Charging Behavior Of Vehicles Research Articles

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.

Exploring the Impact of Charging Behavior on Transportation System in the Era of SAEVs: Balancing Current Request with Charging Station Availability

Shared autonomous electric vehicles (SAEVs) can offer safer, more efficient, and more environmentally friendly real-time mobility services with advanced autonomous driving technologies. In this study, a multi-agent-based simulation model considering SAEVs’ vehicle range and charging behavior is proposed. Based on real-world datasets from the Luohu District in Shenzhen, China, various scenarios with different fleet sizes, charging rates, and vehicle ranges are established to evaluate the impact of these parameters on parking demand, charging demand, vehicle miles traveled (VMT), and response time in the era of SAEVs. The results show there would be much more charging demand than parking demand. Moreover, a larger fleet size and longer vehicle range would lead to more parking demand, more charging demand, and more VMT while increasing the charging rate can dramatically reduce the charging demand and VMT. Average response time can be reduced by increasing the fleet size or the charging rate, and a larger vehicle range leads to longer response time due to the longer time spent recharging. It is worth noting that the VMT generated from relocating from the previous request destination to the origin of the upcoming request accounts for nearly 90% of the total VMT, which should be addressed properly with appropriate scheduling. A charging policy considering current requests and the availability of charging stations was proposed and verified in terms of reducing the response time by 2.5% to 18.9%.

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.

A Literature Review on the Charging Behaviour of Private Electric Vehicles

Electric mobility is one of the ways of containing greenhouse gas and local pollutants emissions in urban areas. Nevertheless, the massive introduction of battery-powered electric vehicles (EVs) is introducing some concerns related to their energy demand. Modelling vehicle usage and charging behavior is essential for charge demand forecasting and energy consumption estimation. Therefore, it is crucial to understand how the charging decisions of EV owners are influenced by different factors, ranging from the charging infrastructure characteristics to the users’ profiles. This review examines the approaches used to investigate charging behavior and highlights the trends and differences between the results, remarking on any gaps worthy of further investigation.

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.

Research on data mining model of fault operation and maintenance based on electric vehicle charging behavior

In recent years, with the development of new energy technology and the country’s strong support for electric vehicles, there is a lack of effective electric vehicle charging fault analysis and diagnosis methods at this stage. A comprehensive analysis of the working principle of the charging process of electric vehicles, based on the clarification of the failure mechanism of the power battery and charging equipment, analyzes the fault-related factors affecting the power battery and charging equipment from multiple angles, and summarizes the relationship between the power battery and charging equipment. The feature parameters related to equipment failure are discretized by the k-means clustering algorithm. Using the optimized FP-Growth algorithm based on weights, the association rules between the power battery and charging equipment failures and the characteristic parameters of the failure factors are mined, and the correlation of the failures is analyzed based on the association rules, and the correlation between the failure factors and the failures is obtained relevant level.

An optimal coordinated planning strategy for distributed energy station based on characteristics of electric vehicle charging behavior under carbon trading mechanism

Under the dual-carbon goal, the number of electric vehicle (EV) has surged, and the uncertainty of the temporal and spatial characteristics of the load has become prominent, which brings the challenge for higher requirements to the equipment composition of distributed energy station (DES) and performance of station-network. This paper proposes a collaborative planning method for low-carbon DES with networks, which is focusing on the characteristics of EV charging behavior. First of all, based on the quantitative analysis of the temporal and spatial distribution of charging load of four typical types of EV, including private vehicle, public vehicle, taxi and bus, the Monte Carlo method is used to construct the charging load modeling of EV in the region area. In addition, the low-carbon DES is modeled equipped with photovoltaic (PV) and power to gas (P2G) devices, and the Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines in the region area. Under carbon trading mechanism, aiming at the lowest annualized total cost of the regional station-network, a station-network collaborative planning modeling strategy for the capacity allocation of low-carbon DES with networks is established. Finally, the case study for a project in Beijing China is implemented on MATLAB, to prove the effectiveness of the method proposed in the paper. The time-of-use electricity price scheme changes the temporal and spatial characteristics of EV load. For example, more than 2 MW the EV load can be brought into the valley price range, which reduces the carbon transaction cost and energy purchase cost of DES by 50 thousand yuan almost when peak-valley electricity price difference is close to 1.2 yuan. At the same time, the growth of EV charging load(the penetration rate of EV can reach up to 20%) in the planning area has not increased the investment and operation-maintenance costs of DES equipment.

Effects of long-range electric vehicles on distribution system using probabilistic analysis

The number of electric vehicles with larger battery capacity that charges at higher power levels is increasing in residential areas. This scenario directly impacts users' charging behavior and modifies the demand profile of electric vehicles. This paper evaluates the effects of long-range electric vehicles on total charging demand, transformer loading and voltage quality in a real distribution system. Electric vehicles demand is modeled based on real-world data obtained from a large-scale project developed in the United Kingdom. This study proposes performance indices to perform a qualitative analysis of the system and uses Monte Carlo Simulation to consider uncertainties in residential demand and vehicle charging behavior. The results show that the current trend of adopting long-range vehicles that charge at higher power levels modifies vehicles charging frequency and duration. It increases transformer peak demand, and the number of hours the transformer operates overloaded. Voltage quality violations occur less frequently when three-phase connectors are used to charge vehicles, as they split demand between phases.© 2017 Elsevier Inc. All rights reserved.

Optimal scheduling of electric vehicle charging operations considering real-time traffic condition and travel distance

As the number of electric vehicles (EVs) increases rapidly, the problem of electric vehicle charging has widely become a concern. Therefore, considering the fact that charging time for one EV cannot be shortened quickly and the number of charging stations will not expand rapidly, how to schedule charging operations of electric vehicles in urban areas becomes a very important issue, since it can improve charging efficiency and relieve charging anxiety of EV users. Up to now, there is no scheduling software tool for practical use in this field. Based on the analysis of electric vehicle charging behavior characteristics, this paper investigates the EV charging problem at the scheduling level. First, a mathematical model for coordinated charging of EVs is proposed to minimize the total charging time for a given number of vehicles. Second, an earliest finish charging scheduling algorithm is presented to solve the charging problem. Then, by considering the combinatorial nature and practical applications with large number of EVs, two practical swarm-optimization-based EV charging scheduling algorithms are proposed. A real-life case study is presented to illustrate the proposed approaches.

Situation Awareness of Electric Vehicle Charging Load Based on Random Forest Algorithm

Due to the important characteristics of energy saving and carbon reduction, electric vehicles have attracted worldwide attention. It can be predicted that the power grid will be faced with the access problem of large-scale electric vehicles. In order to master the user behavior characteristics of electric vehicle load, it is necessary to establish the model based on electric vehicle charging behavior. In this paper, combined with the electric vehicle charging demand and the situational awareness results of the dispatchable resources in the station area, the characteristic indicators of the electric vehicle load are quantitatively analyzed. Situational prediction of electric vehicle load based on random forest algorithm is proposed, and the sample set is divided and trained. A simulation example is used to verify the effectiveness of the method provided in load forecasting.

Impact of Dynamic Electricity Tariff and Home PV System Incentives on Electric Vehicle Charging Behavior: Study on Potential Grid Implications and Economic Effects for Households

The rapid increase of electric vehicles (EVs) would lead to a rise in load demand on power grids but create different potential benefits as well. Those benefits comprise EVs serving as a mobile energy storage system to participate in adjusting the load on the power grids and helping manage renewable energy resources. This paper evaluates the effect of dynamic electricity prices and home photovoltaic (PV) system incentives on users’ EVs charging behavior and potential impacts on grid load and household economy. This has been done by establishing and assessing three different optimized charging configurations and comparing them to an uncontrolled charging strategy. In this study, the charging incentives are applied to a representative sample of 100 households with EVs and PV systems in a metropolitan area. The results show that an optimized charging strategy based on the dynamic electricity tariff can reduce charging costs by 18.5%, while a PV-based optimized strategy can reduce the costs by 33.7%. Moreover, the PV-integrated optimization strategies significantly increase the utilization of PV energy by almost 46% on average, compared to uncontrolled charging. In addition, the simulations of this research have depicted the capability of using home PV systems’ incentives to smoothen the charging profiles and hence significantly reduce the maximum grid load. However, the electricity price optimization strategy increases the aggregated charging peaks, which can only be slightly reduced by peak shaving. Therefore, an identical price signal for all households might be critical. Further analyses have shown that direct charging occurs simultaneously with household electricity assigned to a specific low-voltage grid while PV and price incentive charging configurations shift the charging peaks away from household load peaks.

Location and capacity determination of charging station based on electric vehicle charging behavior analysis

The infrastructure construction of charging facilities for electric vehicles (EVs) is one of the key factors influencing the development of electric vehicle industry. A reasonable location and capacity determination scheme is very crucial to this issue. In this study, a probability calculation model that fully analyses the charging behavior of the owners is proposed to predict the charging load of the planned land. Based on the results of electric vehicle load forecasting, a location model with the lowest users travel cost is established. The location of charging station is optimized by genetic algorithm to obtain the location and capacity library of charging station. Especially, the two‐way cost for both the owners and the operators is considered for the location and capacity decision, and the optimal scheme is selected from the location and capacity library as the final planning result. An example analysis shows that the proposed method is effective and feasible for the location and capacity determination of electric vehicles charging stations in cities. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Charging Behavior Analysis of New Energy Vehicles

In recent years, new energy vehicles in Beijing have developed rapidly. This creates a huge demand for charging. It is a difficult problem to accurately identify the charging behavior of new energy vehicles and evaluate the use effect of social charging piles (CART piles) in Beijing. In response, this paper established the charging characteristics analysis model of new energy logistics vehicles based on the positioning data of new energy logistics vehicles. It also researched vehicle SOC and charging electricity and analyzed the utilization characteristics of charging facilities operation by using the operation data of charging facilities. Finally, this paper established the evaluation index system of regional charging facility layout to evaluate the construction level of charging facilities in each district.

Capturing diversity in electric vehicle charging behaviour for network capacity estimation

This paper proposes a stochastic data-driven model for uncontrolled charging that accurately captures diversity in individual consumer behaviour. This is important because understanding the diversity between consumers is necessary to accurately estimate the number of electric vehicles’ charging a distribution network could support without reinforcements. The model combines readily available travel survey data with high resolution data from an electric vehicle trial, using clustering and conditional probabilities. We demonstrate through a case study of UK residential charging that existing approaches may overestimate the increase in peak distribution network demand by 50%, which has implications for assessing the cost of network investments required. We also show that the peak charging demand varies regionally from 0.2–1.4 kW per household, demonstrating the importance of using locally representative vehicle usage data.

Spatiotemporal Distribution Model of Charging Demand for Electric Vehicle Charging Stations Based on Markov Chain

Considering the diversity of travel habits of users, spatiotemporal distribution model of charging load for highly random electric vehicles(EV), based on Markov chain, is proposed. According to the residents’ travel habits of the 2017 National Household Travel Survey (NHTS) in the United States, the destinations are divided into homes, work places, and other locations. Based on Markov Chain and Monte Carlo method, a highly random and complex process chain with unlimited processes is constructed. The distribution of start times and end times in one-day journey is fitted by the parameterless distribution with normal distribution as the kernel. The distribution of travel time, travel distance and dwell time is fitted by lognormal distribution. Then, a spatiotemporal distribution model of one-day vehicle travel is established. Considering the influence of EVs, the dual-input fuzzy algorithm of travel time and travel distance is used to calculate the power consumption of the trip. According to the crowd’s travel anxiety and different travel needs, different charging fredquencies and different charging power levels are used for vehicle charging behavior. Finally, the Monte Carlo method is used to calculate and analyse the charging load of EVs in different classification scenarios, such as household income, home address, weekdays and weekends. The one-day electric vehicle load distribution model was successfully established in three locations. The results show that the load of electric vehicle charging stations is not only affected by factors such as holidays, but also by the composition of the urban population.

Research on the impact of different charge and discharge modes of electric vehicles on power grid load

The rapid development of the economy has made the scarcity of traditional fossil energy increasingly prominent. At the same time, with the proposal of the beautiful China strategy, electric vehicles have become an important choice for people to travel. Different charging and discharging modes of electric vehicles have different impacts on the power grid load production. Reasonably controlling the impact of electric vehicle charging and discharging loads on the power grid, narrowing the peak-to-valley difference, and ensuring the reliability of grid operation have become urgent issues to be resolved. This paper selects electric private cars and electric buses that have a large effect on the power grid load as the research object, and uses qualitative and quantitative research methods to extract the main influencing factors of electric vehicle charge and discharge. A charge and discharge daily load calculation model for electric vehicles under three modes of disorderly charging, one-way ordered charging and bidirectional ordered charging and discharging was constructed, and a typical Tianjin electric vehicle charging behavior research sample was selected. Monte Carlo method was used to predict the daily load of Tianjin electric vehicles under three charging and discharging modes, and the results were analyzed and discussed, and the corresponding management inspiration was given.

Electric Vehicles and Their Role in the Energy System

Electric vehicles (EVs) can help decarbonise both transport and electricity supply. This is both via reduced tailpipe emissions and due to the flexibility in charge and discharge that EV batteries can offer to the electricity system. For example, smart charging of EVs could enable the storage of roughly one fifth of the solar generation of Great Britain for when this energy is needed. However, to do this, the market needs to align vehicle charging behaviour to complement renewable generation and meet system needs.

Analysis of Electric Vehicle Charging Behavior under Differential Privacy Protection

In order to reduce the impact of electric vehicles on the power grid, the charging behavior of electric vehicles is analyzed, which can improve the reliability of power grid operation. Understanding the charging behavior of electric vehicle users is helpful to improve charging service and guide charging behavior. Aiming at the problem of privacy disclosure in the process of data analysis, this paper proposes an electric vehicle charging data clustering algorithm under differential privacy protection. This method enhances the availability of clustering result by improving the selection of initial cluster center points and detecting outliers. The classification of electric vehicle users is realized through experiment, and the characteristics of charging mode corresponding to each user are analyzed.

Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour

Thousands of electric vehicles (EV), which are large in number and flexible in their use of electricity, will be connected to the power system in the near future, which will bring more uncertainty to the power system. Therefore, it is necessary to study the general characteristics of EV charging behaviours. In the charging process, big data regarding charging behaviour of EVs are generated. This paper proposes a big data mining technique based on Random Forest and Principle Component Analysis for EV charging behaviour to identify and analyse clusters with different charging characteristics from the big data. This paper uses Dundee’s January 2018 EV charging data to conduct experiments, and obtains the charging behaviour clusters of the workdays, weekends, and holidays of January. The superiority of the random forest algorithm in the EV clustering problem is reflected when compared to the Euclidean distance method. The clusters obtained by the random forest algorithm have clearer characteristics, including the user’s charging method and travel behaviour. The results show that the charging behaviour of EVs has certain regularity, and the charging load has obvious peak-to-valley difference that is necessary to be regulated.