper establishes a game model between CNOs and solves it iteratively through the best response dynamic method. An approximation method using the elasticity matrix is proposed to speed up the solution by reducing the multi-layer optimization to a single layer one in each iteration, with its effectiveness validated through numerical tests. Furthermore, the paper discusses the issue of the prisoner’s dilemma that arises among CNOs and explores the potential impact of their cooperative strategies on the overall system.

Prediction of EV Charging Behavior using BOA-based Deep Residual Attention Network

In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry.

Bi-level optimal dispatching of distribution network considering friendly interaction with electric vehicle aggregators

The widespread application of electric vehicles (EVs) is a positive force driving green development. However, their widespread penetration also poses significant challenges and threats to the security and stable operation of the power grid. To address this urgent issue, this article constructs a bi-level optimal dispatching model fostering collaboration between electric vehicle aggregators and the distribution network. The upper-level optimization targets the minimization of peak-valley differences in the distribution network via considerably arranging power outputs of gas turbines, while the lower-level one focuses on reducing the charging expense of EV aggregators via efficient charging transfer. Note that the charging expense is not only composed of electric cost but also a dynamic carbon emission factor-based cost, which contributes to the electricity economy and carbon reduction concurrently. A geometric mean optimizer (GMO) is introduced to solve the mode. Its efficiency is evaluated against three typical algorithms, i.e., genetic algorithm, great-wall construction algorithm, and optimization algorithm based on an extended IEEE 33-bus system with different charging behaviors of EVs on both a typical weekday and weekend. Simulation results demonstrate that the GMO outperforms other competitive algorithms in accuracy and stability. The peak-valley difference between the distribution network and the total cost of EV aggregators can be decreased by over 98% and 76%, respectively.

A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks

The dynamic charging behavior of electric vehicles (EVs) is causing frequent line-overloading problems and serious power security issues. Controlled and smart charging mechanisms for EVs incorporating demand response (DR) may provide significant operational flexibility to the grid operators and reduce charging costs for EV users. However, controlling EV charging may cause inconvenience to individual EV users. A market-based mechanism is proposed in this research work that allows EV users and households to participate in the network congestion alleviation DR program online while maintaining the power quality. Consumers submit their charging requirements (e.g., charging power and charging deadlines) and load curtailment tolerances to the aggregator. The aggregator is an entity assumed to have a long-term contract with the distribution system operator (DSO) and regularly receives network congestion information on behalf of retail energy users. Lyapunov optimization (LO) framework is used to reschedule the EVs, and household loads using DC optimal power flow (DCOPF) to get the dynamic congestion cost signal (CCS). The developed strategy is tested on a modified IEEE 33-bus radial active distribution network (ADN). Simulation results illustrate that the designed algorithm is promising in mitigating network congestion by ensuring significantly fewer violations of network constraints (i.e., line limits) both in terms of capacity and frequency. It also results in less energy cost as compared to other benchmark algorithms, e.g., greedy algorithm, and provides a guarantee of meeting EV charging, and flexible household loads’ (FHL) delay tolerance constraints. The average curtailment ratio of FHL and the service delay for EV-charging requests are converged to the user-defined limits, i.e., 0.25 for curtailment ratio tolerance and 10 times-lots for EV service delay. Unlike other counterparts, the developed algorithm is especially suitable for real-time applications as the per-slot average computational cost is negligible, i.e., 0.55 s.

Research on Orderly Charging Strategy for Electric Vehicles Based on Electricity Price Guidance and Reliability Evaluation of Microgrid

With the increasing use of electric vehicles (EVs), EVs will be widely connected to the microgrid in the future. However, the influence of the disorderly charging behavior of EVs on the stable and reliable operation of the power grid cannot be ignored. To address these challenges, the charging load characteristic model is established to describe the charging behavior of EVs. Then, an EVs orderly charging strategy based on electricity price guidance is proposed, and the goal is to minimize the peak–valley difference ratio and the total cost of EV charging. The result shows that, compared with disorderly charging, the EV orderly charging strategy based on electricity price guidance proposed in this paper can effectively reduce the peaking and valley difference ratio of load, reduce user’s charging costs, and optimize the reliability level of the microgrid.

Research on new energy vehicle charging prediction based on Monte Carlo algorithm and its impact on distribution network

With the vigorous promotion of new energy policies, the large-scale charging of new energy vehicles has put forward higher requirements for the safety and stability of the distribution network. Based on the daily driving habits and charging patterns of new energy vehicles, a Monte Carlo sampling algorithm was used to establish a charging load model for new energy vehicles. The model analyzed the driving range, charging load, and time related parameters of new energy vehicles. By analyzing the law of daily charging power of new energy vehicles, the overall trend of charging load of new energy vehicles is obtained. Combined with the daily electricity consumption law of the distribution network, the total load of the distribution network is obtained, and the degree of impact on the distribution network is analyzed. This provides direction for the scheduling of future electric vehicle charging behavior and the construction of related supporting facilities, and provides strong guidance for the optimization and upgrading of the distribution network.

Driving change: Electric vehicle charging behavior and peak loading

Electric vehicles (EVs) are projected to comprise 40 % of Aotearoa New Zealand's light vehicle fleet by 2040. However, charging decisions made by EV drivers, such as whether to charge immediately or delay charging, will affect peak electricity demand and lifetime of distribution network components. This study uses an agent-based model (ABM) of EV charging to investigate the effect of different EV penetration levels and owner charging decisions on components in New Zealand's residential electricity networks, although the methodology is wholly generalizable to other countries or regions. Monte Carlo simulation is performed for EV charging in a neighborhood of 71 houses, based on a representative residential distribution network, and simulated for 20 days. The key outcome measure is the rate of ‘Exceedance’ of the 300 kVA baseline transformer limit, where greater Exceedance entails shorter lifecycle and increased maintenance or capital costs to the provider. Results show delayed-charging algorithms (‘Altruistic charging’) decrease peak electricity demand and Exceedance, while drivers charging immediately (‘Selfish charging’) increases Exceedance. New Zealand's residential electricity networks are expected to accommodate a 40 % EV transition with 100 % Altruistic charging, as Exceedance is expected to increase less than 20 % from Exceedance without EVs. However, Selfish charging increases the rate of Exceedance by more than 250 %. Longer-term, increasing EV penetration and household electricity demand will require increased workplace charging infrastructure, electricity network upgrades, and/or automated and Internet of Things (IoT)-enabled Demand Side Management (DSM) of EV charging to avoid high rates of Exceedance and increased maintenance and replacement costs.

Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance

Electric vehicles (EVs) not only serve as significant loads for the power grid but also play a crucial role in the operation of the traffic. Their travel and charging behaviors have an impact on both the power grid and the road network. In order to address the potential impacts of a large-scale deployment of EVs on the power grid and the exacerbation of traffic congestion, this paper first establishes a dynamic road network model based on graph theory and time-varying traffic data combined with a road impedance model. Then, the spatio-temporal distribution characteristics of EV travel are modeled. Furthermore, by incorporating real-time road network data, the traditional Dijkstra’s algorithm for finding the optimal path is improved. At each node, the current real-time road impedance is used as the objective for EV path updates, thus accurately capturing the energy consumption of the EVs. Finally, using a standard testing problem on a typical working day based on data from a real case, the impacts of EV travel and charging behaviors on power distribution network operation and traffic congestion are analyzed under scenarios with no guidance and guidance for the shortest travel time. The results show that this method can significantly reduce the time cost by approximately 18% in travel time, which is of particular concern to users. This method balances the load of the charging stations, elevates the voltage level within the safety requirement of 7%, and simultaneously alleviates traffic congestion near the stations.

Research on the Orderly Charging Regulation Capability of Electric Vehicles in Beijing

The carbon peaking and carbon neutrality goals accelerate the pace of energy transformation. Urban energy development faces many challenges. While electric vehicles are promoting the clean and low-carbon transformation of urban transportation sectors, their application prospects as distributed energy storage are increasingly receiving attention from all parties. By providing reasonable guidance on the charging behavior of electric vehicles, they can provide power and energy regulation services such as frequency regulation and peak shaving to the electric power system, improving the utilization level of random and fluctuating new energy. However, there is currently limited research on the quantitative evaluation of the orderly charging regulation ability of electric vehicles. Based on the outlook for energy development and the prediction for the scale of electric vehicles in Beijing, taking into account the characteristics of different vehicle types, this article quantitatively evaluates the regulation ability of orderly charging of electric vehicles in Beijing in the future. The research provides a reference for electric vehicles to participate in demand response and form friendly interaction with the power grid.

Addressing greenhouse gas emissions and optimizing power systems: A novel approach for clean electricity integration in commercial buildings

The global imperative for ensuring sustainable development has heightened the significance of reducing greenhouse gas emissions within the building sector. Consequently, a critical exploration into the integration of clean electricity within buildings has become indispensable. Therefore, this study focuses on the urgent global concern of reducing greenhouse gas emissions in the building sector to achieve sustainable development. However, the optimization of power systems encounters new challenges due to the unpredictable charging behavior of electric vehicles, resulting in flexible demand side management. These challenges necessitate a reevaluation of existing mathematical approaches. To ensure the utilization of clean electricity, a novel and cost-effective power supply system is proposed, specifically tailored for commercial buildings with a focus on creating a comfortable environment in a shopping mall. Furthermore, an energy management strategy is suggested to optimize energy usage. The study takes into account two types of electric vehicles and develops separate models for each based on their unique stochastic behaviors. Additionally, the integration of retired electric vehicle batteries in commercial buildings is explored as a potential solution. To address the inherent complexity of the problem, a developed algorithm based on multi-group search optimization is proposed. This algorithm effectively handles the uncertainties associated with electric vehicles and employs a decomposition approach to provide an optimal solution. The results demonstrate significant reductions of 82.17% and 83.32% in the expected operating cost through the integration of photovoltaic energy alone or both photovoltaic and retired electric vehicle batteries in commercial buildings, respectively. This emphasizes the benefits of incorporating photovoltaic and retired electric vehicle batteries in commercial buildings. Furthermore, the robustness of the proposed method is validated through a comparative analysis with alternative approaches.

Large Scale EV Charging Allocation in Unbalanced Multi-Microgrid System

The improvement of charging infrastructure has greatly relieved the range anxiety of electric vehicles (EV) drivers, leading to the rapid increase in EV ownership. Due to the uncertainty of EV charging behavior, a large number of EVs connected to microgrids (MGs) without coordination will aggravate the unbalanced load distribution in the multi-microgrid system. Contrariwise, EVs with appropriate coordination can be recommended to suitable charging stations. It is beneficial to the safe operation of the power grid. Considering the load distribution among MGs during the whole EV charging period, we propose a charging station recommendation strategy to dispatch EVs to suitable charging stations. The simulation results show that the proposed strategy can optimize the EV charging behavior and effectively balance the load distribution among MGs, which can alleviate the impact of a mass of EV charging behavior on the safe operation of the multi-microgrid system.

Bilevel optimization approach to fast charging station planning in electrified transportation networks

Unplanned en-route charging of electric vehicles (EVs) can unexpectedly cripple traffic conditions. As less-effective location of fast charging station (FCS) may exacerbate this issue, the effects of FCS on EVs' charging behaviors should be incorporated at the system planning stage. This paper proposes a strategic-charging-behavior awared model in the context of electrified transportation network environment. This model is formulated into a bilevel mixed-integer programming problem. A newly designed network equilibrium model targets on the low-level problem, which is to model the drivers' charging reaction to a certain FCS layout. In considering the EV self-serving routing and charging behaviors as well as power network constraints, the upper-level problem is formulated for location and sizing decision-makings, of which the objective is to minimize the overall traffic time and investment cost. To handle the proposed bilevel problem, a descent algorithm is further developed. To examine the effectiveness of the proposed approach, extensive numerical experiments are conducted, through which the obtained results demonstrate that the EV charging demand can perfectly be met while traffic congestions can be alleviated to a great extent.

Assessing the value of information for electric vehicle charging strategies at office buildings

Smart charging strategies for electric vehicles (EVs) require as input information such as energy requirement and dwell time. In practice, that information is often not available. However, estimations may be obtained from historical charging behavior. This paper investigates the added value of historical information for EV charging strategies based on real-world EV charging data collected in an office building parking lot with 125 chargers. Furthermore, it provides valuable insights into EV charging behavior at office building parking lots, based on a statistical analysis of the data. The added value of data availability in EV charging strategies for day-ahead planning and real-time operation of office building parking lots is assessed via a set of quality metrics that measure user satisfaction and impact on the local grid. Offline charging strategies under various degrees of available information are validated by comparing their performance with the real-time operation of the parking lot. Results show a power peak reduction of more than 50% using historical data and simple estimations of arrival times, dwell times, and energy requirement. A trade-off between power peaks and service quality (on average 4.4kWh energy not served) is observed. It was found that knowledge of individual average energy provides higher added value compared to knowing individual average dwell time in both offline planning and real-time operation of the parking lot.

A Bilevel Graph Reinforcement Learning Method for Electric Vehicle Fleet Charging Guidance

This letter proposes a bilevel graph reinforcement learning method for electric vehicle (EV) fleet charging guidance, achieving collaborative optimization of the transportation-electrification coupled system. A dual-agent architecture is first constructed, where the upper-level is used for charging and the lower-level is used for routing. The EV traveling and charging behavior is characterized as a graph-structured interaction process. A graph attention network (GAT) is leveraged to extract the topology correlation and feature information. Then the extracted topology embedded with knowledge, as intermediate latent environment states, is fed into the underlying network of deep reinforcement learning (DRL). A DRL-based sequential scheduling pattern is developed to realize the guidance of multiple EVs. Extensive experimental results verify the superiority and adaptability of our proposed methodology.

Performance Under Limited Service Capacity for Load Balancing EV Charging Network

The economic benefit and energy security brought by renewable energy have greatly promoted the development of microgrid (MG). However, the load demand varies from area to area, resulting in the unbalanced load distribution among MGs. With the perfection of charging facilities, electric vehicle (EV) has attracted more consumers in the automobile market. A large number of EVs connected to MGs without coordination may threaten the safe operation of the power grid. Moreover, considering the constraints of economic factors, the number of charging piles in the MG is limited, i.e., the service capacity of MG is limited. Based on this, the impact of service capacity on the load balancing and average user time is studied. The load balancing matching strategy (LBMS) is adopted to lead the EV charging behavior. We find that the larger service capacity means that the load distribution is more balanced and the average user time is shorter. Furthermore, we find the optimal service capacity of the power grid. When the service capacity is more than that, it will no longer improve the system performance and cause the waste of charging facilities.