Private Electric Vehicles Research Articles

Utilizing electric bus depots for public Charging: Operation strategies and benefit analysis

The uneven distribution of public charging infrastructure poses significant challenges for private electric vehicles (EVs). Many Chinese city bus depots have abundant chargers for electric buses (EBs), often unused during daytime operations. This study proposes an innovative mechanism to utilize bus depot chargers for private EVs, prioritizing the charging demands of EBs. Uncertainty in EB and EV charging demand, EV arrival time are considered. The objective is to maximize bus company revenue through a two-stage stochastic model. Practical applicability is demonstrated through a real-world bus depot case in Shanghai with 127 EBs and 376 private EVs. The results show that allocating 58% of the charging power and 30% of the chargers to the public from 10:00 to 24:00 meets 97.54% of the private EVs’ charging demand. The bus operator faced a charging cost of 10,747 CNY, while now it expects a profit of 1733 CNY with the project running.

Free-ridership in subsidies for company- and private electric vehicles

Consumer subsidies are commonly employed to incentivize the purchase of battery electric vehicles (BEVs), but free-ridership potentially undermines their effectiveness. The present study investigates BEV subsidies in Germany, distinguishing their effect between company- and private cars. Drawing on a panel of high-resolution car registration data, we use the estimates from a Poisson pseudo-maximum likelihood model to predict BEV registrations in the absence of the subsidy. We calculate aggregate free-rider rates of 56.5% for private cars and 87.1% for company cars. We further find that the cost of the subsidy per induced BEV among private consumers is €9,718, while it is €30,780 among companies. Overall, the estimates suggest that the subsidy is considerably less cost effective among company cars, which comprise 55% of new BEV sales. An auxiliary analysis that recognizes the possibility of differential pass-through rates of the subsidy over time confirms this conclusion.

Factors and incentives that affect electric vehicle adoption in Greece

Electric vehicle proliferation is still at exceptionally low level in Greece. The subsidy program “I move electrically” aims to accelerate the transition to EVs providing incentives both for company and private electric vehicles. In order to analyze the factors that affect the consumers’ intention to purchase private electric vehicles in Attica a questionnaire survey was conducted. Discrete choice models were developed to analyze the factors that influence the intention of users to purchase and willingness to pay extra for an EV compared to a conventional car. Travel and demographic characteristics, provision of financial incentives, environmental awareness, charging time and charging infrastructure were found to affect the intention to purchase an EV in Greece. The paper concludes with policy measures and recommendations regarding the charging infrastructure, the provision of financial incentives and marketing policies that could support the adoption of EVs.

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.

A Trading Mode Based on the Management of Residual Electric Energy in Electric Vehicles

Aiming at the distributed resources of electric vehicles with photovoltaics (PVs) on the user side, a trading mode of surplus energy sharing for electric vehicles based on the user-side PVs is proposed by utilizing the bidirectional mobility of information and energy. Power transfer can be implemented between different electric vehicle users through vehicle-to-grid (V2G) technology with a reasonable distribution of benefits taken into account. First, the operational framework of electric energy trading is presented, and the transmission architecture of each body of interest in the system is analyzed. Second, the portraits of EV users’ charging behaviors are established considering their different charging habits, and electric vehicle users are divided into electricity buyers and sellers in each trading time period. An electricity transaction model based on “multi-seller–multi-buyer” is established, and all electricity transactions are realized through blockchain-based decentralized technology. Finally, the benefit to each interest group is maximized using the improved Northern Goshawk Optimization (NGO) algorithm. Simulation results of a sample system indicate that the new power trading mode proposed in this study could lead to reasonable reuse of the electric energy of private electric vehicles and can achieve a win–win situation for all stakeholders.

Research on the Long-Term Sustainability of a Transition Towards Private Electric Vehicles

In recent years, a conflict has arisen between the urgency of fixing climate change and the reliance on cars as the primary mode of transportation. Electric vehicles have the potential to satisfy both conditions. With the accelerating growth in the EV market, it would seem as if they will soon become the future of transportation. Before a complete transition is made to EVs, it is necessary to assess the true sustainability of an EV. Due to the massive scale of the change in transportation, consequences for mistakes are significantly amplified. This paper sets out to research and evaluate EVs' sustainability compared to fossil-fule vehicles. Contrary to popular belief, EVs are nowhere near as beneficial to the environment as people had hoped. First, the concept of privately owned vehicles is unsustainable. In addition, EVs may emit more pollution than the fossil-fule vehicle. The exploitation of rare earth minerals is also causing heavy environmental damage while involving child labor. Despite this, if the issues are recognized and acted upon, it is reasonable to believe EVs still have the potential to act as a stepping stone to sustainable transportation for all.

Resilience-oriented planning method of local emergency power supply considering V2B

Private electric vehicles (EVs) have great potential to conduct emergency power supply, considering the rapid development of EVs and vehicle-to-building (V2B) technologies. To enhance the resilience of the building power supply, charging piles can be upgraded to support bi-directional power supply, thus enabling EVs to help restore the buildings affected by disasters. A planning method for the charging piles’ upgrade is proposed. First, a scenario set is generated to consider the influence of uncertainties during the planning period. The uncertainties of disasters, EVs, and building load are included. Then, a two-stage stochastic programming model is established to decide the upgrade plan. Pre-disaster decisions are made in the first stage and the building is restored in the second stage. The method is applied to two different types of buildings and the test results verify the effectiveness of the proposed method.

Charging private electric vehicles solely by photovoltaics: A battery-free direct-current microgrid with distributed charging strategy

Charging electric vehicles (EV) by photovoltaics (PV) contributes to achieving carbon neutrality, but puts pressure on urban renewal, e.g., large investments in distribution grid upgrade and energy storage (ES). To solve this problem, we proposed a charging system aiming at providing intermittent but free solar charging service for private EV drivers to cover their daily intra-urban transportation demand. It is a battery-free direct-current (DC) microgrid with a distributed charging strategy, taking variable DC bus voltage as a control signal. The system was optimized and tested by simulation on an office building with 80 private EVs. We performed a system performance comparison, when achieving the same guarantee level of EV use with the parking shed fully cover by PV. A 10-kW charger (DC Level 1) with the strategy can take the role of a 3.5-kW charger (AC Level 2) with ES (26.0 kWh/parking space) to coordinate PV generation and charging load. Furthermore, connecting the system with the adjacent building can increase the annual solar self-consumption rate from 33.3% to 67.9%, and avoid the distribution grid upgrade (average 5.6 kVA/charger). The discounted payback time of this building-connected system decreases from 9.4 to 4.5 years, compared with a conventional solar charging station.

Economic Viability of Dynamic Wireless Charging Technology for Private EVs

Dynamic wireless charging (DWC), which enables charging, while electric vehicles (EVs) are in motion, is an attractive charging way. However, additional installed power tracks underneath the lane bring a significant concern to its economic viability. This article proposes a comprehensive framework to evaluate the economic viability of the DWC lane for private EVs. The investment of local power support with renewable energy integration and energy storage is also considered in the DWC system. Charging choices of private EVs among multiple charging providers are modeled to estimate the electricity demand of the DWC system. The grid impact of the DWC system is studied via a multibus ac network. Moreover, an optimization policy is proposed to maximize the DWC provider’s profit and minimize the grid impact by adjusting the charging price and electricity procurement at each horizon. It is found that the payback period of the DWC system with the proposed optimization policy is shortened by 25% compared with the fixed charging price strategy. Even with a tight grid impact limit, the payback period will not be significantly longer under the proposed optimization policy. When the efficiency of the DWC lane increases to 90% and the cost reduces to 50%, the payback period is shortened by 19% and 22%, respectively.

Elastic flexible unit commitment: A scenario-based power-electric fleet analysis

This paper presents an elastic flexible unit commitment (EFUC) in a coupled power and electric fleet (P-EF) network. In this framework, a master independent system operator (MISO) coordinates the day-ahead unit commitment problem (UCP) while various sources of both networks are integrated. On the power side, battery energy storage (BES) is allocated, and on the electric fleet side, private electric vehicles (PEVs), electric buses (EBs), and electric taxis (ETs) are integrated. The temporalities of PEVs and EBs are managed by PEV to grid (P2G) and bus to grid (B2G) technologies, respectively. Traveling patterns present the spatial impact of PEVs and ETs on day-ahead commitment. In this P-EF network, an incentive-based demand response (IBDR) program is implemented by MISO. A scenario-based technique is applied to generate operation scenarios for coupled uncertainties of load demand and the number of PEVs. The overall mixed integer linear programming (MILP) model is tested on IEEE 6-bus and 24-bus systems which are integrated with traveling patterns of PEVs and ETs. Results confirm that classifying the behavior of various electric vehicles leads to an explicit understanding of each source’s impact on UCP cost and required energy. The results indicate that MISO prioritizes the utilization of spatio-temporal sources (e.g., PEVs) over only stationary temporal ones (e.g., EBs or BES) since both time and location are under control. Also, MISO damps the spike prices by the IBDR program while offering incentives to responsive loads under uncertainty.

Technical and Economic Assessment of Battery Storage and Vehicle-to-Grid Systems in Building Microgrids

In an electrical microgrid, distributed renewable generation is one of the main tools used to achieve energy sustainability, cost efficiency and autonomy from the grid. However, reliance on intermittent power sources will lead to a mismatch between generation and demand, causing problems for microgrid management. Flexibility is key to reducing the mismatch and providing a stable operation. In such a context, demand response and energy storage systems are the main factors that contribute to flexibility in a microgrid. This paper provides an assessment of the technical and economic impacts of a microgrid at the building level, considering photovoltaic generation, battery energy storage and the use of electric vehicles in a vehicle-to-building system. The main novel contributions of this work are the quantification of system efficiencies and the provision of insights into the design and implementation of microgrids using real on-site data. Several tests were conducted using real on-site data to calculate the overall efficiencies of the different assets during their operation. An economic assessment was carried out to evaluate the potential benefits of coordinating battery storage with a vehicle-to-building system regarding the flexibility and cost-efficient operation of the microgrid. The results show that these two systems effectively increase the levels of self-consumption and available flexibility, but the usefulness of private electric vehicles in public buildings is constrained by the schedules and parking times of the users. Furthermore, economic benefits are highly dependent on the variability of tariffs and the costs of energy storage systems and their degradation, as well as the efficiency of the equipment used in the conversion chain.

Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven

The rapid development of electric vehicles (EVs) has posed challenges to power grids and transportation networks. Accurately capturing the usage patterns of EV users is a prerequisite for EVs’ interaction with electrified transportation networks. Thus, this paper proposes a full data chain (FDC) driven model to mine EVs’ comprehensive characteristics. By collecting the data of 150 private electric vehicles (PREVs), 100 commercial electric vehicles (CEVs), and 50 official electric vehicles (OEVs) in Chongqing, China, the driving characteristics are firstly mined by the adoption of origin-destination (OD) distribution and driving portrait. Moreover, the charging characteristics are extracted based on the state recognition for data chains. Then, vehicle usage characteristics of different types of users are comprehensively described based on the density-based spatial clustering of applications with noise (DBSCAN). Finally, the results of EV user characteristics are analyzed, and the effectiveness of the proposed model is verified by regional charging load analysis and urban road traffic flow comparison. The findings provide a data source and user behavior model for the planning, operation, and control of power grids and transportation networks.

Voltage Regulation With Electric Taxi Based on Dynamic Game Strategy

In this paper, a battery charging and swapping optimization model is established for electric vehicles (EVs) and battery charging/swapping stations (BCSS). The EVs are categorized into private electric vehicles (PrEVs) and electric taxis (ETs), where the charging behaviors of PrEVs are modeled based on the Monte Carlo (MC) method, and the battery swapping (BS) strategies of ETs are optimized by bi-level dynamic game. Moreover, the voltage deviation of the power grid is considered in the load regulation process of ETs. A “path-location” model is established combining with the Floyd algorithm in the simulation and the IEEE 14-Bus system is used to derive the node voltage. Numerical results show that the proposed strategy can simultaneously increase the revenue of BCSS and ETs and reduce the voltage deviation.

Emission mitigation potential from coordinated charging schemes for future private electric vehicles

Plug-in electric vehicles (EVs) are expected to synergize with low or even zero-carbon electricity towards a deep mitigation of greenhouse gas (GHG) emission and ultimately carbon neutrality. These benefits are only obtainable when EV charging maximizes the consumption of electricity generated from renewable energy sources. However, the current mismatch between renewable energy output and EV charging demand poses a substantial challenge. Rescheduling charging events, namely charging coordination, has the potential to integrate renewable electricity and tap into higher GHG emission reductions. In this study, with Beijing as the research domain, we develop a charging demand model informed by real-world usage data generated by a massive fleet of private cars, and evaluate the comprehensive impacts of various charging coordination schemes in the future with higher adoption rates of both private EVs and renewable power sources. The research finds that strategies aiming at maximizing renewable power consumption, netload valley filling, and charging cost minimization have similar climate benefits, which can reduce well-to-wheels GHG emissions by approximately 20%, cut charging cost by half, and erase 95% of the need for newly installed generation capacity compared to the benchmark scenario without coordinated charging. On the other hand, conventional charging strategies, such as the simple demand-valley-filling strategy, would increase GHG emissions. The study indicates that substantial benefits can be synergistically achieved between power and transport systems towards improved grid stability, lower fuel cost, and greater emission mitigation by leveraging EV charging flexibility at the end-user side (i.e., charging facility). Furthermore, the potential need for sophisticated charging coordination with multiple optimization objectives is brought to attention.

Data‐Driven Temporal Charging Patterns of Electric Vehicles in China

Users of electric vehicles (EVs) have different preferences for the temporal charging patterns. The analysis of temporal charging patterns can aid in the decision‐making of EV‐related stakeholders, including policymakers, grid operators, and automobile companies. Herein, a recognition and factor analysis framework of temporal charging patterns for EVs’ usage improvement is proposed. A Gaussian mixture model (GMM) is used to obtain temporal charging patterns by considering characteristics related to the charging session and time of parking period. A multinomial logit model is used for the factor analysis of the temporal charging patterns. An experiment is conducted with the proposed framework using data of private electric vehicles (PEVs) and carsharing electric vehicles (CSEVs) collected between 2018 and 2019 in China. The data are statistically analyzed, and then six types of temporal charging patterns are obtained by the GMM. A comparison of the pattern distributions over time shows that they are obviously different between PEVs and CSEVs. The analysis of the patterns’ influencing factors of PEVs and CSEVs shows that the PEVs have more influencing factors than the CSEVs.

Exploring the potential of rental electric vehicles for vehicle-to-grid: A data-driven approach

Vehicle-to-Grid (V2G) is a promising technology in electrification of transportation. Previous studies of V2G tended to be focused on private Electric Vehicles (EVs), paying little attention to rental EVs. In response, this paper proposed a data-driven approach to quantify the potential contribution of rental EVs to smart grid through V2G, using a one-month GPS trajectory dataset containing 967 rental EVs in Beijing in January 2019. The data-driven approach was tested within several “what-if” scenarios in Beijing, which considered availability of infrastructures with different charging/discharging speeds. The results suggested that the increased availability of infrastructure and charging/discharging speed could increase both charging and discharging demands. On overage, a 10% increase in the probability of getting EVs charged gives rise to a 13% increase in charging demand, and a 28% increase in discharging demand. Given that rental EV users can get access to infrastructure anytime and anywhere with a charging/discharging speed of 3.0 km/min (i.e., 3 km electric range per minute), it was estimated that the total daily charging and discharging demands, on average, were 1,860,234 kWh and 1,467,044 kWh, respectively, which were around 3.12% and 2.46% of the total daily residential electricity demand. Furthermore, the increased availability and charging/discharging speed can also lead to a smaller gap between the charging cost per vehicle per day and discharging reward per vehicle per day, bringing more economic benefits to rental EV users through V2G.

Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications

Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications

Methodology for Estimating the Spatial and Temporal Power Demand of Private Electric Vehicles for an Entire Urban Region Using Open Data

With continuous proliferation of private battery electric vehicles (BEVs) in urban regions, the demand for electrical energy and power is constantly increasing. Electrical grid infrastructure operators are facing the question of where and to what extent they need to expand their infrastructure in order to meet the additional demand. Therefore, the aim of this paper is to develop an activity-based mobility model that supports electrical grid operators in detecting and evaluating possible overloads within the electrical grid, deriving from the aforementioned electrification. We apply our model, which fully relies on open data, to the urban area of Berlin. In addition to a household travel survey, statistics on the population density, the degree of motorisation, and the household income in fine spatial resolution are key data sources for generation of the model. The results show that the spatial distribution of the BEV charging energy demand is highly heterogeneous. The demand per capita is higher in peripheral areas of the city, while the demand per m2 area is higher in the inner city. For reference areas, we analysed the temporal distribution of the BEV charging power demand, by assuming that the vehicles are solely charged at their residential district. We show that the households’ power demand peak in the evening coincide with the BEV power demand peak while the total power demand can increase up to 77.9%.

Research on layout planning of Charging Infrastructure for Private Electric Vehicle Charging Station

Green development is the theme of the world today. As a kind of pollution-free and convenient carrying tool, the electric vehicle (EV) has been developed rapidly. The layout of the charging station satisfies the customers’ charging demand. Establishing vehicle prediction and charging scale prediction models to meet customers with charging needs. In the research on the layout planning of the charging stations, the elasticity coefficient method is used to predict the number of private electric vehicle (EV), and the queuing theory model is used to predict the charging demand. Forecasting the scale of charging stations based on private electric vehicles ownership and charging demand. Finally, a city is selected as the planning area for example analysis. The feasibility and nationality of the charging station layout planning method were proved.

Private versus Shared, Automated Electric Vehicles for U.S. Personal Mobility: Energy Use, Greenhouse Gas Emissions, Grid Integration, and Cost Impacts.

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. While the convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, it requires careful integration with rapidly evolving electricity systems. Here, we examine these interactions using a U.S.-wide simulation framework encompassing private electric vehicles (EVs), shared automated EVs (SAEVs), charging infrastructure, controlled EV charging, and a grid economic dispatch model to simulate personal mobility exclusively using EVs. We find that private EVs with uncontrolled charging would reduce GHG emissions by 46% compared to gasoline vehicles. Private EVs with fleetwide controlled charging would achieve a 49% reduction in emissions from baseline and reduce peak charging demand by 53% from the uncontrolled scenario. We also find that an SAEV fleet 9% the size of today's active vehicle fleet can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Such an SAEV fleet would achieve a 70% reduction in GHG emissions at 41% of the lifecycle cost as a private EV fleet with controlled charging. The emissions and cost advantage of SAEVs is primarily due to reduced vehicle manufacturing compared with private EVs.