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Featured Application: The LM-IEKF algorithm proposed in this paper can effectively estimate the state of charge of a lithium-ion battery, and it is suitable for the estimation of an electric vehicle. The error covariance matrix in the IKEF process is modified by the LM algorithm, and it can still maintain a good convergence speed and estimation accuracy in the face of severe current changes. With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm. [ABSTRACT FROM AUTHOR]

The couple between the power network and the transportation network (TN) is deepening gradually with the increasing penetration rate of electric vehicles (EV), which also poses a great challenge to the traditional voltage control scheme. In this paper, we propose a coordinated voltage control strategy for the active distribution networks considering multiple types of EV. In the first stage, the action of on-load tap changer and capacitor banks, etc., are determined by optimal power f low calculation, and the node electricity price is also determined based on dynamic time-of-use tariff mechanism. In the second stage, multiple operating scenarios of multiple types of EVs such as cabs, private cars and buses are considered, and the scheduling results of each EV are solved by building an optimization model based on constraints such as queuing theory, Floyd-Warshall algorithm and traffic flow information. In the third stage, the output power of photovoltaic and energy storage systems is fine-tuned in the normal control mode. The charging power of EVs is also regulated in the emergency control mode to reduce the voltage deviation, and the amount of regulation is calculated based on the fair voltage control mode of EVs. Finally, we test the modified IEEE 33-bus distribution system coupled with the 24-bus Beijing TN. The simulation results show that the proposed scheme can mitigate voltage violations well. [ABSTRACT FROM AUTHOR]

With a large number of new energy vehicles being put into use, it is the general trend for traditional fuel vehicles to withdraw from the market in an orderly manner. Determining the optimal ratio between them in this process is of great significance to the low-carbon sustainable development of cities. Therefore, considering the constraints of urban automobile development planning and air pollution prevention and control policies, a multi-objective model to minimize pollutants and costs is constructed in this paper. Through model calculation and sensitivity analysis of dynamic impact relationship of different types of vehicles, it is determined that when new energy vehicles account for around 36% in Beijing, 57% in Shanghai and 46% in Guangzhou, the pollutant emissions can be minimized without causing a significant increase in social costs. Additionally, compared with 2030, Beijing, Shanghai and Guangzhou can achieve emission reductions of 320,000 tons, 200,000 tons and 250,000 tons, respectively, in 2050 if they implement the policy of banning the sale and delisting of fuel vehicles, which could provide suggestions for the guidance of the low-carbon development plan of the automobile industry. [ABSTRACT FROM AUTHOR]

With the increasing demand for electric vehicle (EV) charging, there are cases of complicated trading between EV users and charging operators or electrical utilities. This paper proposes a new trading model based on consortium blockchain. Firstly, mutual trust interconnected transaction networks and channels between charging operators are established. The PBFT consensus algorithm is used to verify EV charging transactions, and smart contracts are used to complete the process. In this model, charging station nodes of each company can verify transactions, the interconnection and independent management of charging transactions are realized, users' charging methods are enriched, and the flexibility of charging services is improved. Finally, this paper uses hyperledger fabric to build an experimental environment. The feasibility of the above method is verified with an actual distribution scenario of some charging stations in Beijing. [ABSTRACT FROM AUTHOR]

With the decarbonization of the transportation sector and the diversification of travel demand, the development of shared electric vehicles has become crucial. Based on survey data of travel mode and destination of shared electric vehicles in Beijing, this paper aims to explore the formation and distribution mechanisms of the demand for shared electric vehicles. First of all, a multi-index and multi-cause (MIMIC) model was established to quantify the psychological latent variables that cannot be directly observed and to analyze the mechanisms between individual socio-demographic attributes and latent variables. Secondly, these psychological latent variables were added to mixed logit (ML) models as explanatory variables to form hybrid choice models to explore the travel mode choice behavior and travel destination choice behavior, respectively, when using shared electric vehicles for leisure travel. The results show that potential users of shared electric vehicles are characterized by higher education, employees of enterprises, no car availability and high driving years, and most of them travel for the purpose of connecting to transport hubs. Latent variables such as individual carbon trading, subjective norms, risks and behavioral intentions all affect the demand for shared electric vehicles; in-car time, out-of-car time, travel cost and the number of subway stations have negative impacts on the demand, while mall properties and the number of parking lots have positive impacts on the demand. Furthermore, the use of shared electric vehicles is highly correlated with the use of cars and subways, and part of the travel demand could be transferred to shared electric vehicles by taking certain measures. [ABSTRACT FROM AUTHOR]

The promotion and use of new energy vehicles are important means to reduce carbon emissions. However, its effect is usually affected by the rebound effect. The current research does not consider the impact of the substitution effect of new energy vehicles on traditional fuel vehicles to the carbon emission rebound effect, including its situation in different scenarios. This paper considers the purchase stage, use stage of the new energy vehicles and constructs a comprehensive two-stage almost ideal demand system model. The rebound effect of carbon emissions from 2014 to 2019 and different scenarios from 2020 to 2030 is quantified. The results show that the rebound effect of carbon emissions without car purchase subsidies is 4%–9% less than that with car purchase subsidies in the corresponding year. In the prediction results of the rebound effect under different future scenarios, the values of partial rebound effects are more than 80%, and the values of backfire effects are less than 115%, which indicates that the rebound effect of carbon emissions shows a convergence trend. In the future, the rebound effect of carbon emissions will show a trend of convergence. When the total vehicle sales remain unchanged, the replacement proportion of new energy vehicles reaches 85%, and there is no subsidy policy, the rebound effect of carbon emissions is the smallest, indicating that this scenario is the most favorable for reducing carbon emissions and reducing environmental pressure. [ABSTRACT FROM AUTHOR]

The dual-carbon strategy advocates a green, environmentally friendly, and low-carbon lifestyle. In the field of transportation, electric vehicles (EVs) have been regarded as an effective solution to reduce carbon emissions and to conserve energy. Developing a reasonable charging guidance scheme for users is a feasible way to solve problems, such as the range anxiety of EV users, and has a great application value for the promotion of EVs in the future. In practical situations, how to develop charging induction schemes for users that better meet their needs according to the type of user and their multi-dimensional preferences is the focus of this paper. To this end, this study utilized charging behavioral data to investigate the multi-dimensional charging preference of users based on the collaborative filtering algorithm. Then, a multi-objective optimization model was established based on the preference degree of each charging station and the integrated travel cost. An NSGA-III framework was used to design the algorithm to solve the proposed model. The algorithm was tested using simulation experiments that were designed based on the road network and charging stations in Beijing. The final result is an experimental analysis of the weight matrices for the three different preferences of minimum energy consumption cost, minimum time cost, and minimum fee cost, which yields a difference of about 4.4% between the optimal energy consumption cost and the maximum energy cost, about 2.9% between the optimal time cost and the maximum time cost, and about 10% between the optimal fee cost and the maximum fee cost under these three different preferences, respectively. The proposed multi-objective optimization model is able to provide users with reliable charging station selection by incorporating their personalized charging preference characteristics and charge guidance schemes. [ABSTRACT FROM AUTHOR]

With the development of economy, energy dilemma and environment protection give urgent pressure on automobile industry. More and more countries are making new energy vehicle policy. How to choose a proper routing when driving an electric vehicle? Does charging stations layout in a city meet the demand of electric vehicles? This paper gives out the electric vehicles' routing optimizing algorithm. The algorithm focus on decision policy before an electric vehicle starts. There are 3 kinds of situations: the situation moving to destination, moving to a charging station and staying origin. Each situation has its judgment and it is discussed in detail. Except that, the electric vehicles' routing optimization is solved in this paper. By applying routing optimizing algorithm, a multi-agent simulation model is build and the model is running with real data of charging stations in Beijing. The simulation results show that this model is valid. Through analysis of simulation data, this paper concludes that the layout of charging stations in Beijing is not reasonable, because charging stations are not visited by electric vehicles equally. 4 charging stations have higher visiting frequency. More charging stations should be built near these areas. [ABSTRACT FROM AUTHOR]

The market penetration rate of electric vehicle (EV) is on the rise globally. However, the use behaviors of private EVs have not been well understood, in part due to the lack of proper datasets. This paper used a unique dataset containing trajectories of over 76,000 private EVs (accounting for 68% of the private EV fleet) in Beijing to uncover trip, parking and charging patterns of private EVs, so as to better inform policy making and infrastructure planning for different EV-related stakeholders, including planners, vehicle manufacturers, and power grid and infrastructure companies. We conducted both statistical and spatiotemporal analyses. In terms of statistical patterns, most of the EV trip distances (over 71%) were shorter than 15 km. Also, most of parking events (around 76%) lasted for less than 1 h. From a spatial perspective, the densities of trip Origins and Destinations (ODs), parking events and charging events in the central districts tended to be much higher than those of the other districts. Furthermore, the number of intra-district trips tended to be much higher than the number of inter-district trips. In terms of temporal trip patterns, there were two peak periods on working days: a morning peak period from 7 to 9 AM, and an afternoon peak period from 5 to 7 PM; On non-working days, there was only one peak period from 9 AM to 5 PM; while the temporal charging patterns on working and non-working days had a similar trend: most of EV drivers got their EVs charged overnight. Finally, we demonstrated how to apply the observed statistical and spatiotemporal patterns into policy making (i.e., time-of-use tariff) and infrastructure planning (i.e., deployment of normal charging posts, enroute fast charging stations and vehicle-to-grid enabled infrastructures). [ABSTRACT FROM AUTHOR]

The development of mobile Internet, smartphones, and location-based services has enabled ridesourcing, which pools vehicles and drivers to provide on-demand travel services. As an alternative transportation option, ridesourcing has significant impacts on urban travel. However, the unique mobility pattern of ridesourcing and its impact on vehicle electrification have not been well studied. To address this gap, this paper presents a comparative, big-data-driven framework to characterize the ridesourcing mobility pattern, and evaluate the acceptance potential of electric vehicles for ridesourcing in comparison with other types of vehicle use. Multi-temporal resolution ridesourcing trips are extracted from raw GPS trajectories. The patterns of three urban travel (household, ridesourcing, and taxis) are extracted from GPS trajectories in Beijing, and compared. The electrification potentials of these types of travel under different charging levels are then evaluated. The results demonstrate that mobility patterns of household, ridesourcing, and taxi drivers are similar when a single trip is considered but differ significantly when total vehicle travel is considered. We show that potential acceptance of electric vehicles decreases significantly from household to ridesourcing and taxi vehicle use. These findings provide useful insights into of the role vehicle electrification can play in sustainability of urban personal transportation across a range of drivers. [ABSTRACT FROM AUTHOR]

The ownership of electric vehicles, including plug-in electric vehicles (PEVs) and hybrid electric vehicles (HEVs), begins to be prevalent at present and poses as a promising way to alleviate environmental pollutions. However, the traditional method of environmental impact assessment is encountering challenges due to the large differences between conventional internal combustion engine vehicles (ICEVs) and electric vehicles. This paper proposes a novel SEA method for the penetration of electric vehicles at link level. An absorbing Markov chain model is formulated to represent the transition process among ICEVs, PEVs, and HEVs in the future, where PEV is an absorbing state. In addition, both HEV and PEV travelers are assumed to behave in the same manner as conventional ICEV travelers. As a result, the vehicle fleet composition is consistent with their market shares on each link. However, the three vehicle types are distinguished by their respective emissions rates. Therefore, the strategic environmental assessment can be worked out based on market penetration level and link traffic flows. For a given road network, transport system equilibrium is proposed to calculate link traffic flows. It is a four-step sequential model with feedback channel which can be solved by method of successive averages (MSA) effectively. For sustainable development, China plans to build a new city named Xiong'an nearby the capital Beijing. The strategic environmental assessment is conducted using the development of Xiong'an as an experimental study to demonstrate the effectiveness of proposed method. This research is very useful in urban planning with environmental constraints. [ABSTRACT FROM AUTHOR]

Electric vehicle timeshare rental is a form of sustainable transportation and is growing in popularity around the world. An analysis of the present situation is thus useful for transport management and planning. The study reported in this paper aimed to clarify the usage and operation of electric vehicle timeshare rental schemes in Beijing, China, including mobility patterns, pricing structure, willingness and factors affecting satisfaction through data analysis and questionnaires. The results showed that users had distinctive socio-demographic attributes. Mobility patterns represented a regularity in both time and space, and the pricing structure was a significant factor affecting the users' opinions. Eight factors, including age and cost, showed strong correlation with user satisfaction. It is concluded that electric vehicle timeshare rental is a promising travel mode that could replace private cars and promote the development of electric vehicles. The results of this study can contribute to programming and management of electric vehicle timeshare rental schemes in the future. [ABSTRACT FROM AUTHOR]

Shared Autonomous Electric Vehicles (SAEVs) are pivotal for future transportation, offering both promise and challenges upon integration with the power grid. This symbiosis augments power system flexibility, stability and reliability through Vehicle-to-Grid (V2G) services, and optimize transportation efficiency. However, it amplifies the demand for robust charging infrastructure and electricity power during peak periods. This paper proposes a framework employing a sequential receding horizon optimization approach to manage SAEV mobility and charging dynamics. Focused on maximizing transportation service quality while ensuring power grid stability, the model accommodates dynamic trip requests and electricity generation, utilizing a rolling horizon algorithm. Notably, the study explores the potential of SAEVs in fortifying the integration of renewable energy resources (RES) into the power grid. Our research strives to equip policymakers and system planners with a robust tool for crafting efficient and sustainable future urban transportation and energy systems. • A novel modeling and optimization framework for SAEV-PowerGrid coupled system. • Applied a rolling horizon algorithm to solve the problem with realistic sequential and dynamic input. • Case study applications for downtown Beijing. • Revealed SAEV's significant role in integrating renewable energy resources into the powergrid. [ABSTRACT FROM AUTHOR]

Abstract Electrifying urban transportation through the adoption of Electric Vehicles (EVs) has great potential to mitigate two global challenges, namely climate change and energy scarcity, and also to improve local air quality and further benefit human health. This paper was focused on the six typical factors potentially influencing the purchase behaviour of EVs in Beijing, China, namely vehicle price, vehicle usage, social influence, environmental awareness, purchase-related policies and usage-related policies. Specifically, this study used the data collected in a paper-based questionnaire survey in Beijing from September 2015 to March 2016, covering all of the 16 administrative regions, and tried to quantify the relative importance of the six factors, based on their weights (scores) given by participants. Furthermore, Multinomial Logit (MNL) models and Moran's I (a measure of global spatial autocorrelation) were used to analyse the weights of each factor from statistical and spatial perspectives, respectively. The results suggest that 1) vehicle price and usage tend to be more influential among the six factors, accounting for 32.3% and 28.1% of the importance; 2) Apart from the weight of social influence, the weights of the other five factors are closely associated with socio-demographic characteristics, such as individual income and the level of education; 3) people having similar attitudes towards vehicle usage (Moran's I = 0.10) and purchase restriction (Moran's I = 0.14) tend to live close to each other. This paper concludes with a discussion on applying the empirical findings in policy making and modelling of EV purchase behaviour. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling. [ABSTRACT FROM AUTHOR]

The widespread adoption of electric vehicles has become pivotal in attaining emissions reduction objectives, and the rationale behind designing and situating charging stations determines the unfurling of electric vehicles. This scholarly work fully mines the geographical situational particulars underlying the functioning statistics of charging stations in Beijing during the period from January to March 2016. By incorporating the urban road network, we construct a charging stations accessibility index to investigate the influence of stations accessibility on utilization rates. The findings reveal a substantial positive relationship between the overall accessibility of charging stations and their utilization rates. However, the limited number of charging stations hinders the agglomeration effect from significantly boosting utilization rates. Moreover, the impact of charging station accessibility on utilization rates varies significantly across regions with different levels of development, indicating pronounced heterogeneity. The research conclusions of this paper provide useful suggestions for promoting the rational layout of charging stations. [Display omitted] • Charging station accessibility depends on driving distance to important city areas. • The accessibility of charging stations significantly impacts their utilization rate. • Charging station accessibility's impact on utilization rate varies significantly across regions. • Insufficient charging stations limit agglomeration's impact on utilization rates. [ABSTRACT FROM AUTHOR]

This research tries to establish the emissions diffusion model of high-speed motor vehicles on the Capital Ring Expressway (CRE) around Beijing, China, applying the MOVES and the CALPUFF models. Data required for this study have been collected from different sources. The results show that small buses and large trucks have the highest pollutant emissions on the CRE. The pollutants like CO and HC mainly comes from gasoline vehicles, while NOx, PM10 and PM2.5 comes from diesel vehicles. The average concentrations of the diffusion of CO, NOx, CH4, PM10, and PM2.5 along with the CRE are 6.5 mg/m3, 2.3 mg/m3, 0.27 mg/m3, 0.055 mg/m3 and 0.056 mg/m3 respectively. The results of this study have practical significance and influence on the emission reduction and concentration control strategies under different circumstances, and put forward suggestions on changing transportation modes, strengthening supervision and so on. It has produced a number of recommendations for the policy makers including (1) active promotion of new energy vehicles on expressways, (2) strategic change of the mode of passenger and freight transportation on expressways, (3) strengthen traffic management and supervision of motor vehicles on expressways, and (4) strengthen vegetation protection in rural and ecologically sound areas along the route. [ABSTRACT FROM AUTHOR]

• The Monte Carlo method is used to construct the charging load of EV in the planning area divided into four different vehicles type. • The Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines. • Proposed collaborative planning modeling promotes the time-of-use electricity price scheme to change the temporal and spatial characteristics of EV charging load. • The growth of EV charging load in the planning area has not increased the investment and operation-maintenance costs of DES equipment, and reduced carbon transaction costs as well as improved economic of system operation. • Simulations are conducted to verify the effeteness of proposed method. 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. [ABSTRACT FROM AUTHOR]

This paper investigates the win-win commercialization mode of aggregating electric vehicles (EVs) in demand side for ancillary service. We have conducted a half-year-long incentive verification experiment covering 10,066 electric vehicle owners in Beijing. Based on the experimental results, we develop an incentive-based mechanism that enables electric vehicles to participate the wholesale capacity market through an aggregator. The aggregator, which is held by charging service operators can make a profit by designing a smart pricing policy. In this process, not only the electric vehicle owners but also the utility can gain benefits. [ABSTRACT FROM AUTHOR]

This study examines the factors that influence the use of carsharing systems in Beijing, as well as the potential for carsharing systems that integrate electric vehicles. Investigated variables include weather, air quality, price, vehicle attributes and “status” indicators. Additionally, we explore how the impacts of these factors differ when carsharing is utilized for one-way trips as compared with round-trips. The study relies on a pen-and-paper survey (1010 completed survey forms with 2023 reported trips) which uses a stated-preference pivoting design to build hypothetical choice sets around actual trips. The data are used to estimate binomial logit regression models for one-way and round-trip carsharing. Results of the analysis indicate that age, car ownership, shelter mode, the original cost for taxi users, perceived parking availability, cold weather, and relative cost differences are significant for one-way carsharing. For round-trip carsharing, significant factors include car ownership, income, gender, environmental concern, and relative cost differences. The most statistically significant factors to attract carsharing customers are the cost gap (defined as cost of original mode – cost of carshare) for both one-way and round-trip carsharing services, and car ownership, which has a positive significant effect for one-way trips and a negative significant effect for round-trips. This paper contributes to the literature by further examining the determinants that affect the use of carsharing, distinguishing between one-way trips and round-trips, and developing models that can be applied in urban environments like Beijing. [ABSTRACT FROM AUTHOR]

A method for evaluating the charging and discharging scheduling potential of electric vehicles in case of the occasional operation of a power grid is proposed, which describes the variations in the users’ responses. Considering the social relationships and mutual learning among users, an evolutionary game model for the optimal response in a complex network is established to study the response rule of the users for the scheduling measures. Wardrop's first traffic equilibrium model is applied to calculate the shortest time for the vehicles to arrive at the charging station from their response sites. Finally, the scheduling potential at a charging station is effectively evaluated considering the influence of the type of activities that the users participate in and their credits. Taking a test traffic network and the traffic network of Beijing third ring road as examples, the responses of the electric vehicle users are studied, and the scheduling potential of electric vehicles in different scenarios is verified. Although an increased incentive can improve the response level of the electric vehicles, it may lead to a reduction in the scheduling capacity, delays in the charging and discharging starting time, and a shortened scheduling duration. [ABSTRACT FROM AUTHOR]

• Developing a data-driven micro-simulation approach based on trajectory data. • Exploring the potential of private home charging post sharing (PHCPS). • PHCPS could provide more charging opportunities for EV users. • PHCPS could decrease EV users' dependence on public charging facilities. • EV users' willingness to share and use private chargers was influential. The lack of charging infrastructure has been one of the main barriers to the uptake of electric vehicles (EVs), and sharing private charging facilities could be a promising solution. This paper explored the potential of private home charging post sharing (PHCPS) (specifically, the impact of PHCPS on the usage of EVs and public and private charging posts) in Beijing using a data-driven micro-simulation approach. In the simulation, EV users' travel and charging behaviors were defined with empirical findings from a trajectory dataset collected from over 76,000 private EVs in January of 2018. We quantified the potential by comparing the results of simulations with and without PHCPS. In the baseline scenario, we found that PHCPS gave rise to 6.32 % increase in the proportion of parking events with accessible charging posts and 33.37 % decrease in the average electricity provided by a public charging post on a working day. The results suggest that PHCPS could provide more charging opportunities for private EV users and decrease private EV users' dependence on public charging stations, revealing the great potential of promoting PHCPS in the EV charging market. [ABSTRACT FROM AUTHOR]

The scale-up of urban electric vehicle (EV) fleets, driven by environmental benefits, is resulting in surging aggregate energy demands that may reshape a city's power supply. This paper establishes an integrated data-driven assessment model for investigating the energy use (kWh) patterns and charging load (kW) profiles of urban-scale EV fleets. To this end, urban EV operating and operational datasets are linked with climate data and vehicle specifications. Four vehicle fleet types are distinguished: private, taxi, rental, and business fleets. Statistical models regarding distribution analysis, spectrum analysis, and identical distribution tests are employed to analyze the patterns of driving distances, energy consumption, and shares of active charging EVs. The minute-level changes in charging EV numbers and aggregate charging power are examined to reflect the grid load impact. The results show that private light-duty EVs in Beijing consume an average of 9.1 kWh/day, with more charging activities on Fridays. The primary load peaks of light-duty EVs in Beijing usually occur between 11 p.m. and 1 a.m., attributable chiefly to the private fleet's midnight peak load estimated at 28 % of the total daily charging private EV count multiplied by 5.5 kW/EV. Secondary peaks occur between 8 a.m. and 10 a.m. on weekdays for private fleets, and at 4 p.m. for public fleets. Our work can be extensively used for analyses on transport emissions, urban power supply, infrastructure build-ups, and policymaking. • An integrated data-driven model for large-scale EV operation analysis is proposed. • Real-world energy use patterns and load profiles of urban EV fleets are unveiled. • Urban-scale EV big data are utilized for fact-based assessments. • Diverse data sources are linked through spatiotemporal information. [ABSTRACT FROM AUTHOR]

Research has shown, that the replacement of conventional vehicles (CVs) by electric vehicles (EVs) can reduce urban heat island intensity (UHII). This leads not only to a more comfortable climate during summer but also to a reduction of carbon dioxide emissions from the vehicles themselves and to a reduction in the need for air conditioning (AC) and associated emissions. The current study examines to what extent a range of different mobility concepts, can lead to a reduction in urban heat island intensity and carbon dioxide emissions in the city of Beijing, China. For this purpose, the impact that electric vehicles fuelled with the average energy mix, hydrogen vehicles (HyVs) fuelled with hydrogen from electrolysis from the regular energy mix, HyVs fuelled with hydrogen produced through wind power, energy efficient CVs, EVs fuelled with wind power and energy efficient metro light railway (metro) travel have on summer heat island intensity (SHII) and CO 2 -emissions is analysed. The lowest SHII mitigation effect was found for a replacement of CVs with fuel efficient diesel cars while hydrogen cars that are fuelled with hydrogen generated from the average electricity mix even leads to higher SHII and higher CO 2 emissions. The largest SHII mitigation effect was found for a scenario where conventional vehicles are replaced with energy efficient metro travel. The CO 2 saving potential is highest for EVs which are powered with electricity generated from wind turbines, closely followed by HyVs that are fuelled with hydrogen generated from electrolysis of water using wind electricity. The paper helps policymakers and consumers to make the right choices when it comes to environmentally friendly mobility options. • Summer heat island intensity (SHII) is mitigated through different mobility choices. • CO 2 reduction by over 12,000 tons/day is possible through reduced usage of air conditioning. • Energy efficient public transportation leads to the highest mitigation of SHII and CO 2 levels. • Electric and hydrogen cars fuelled with renewable energies mitigate SHII and CO 2 levels. • Hydrogen cars fuelled with non-renewable energies lead to higher SHII and CO 2 levels. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the potential expansion and impacts of Electric Vehicle (EV) market in Beijing, China at the micro level with an agent-based integrated urban model (SelfSim-EV), considering the interactions, feedbacks and dynamics found in the complex urban system. Specifically, a calibrated and validated SelfSim-EV Beijing model was firstly used to simulate how the EV market might expand in the context of urban evolution from 2016 to 2020, based on which the potential impacts of EV market expansion on the environment, power grid system and transportation infrastructures were assessed at the multiple resolutions. The results suggest that 1) the adoption rate of Battery Electric Vehicle (BEV) increases over the period, whereas the rate of Plug-in Hybrid Electric Vehicle (PHEV) almost remains the same; Furthermore, the so-called neighbour effects appear to influence the uptake of BEVs, based on the spatial analyses of the residential locations of BEV owners; 2) the EV market expansion could eventually benefit the environment, as evident from the slight decrease in the amounts of HC, CO and CO 2 emissions after 2017; 3) Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system; 4) the EV market expansion could influence several EV-related transport facilities, including parking lots, refuelling stations, and charging posts at parking lots, in terms of quantity, layout and usage. These results are expected to be useful for different EV-related stakeholders, such as local authorities and manufacturers, to shape polices and invest in technologies and infrastructures for EVs. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging demand is an essential input of charging facility location models. However, charging demand may vary across seasons. In response, this paper first provided insights into the seasonal variance in charging demand using a unique GPS trajectory dataset which contained travel, parking, and charging information of 2,658 private EVs in Beijing. The dataset was collected in January, April, July, and October 2018, which were representative months in winter, spring, summer, and autumn, respectively. Through statistical and spatiotemporal analyses, we found that in winter, EVs got recharged when their state of charge (SOC) was lower: the average SOCs on working days were 51.96%, 48.39%, 50.86%, and 43.50%, in spring, summer, autumn, and winter, respectively. Furthermore, the central urban areas tended to have a higher charging demand in winter. To further explore how the seasonal variance in charging demand may influence infrastructure deployment, we used the classical p-median model to deploy charging facilities with the charging demands in the four seasons, considering the modifiable areal unit problem (MAUP). The results suggested that the seasonal variance did influence the layout of charging facilities under different spatial analysis units (SAUs). The deployment of charging facilities in the central urban areas and outer suburbs tended to be more sensitive to seasonal variance in charging demand. The findings are expected to be useful for charging infrastructure planning in both the transport and power sectors. • Explore seasonal variance in Electric Vehicle (EV) charging demand with big data. • EVs got recharged when their state of charge (SOC) was lower in winter. • The central urban areas tended to have a higher charging demand in winter. • Significant seasonal effects on infrastructure deployment in central and outer areas. • More noticeable seasonal effects with fewer charging infrastructure to be deployed. [ABSTRACT FROM AUTHOR]

• Comparing BEV and HFCV from operational, economic, and environmental perspectives. • Proposing a data-driven and simulation-based multi-objective optimization method. • Using LCA to estimate GHG emissions and energy consumption in C2G life cycle. • Using a real-world trajectory dataset to quantify energy demand at the micro-scale. • Different combinations of fuel and facility have benefits and downsides. In order to evaluate suitability of battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs) in freight transport systems, this paper proposes a data-driven and simulation-based multi-objective optimization method to deploy charging/refueling facilities for BEVs/HFCVs. The model considers three objectives, namely minimizing total system cost, maximizing service reliability, and minimizing greenhouse gas (GHG) emissions. In particular, a data-driven micro-simulation approach is developed to simulate the operation of freight transport systems with different vehicle and facility types based on the analysis of a one-week Global Positioning System (GPS) trajectory dataset containing 63,000 freight vehicles in Beijing. With the model, we compare the suitability of BEVs and HFCVs within three typical scenarios, i.e., BEVs coupled with Charging Stations (BEV-CS), BEVs coupled with Battery Swap Stations (BEV-BSS), and HFCVs coupled with Hydrogen Refueling Stations (HFCV-HRS). The results suggest that BEV-CS has the lowest total system cost: its system cost is 62.5% and 90.3% of the costs in BEV-BSS and HFCV-HRS, respectively. BEV-BSS has the lowest delay time: its delay time is 62.1% and 86.0% of the delay times in BEV-CS and HFCV-HRS, respectively. HFCV-HRS has the lowest GHG emissions: its emissions are 37.3% and 46.9% of the emissions in BEV-CS and BEV-BSS, respectively. The results are expected to be helpful for policy making and infrastructure planning in promoting the development of alternative fuel vehicles. [ABSTRACT FROM AUTHOR]

Dynamic wireless charging (DWC) technology has the potential to promote the uptake of electric freight vehicles (EFVs) by allowing charging on the move. This paper presented a joint DWC lane deployment and operation model based on the trajectory data of actual EFVs. The model was formulated as a multi-objective mixed-integer programming model, with objectives to maximize saved charging time, minimize charging cost, and minimize negative impact on traffic. The model considered various constraints related to investment, facility construction, and charging decisions of EFV drivers. It was first tested on a small network and was further applied into a large-scale scenario in Beijing. The results suggested that DWC could reduce charging time by approximately 0.08–0.11 h per EFV per day within an investment limit of 67 million dollars. The deployment of DWC can be long-term profitable, resulting in a net value of 140.83 million dollars over a 25-year period. [ABSTRACT FROM AUTHOR]

This study addresses the comparative carbon emissions of different transportation modes within a unified evaluation framework, focusing on their carbon footprints from inception to disposal. Specifically, the entire life cycle carbon emissions of High-Speed Rail (HSR), battery electric vehicles, conventional internal combustion engine vehicles, battery electric buses, and conventional internal combustion engine buses are analyzed. The life cycle is segmented into vehicle manufacturing, fuel or electricity production, operational, and dismantlingrecycling stages. This analysis is applied to the Beijing-Tianjin intercity transportation system to explore emission reduction strategies. Results indicate that HSR demonstrates significant carbon emission reduction, with an intensity of only 24 %-32 % compared to private vehicles and 47 %-89 % compared to buses. Notably, HSR travel for Beijing-Tianjin intercity emits only 24 % of private vehicle emissions, demonstrating the emission reduction benefits of transportation structure optimization. Additionally, predictive modeling reveals the potential for carbon emission reduction through energy structure optimization, providing a guideline for the development of effective transportation management systems. [ABSTRACT FROM AUTHOR]

Photovoltaic–energy storage charging station (PV-ES CS) combines photovoltaic (PV), battery energy storage system (BESS) and charging station together. As one of the most promising charging facilities, PV-ES CS plays a decisive role in improving the convenience of EV charging, saving energy and reducing pollution emissions. To promote PV-ES CS and improve the network of charging facilities, it is necessary to explore its comprehensive benefits. Firstly, to make full use of peak-to-valley electricity price difference and consume the power generated by the PV, this paper introduces the energy management strategy of the station based on time-of-use (TOU) electricity price. Secondly, from the perspective of multiple beneficiaries, a comprehensive benefits analysis model of charging station is proposed, including the benefits of PV-ES CS, power grid and society. Finally, the comprehensive benefits of the new charging station are analyzed through a PV-ES CS in Beijing. The impact of the construction cost reduction (including BESS cost and PV cost) and subsidy decline on the economy of the charging station is discussed. The government should further enrich charging stations subsidies. The results show that the social and economic benefits brought by PV-ES CS are far greater than the economic benefits of the station itself. With the development of the new charging facility in the future, it will bring considerable social and economic benefits and play a vital role in saving energy and reducing emissions. • The paper analyzes the benefits of charging station integrated photovoltaic and energy storage, power grid and society. • The social and economic benefits brought by PV-ES CS are more significant, compared with its own economic benefits. • A comprehensive benefit analysis model of charging station is proposed. • The impact of the construction cost reduction and subsidy decline on the economy of the charging station is discussed. • Some policy recommendations are put forward based on the analysis. [ABSTRACT FROM AUTHOR]

• This paper explores the role of fast charging in reducing range anxiety. • 1.7 million charging and driving records of electric vehicles in Beijing were used. • The charging rate is negatively related to range anxiety. • Temperature increases the effects of charging rate on the range anxiety. • The effect of range anxiety decreases with the growth of charging piles. Range anxiety is a psychological condition in which people worry that the limited battery capacity of electric vehicles (EV) will result in running out of power before reaching their destination. In this paper, with the application of quantile regression, 1.7 million charging and driving records for EVs in Beijing are used to explore how fast charging impacts on range anxiety. As state of charge (SOC) is highly related to range anxiety, this paper mainly focuses on the relationship between the charging rate and the SOC, which can be further extended to range anxiety. The results show that the charging rate is negatively related to the SOC, which indicates that fast charging could improve the charging rate and then reduce both the SOC and range anxiety. Temperature positively moderates the effects of the charging rate on range anxiety, which indicates that fast charging would be more effective in reducing range anxiety when it is a cold weather. Additionally, the quantity of charging piles negatively moderates the relationship between the charging rate and range anxiety. However, range anxiety may also influence the choice of charging rate, which might bring a reverse causality problem between the charging rate and the SOC, and result in an exaggerated significance of our analysis. After discussing the endogeneity of our regression results, we believe the possible bias is still totally acceptable. [ABSTRACT FROM AUTHOR]

The market of electric taxi has achieved rapid development in China recently, while further development is hindered by technical factors such as battery mileage and charging. First-tier cities as Beijing have explored and innovated in electric taxi business models to make up for the deficiencies caused by technological immaturity at the present stage. From the perspective of value chain, this paper carries out an analysis on current status of electric taxi business models in Beijing, and proposes a model for analyzing its economy. Moreover, a comparative analysis was conducted on the economy and sensitivity of battery changing and battery charging models based on field research data acquired from Pinggu District of Beijing. The research findings are as follows: a) overall, the battery changing model is better than the battery charging model economically. b) the sensitivity of the battery changing model to project period, carbon price and charging price is significantly higher than that of the charging model, because of its higher service frequency and greater service quantity. c) among all the influencing factors, charging price has the greatest influence on the two models. d) in the battery changing model, the breakeven point of the whole business model gradually declines as electricity price rises, so the battery changing model still can survive even without government subsidies. e) owing to vehicle loss, battery loss and other reasons, the two models have an optimal period respectively, but the greater loss of the battery changing model results in that its optimal project period is shorter than that of the battery charging model. [ABSTRACT FROM AUTHOR]

• A deep learning approach to predicting the electric vehicle (EV) charging demand. • A real-world large EV trajectory dataset was used for model training and validation. • The deep learning approach outperformed traditional time series prediction models. • Input data structures, and sample sizes were not influential to prediction accuracy. Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to the operation of EV fleets and charging stations. This paper develops a Long Short-Term Memory (LSTM) neural network to predict the EV charging demand at the station level for the next few hours (e.g., 1–5 h), using a unique trajectory dataset containing over 76,000 private EVs in Beijing in January 2018. To explore the performance of the LSTM model, we set up four scenarios by 1) comparing LSTM against two typical time series prediction models, i.e., the Auto-Regressive Moving Average model (ARIMA), and the Multiple Layer Perceptron model (MLP), 2) and investigating how different input data structures, sample sizes, and time spans and intervals would influence model accuracy. The results suggest that the LSTM model outperformed the ARIMA, and MLP models, and their MAPE1 values are 6.83 %, 21.58 %, and 18.31 %, respectively. In addition, we find that the time span and interval tend to be more influential to the LSTM model's prediction accuracy than input data structures, and sample sizes. In general, the LSTM model with a shorter time span or interval (e.g., 1 h) would perform better. [ABSTRACT FROM AUTHOR]

The development of electric vehicles (EVs) is expected to play an important role in achieving the emission reduction targets in the transportation sector by many countries and regions, including China's dual carbon goals. A bottleneck to the mass roll-out of EVs is the limited charging facilities. This paper considers the planning of charging facilities for a new developing area in a metropolis, and an optimization model for charging station planning based on the dynamic transportation system is proposed. The proposed model is developed using an objective framework that considers the spatio-temporal characteristics of EV charging demand in order to minimize the overall cost while the constraints from suppliers and drivers are met. The Voronoi diagram is used to determine the final service boundary of each charging station, and the effect is verified in the planning of charge stations for the Yizhuang new town in Beijing. The case study confirms that the proposed method can optimize the charging facilities that fit well with the traffic network conditions. Furthermore, it is shown that the charging demand varies in accordance with the population density and regional functionality in different areas. [ABSTRACT FROM AUTHOR]

Demand-side decarbonization and electrification promote a strong coupling between electric vehicles (EVs) and buildings. From a building-centric perspective, this paper established a survey-based simulation platform for charging system design, which supports annual simulations with a 15-min interval. Field survey data of private vehicles in two office buildings located in Beijing (a metropolis) and Ruicheng (a county in China) were used as inputs and for validation. The platform was applied to analyze workplace charging and give suggestions for charging system design. First, the city size greatly influences vehicle behavior and charging demands. This study indicates an unusual double-peak charging demand on weekdays in the county case due to different vehicle behavior from the urban cases. Second, slow charging at workplaces can almost meet the intra-urban travel demand of private EVs. Specifically, chargers with a rated power of 3–4 kW can achieve a guarantee level of EV use of SOC 0.999 = 20%. Third, the low solar self-consumption rate of current PV charging systems (generally 18%∼42%) mainly results from significant PV surpluses on weekends and in summer. These findings emphasize building-centric planning of EV-related infrastructures and proper charging strategies for energy interaction between EVs and buildings. [Display omitted] • Electric vehicle (EV) behavior is investigated from a building-centric perspective. • A simulation platform is developed for charging system design (τ = 1 yr, Δ τ = 15 min). • City size greatly influences vehicle behavior and charging demands. • Slow charging at workplace can meet the intra-urban travel demand of private EVs. • PV surpluses on weekends and in summer result in low solar power self-consumption. [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) are promoted as a foreseeable future vehicle technology to reduce dependence on fossil fuels and greenhouse gas emissions associated with conventional vehicles. This paper proposes a data-driven approach to improving the electrification rate of the vehicle miles traveled (VMT) by taxi fleet in Beijing. Specifically, based on the gathered real-time vehicle trajectory data of 46,765 taxis in Beijing, we conduct time-series simulations to derive insights for the public charging station deployment plan, including the locations of public charging stations, the number of chargers at each station and their types. The proposed simulation model defines the electric vehicle charging opportunity from the aspects of time window, charging demand and charger availability, and further incorporates the heterogeneous travel patterns of individual vehicles. Although this study only examines one type of fleet in a specific city, the methodological framework is readily applicable to other cities and types of fleet with similar dataset available, and the analysis results contribute to our understanding on electric vehicle’s charging behavior. Simulation results indicate that: (i) locating public charging stations to the clustered charging time windows is a superior strategy to increase the electrification rate of VMT; (ii) deploying 500 public stations (each includes 30 slow chargers) can electrify 170 million VMT in Beijing in two months, if EV’s battery range is 80km and home charging is available; (iii) appropriately combining slow and fast chargers in public charging stations contributes to the electrification rate; (iv) breaking the charging stations into smaller ones and spatially distributing them will increase the electrification rate of VMT; (v) feeding the information of availability of chargers in charging stations to drivers can increase the electrification rate of VMT; (vi) the impact of stochasticity embedded in the trajectory data can be significantly mitigated by adopting the dataset covering a longer period. [ABSTRACT FROM AUTHOR]

This paper proposes a system dynamics model to develop a real-time charge pricing (RCP) mechanism of electric vehicles (EVs). The model includes six modules: power consumption of EVs, generator set dispatching, charge pricing, user response, benefit evaluation of all stakeholders and charging stations' life-cycle net income. We consider four charge pricing scenarios and design a RCP mechanism for Beijing according to the simulation results. Sensitivity analysis proves that the model is robust, and the increased charging power of EVs is beneficial for charging service operators. The empirical results indicate that RCP based on the peak-valley time-of-use tariff is propitious for the existing development scale of EVs. In addition, the government subsidies are important to drive EV development in the initial period. However, it should be phased out to reduce the financial burden accompanying the amplification of the scale of EVs. [ABSTRACT FROM AUTHOR]

• Synchronous multi-parameter prediction for battery systems is investigated. • All-climate and all-state application in electric vehicles can be obtained. • A developed pre-dropout technique is introduced to prevent LSTM from overfitting. • Different models can be intelligently applied in driving/charging states. • The predicted horizons are adjustable to provide adequate emergency time. Voltage, temperature, and state of charge (SOC) are the main characterizing parameters for various battery faults that can cause these parameters' abnormal fluctuations. Accurate prediction for these parameters is critical for the safe, durable, and reliable operation of battery systems in electric vehicles. This paper investigates a new deep-learning-enabled method to perform accurate synchronous multi-parameter prediction for battery systems using a long short-term memory (LSTM) recurrent neural network. A year-long dataset of an electric taxi was retrieved at the Service and Management Center for electric vehicles (SMC-EV) in Beijing to train the LSTM model and verify the model's validity and stability. By taking into account the impacts of weather and driver's behaviors on a battery system's performance to improve the prediction accuracy, a Weather-Vehicle-Driver analysis method is proposed, and a developed pre-dropout technique is introduced to prevent LSTM from overfitting. Besides, the many-to-many(m-n) model structure using a developed dual-model-cooperation prediction strategy is applied for offline training the LSTM model after all hyperparameters pre-optimized. Additionally, the stability and robustness of this method have been verified through 10-fold cross-validation and comparative analysis of multiple sets of hyperparameters. The results show that the proposed model has powerful and precise online prediction ability for the three target parameters. This paper also provides feasibility for synchronous multiple fault prognosis based on accurate parameter prediction of the battery system. This is the first of its kind to apply LSTM to the synchronous multi-parameter prediction of the battery system. [ABSTRACT FROM AUTHOR]

• A new bi-level planning model is proposed that integrates the interests of both charging station investors and EV users, where the upper model has the global economic return as the optimization objective, and the lower model has the EV user's satisfaction with the charging service (charging cost) as the optimization objective. The global economic cost is optimized while the service satisfaction of the user is improved. • The time cost, economic cost, and mileage anxiety are considered together to characterize the service satisfaction of EV users. Specifically, the charging queuing time, driving distance, desired charging volume, and actual charging volume are used to reflect the charging cost of EV users and to reflect the autonomy of EV users more accurately, and a combination of fast and slow charging posts are used for planning to meet the needs of different users and grid constraints, to improve the operational efficiency of charging stations and reduce the charging queuing time of EV users. • The conventional method and the bi-level planning method are solved separately for a region of Beijing, and the effectiveness of the proposed method is verified by comparing factors such as investors' returns, users' charging costs, and queuing times. The rapid development of electric vehicles (EV) has placed greater demands on the planning and construction of public electric vehicle charging stations (EVCS). As EV users are highly autonomous in their charging behavior, the interests of investors and EV users are mutually affected and challenging to balance. Therefore, this paper proposes a bi-level planning model to balance the interests of investors and EV users and optimize global economic costs while improving the service satisfaction of users. The upper-level model aims to optimize the economic cost. In contrast, the lower-level model aims to optimize the service satisfaction of EV users and characterizes the charging satisfaction of EV users through the costs of charging queuing time, distance traveled, desired to charge volume, and actual charging volume, to more accurately reflect the autonomy of EV users. A combination of fast and slow charging piles is also used for planning to meet the needs of different users and improve charging stations' operational efficiency. Finally, a case study is conducted in an area of Beijing to verify that the optimization model has the advantages of low global economic cost, short charging queuing time for users, and high service satisfaction. [ABSTRACT FROM AUTHOR]

• The capacity optimization model of the integrated photovoltaic- energy storage-charging station was built. • The case study bases on the data of 21 charging stations in Beijing. • The construction of the integrated charging station shows the maximum economic and environment benefit in hospital and minimum in residential. • A decline in energy storage costs increases the benefits of all-scale investments, an increase in electric vehicles promotes the benefits of small-scale investments, expansion of the peak-to-valley price distance increases the benefits of large-scale investments. The coupled photovoltaic-energy storage-charging station (PV-ES-CS) is an important approach of promoting the transition from fossil energy consumption to low-carbon energy use. However, the integrated charging station is underdeveloped. One of the key reasons for this is that there lacks the evaluation of its economic and environmental benefits. Based on the electricity load of different types of buildings and the data of electric vehicle charging stations in Beijing, this paper analyzes the economic and environmental benefits of integrated charging station developed over different scales by the capacity optimization model. Said benefits are shown to differ considerably with different locations (building types): the construction of the integrated charging station in places where the electricity load is high at night, and where the curve of daytime electricity consumption is consistent with the PV generation curve (e.g., hospitals), shows maximal economic benefits and carbon dioxide emissions reduction. In other (e.g., residential) areas, where power consumption is lower across both day and nighttime hours, the economic benefits and carbon dioxide emissions reduction are lowest. The economic and environmental benefits of the integrated charging station also markedly differ on different scales: with scale expansion, the rate of return on investment and the carbon dioxide emissions reduction first increase and then decrease. A decline in energy storage costs increases the economic benefits of all integrated charging station scales, an increase in EVs increases the economic benefits of small-scale investments, and expansion of the peak-to-valley price difference increases the economic benefits of large-scale investments. [ABSTRACT FROM AUTHOR]

Charging behavior is essential to understanding the real performance and evaluating the sustainability of battery electric vehicle (BEV) development and providing the basis for optimal infrastructure deployment. However, it is very hard to obtain the rules, due to lack of the data support, etc. In this research, analyzing the charging behavior of users with private charging piles (PCPs) is carried out based on the real vehicle data of 168 BEV users in Beijing, covering 8825 charging events for a one-year duration. In this study, the charging behaviors are defined by five indexes: the starting state of charge (SOC) of batteries, charging location selection, charging start time, driving distance, and duration between two charging events. To further find the influencing rules of the PCPs owning state, we setup a method to divide the data into two categories to process further analysis and comparison. Meanwhile, in order to better observe the impact of electric vehicle charging on the power grid, we use a Monte Carlo (MC) simulation to predict the charging load of different users based on the analysis. In addition, an agent-based trip chain model (ABTCM), a multinomial logistic regression (MLR), and a machine learning algorithm (MLA) approach are proposed to analyze the charging behavior. The results show that with 40% or lower charging start SOC, the proportion of users without PCPs (weekday: 55.9%; weekend: 59.9%) is larger than users with PCPs (weekday: 45.5%; weekend: 42.6%). Meanwhile, users without PCPs have a certain decrease in the range of 60–80% charging start SOC. The median charging time duration is 51.44 h for users with PCPs and is 17.25 h for users without PCPs. The charging peak effect is evident, and the two types of users have different power consumption distributions. Due to the existence of PCPs, users have lower mileage anxiety and more diverse charging time choices. The analysis results and method can provide a basis for optimal deployment and allocation of charging infrastructure, and to make suitable incentive policies for changing the charging behavior, targeting the carbon neutral objectives. [ABSTRACT FROM AUTHOR]

In this paper, we present a case study on planning the locations of public electric vehicle (EV) charging stations in Beijing, China. Our objectives are to incorporate the local constraints of supply and demand on public EV charging stations into facility location models and to compare the optimal locations from three different location models. On the supply side, we analyse the institutional and spatial constraints in public charging infrastructure construction to select the potential sites. On the demand side, interviews with stakeholders are conducted and the ranking-type Delphi method is used when estimating the EV demand with aggregate data from municipal statistical yearbooks and the national census. With the estimated EV demand, we compare three classic facility location models – the set covering model, the maximal covering location model, and the p -median model – and we aim to provide policy-makers with a comprehensive analysis to better understand the effectiveness of these traditional models for locating EV charging facilities. Our results show that the p -median solutions are more effective than the other two models in the sense that the charging stations are closer to the communities with higher EV demand, and, therefore, the majority of EV users have more convenient access to the charging facilities. From the experiments of comparing only the p -median and the maximal covering location models, our results suggest that (1) the p -median model outperforms the maximal covering location model in terms of satisfying the other’s objective, and (2) when the number of charging stations to be built is large, or when minor change is required, the solutions to both models are more stable as p increases. [ABSTRACT FROM AUTHOR]

The national and municipal government of China enacted a series of regulations and policies to stimulate/promote the development of new energy vehicles, in order to mitigate the increasingly serious carbon emissions, environmental pollution, and energy shortage. As a large metropolitan and populated city subject to the notorious air pollution, Beijing has been making a remarkable progress in the large-scale demonstration of new energy vehicles in recent years, which could result in a significant impact on both transport and electricity sectors. As a result, there is an urgent necessity to study the characteristics of the large-scale new energy vehicles adoption for a deep understanding of operational status (e.g., energy consumption and battery charging patterns) and benefits, as well as charging facilities. Based on the operational data collected from realistic electric-taxi demonstration in Beijing, the driver behavior and charging characteristics are examined in this paper. The energy consumption and efficiency of two representative electric-taxi platforms are compared, and the influence of the driving schedules is discussed. The results show that the average driving distance per day of these electric taxes is 117.98 km, and 92% of drivers recharge their cars twice per day. Further study shows that the drivers make two trips per day, and the two peaks in the distribution of departure and arrival times coincide with the rush hour in the morning and evening. The taxi recharge duration is largely influenced by the charging power. Generally, the associated battery SOC (state of charge) swing is between 40% and 100%. By evaluating the energy consumption of 282 trips recorded in 2013 and 2014, we find that the two platforms have similar energy efficiency. The micro-trips method is utilized to probe the correlation of energy consumption and average speed. [ABSTRACT FROM AUTHOR]

To reduce traffic congestion and protect the environment, license plate control (LPC) policy has been implemented in Beijing since 2011. In 2019, 100,000 vehicle license plates were distributed, including 60,000 for electric vehicles (EVs) and 40,000 for gasoline vehicle (GVs). However, whether the current license plate allocation is optimal from a social welfare maximization perspective remains unclear. This paper proposes a two-level Stackelberg game, which portrays the interaction between vehicle applicants and the government to quantify the optimal number of EV license plates under the LPC policy in Beijing. The equilibrium number of EV license plates derived from the Stackelberg model is 58,800, which could increase the social welfare by 0.38%. Sensitivity analysis is conducted to illustrate the impact of important influential factors — total license plate quota, vehicle rental fee, and energy price — on EV adoption. The LPC policy under COVID-19 is also studied through a scenario analysis. If the government additionally increases the total quota by 20,000, 24% could be allocated to GV and 76% to EV. One third reduction of the current vehicle rental fee could increase EV license plates by 10.5%. In terms of energy prices, when gasoline price is low, reducing electricity prices could contribute to EV adoption significantly, while that effect tapers off as gasoline prices increase. • The optimal license plate allocation between EVs and GVs in Beijing is analyzed. • A Stackelberg game is proposed to model stakeholders' interaction under LPC policy. • The optimal license plate allocation policy under COVID-19 is also studied. • One third reduction of vehicle rental fee could increase EV adoption by 10.5%. [ABSTRACT FROM AUTHOR]

• Develop an agent-based model of electric vehicle (EV) and vehicle-to-grid (V2G) adoption. • The EV-V2G model can explicitly simulate social influences on the EV and V2G adoption. • Only 3.3% of survey participants were familiar with V2G. • V2G selling price and advertisement were not very influential to the diffusion of V2G. The potential widespread adoption of Electric Vehicles (EVs) has received considerable attention across the globe. However, as a promising technology for both EVs and smart grid, Vehicle-to-Grid (V2G) tended to receive much less attention. This paper developed an agent-based joint EV and V2G model to simultaneously simulate how EVs and V2G might diffuse across space and over time, with empirical findings from a questionnaire survey in Beijing. In particular, random forest models were developed with the survey data to generate each agent's preferences and attitudes towards EVs and V2G. The joint model also considered three typical levels of social influence, i.e., global influence, neighbor effect, and friendship effect, in the diffusion of EVs and V2G. Finally, the joint model was tested through several "what-if" scenarios, considering different V2G prices, EV/V2G advertisement intensities, and vehicle purchase restrictions. The survey results suggested that 67.7% of the respondents were familiar with EVs, but only 3.3% of them were familiar with V2G. However, over 70% of them would/might try V2G given that they had an EV. The model results suggested that the number of CV applicants was 6.19 times that of BEV applicants in 2030 in the baseline scenario, and only 27.8% of BEV users adopted V2G. Furthermore, V2G selling price, EV/V2G advertisement, and dedicated PHEV purchase permits were not very influential to the diffusion of V2G. The outcomes would be helpful for EV- and V2G-related stakeholders in policy making and technology investment. [ABSTRACT FROM AUTHOR]

• A novel tri-stage online dispatch framework for a microgrid is proposed. • The proposed framework coordinates the EV charging behavior at different timescales. • Multiple uncertainties introduced by EV, PV, and load are taken into account. • The real-world data of eight EVs in Beijing is collected to model EV charging behavior. This paper proposes a novel tri-stage online dispatch framework that coordinates the charging behaviors of electrical vehicles (EVs) within an AC/DC hybrid microgrid in the presence of uncertainties. A day-ahead scheduling model is proposed as the first stage to optimize the total operational cost for the next day (24 h). In the second stage, a receding horizon manner is adopted to adjust the day-ahead scheduling results, which can compensate for unpredictable disturbances. Both the first and second stages are operated with a time scale of one hour, which is, however, insufficient in capturing the operations of EVs. Hence, the third stage is introduced with the stochastic model predictive control (SMPC) method in a time scale of 10 min to further consider uncertainties of load, PV, EVs. The real-world behavioral data of eight private EVs in Beijing are used to evaluate the performance of our dispatch model. The simulation results show that compared with some traditional online dispatch methods, the total operational cost of the proposed dispatch framework is significantly reduced. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• An agent-based spatial integrated model was used to simulate electric vehicle (EV) diffusion. • Cost of on-board battery was influential to the uptake of plug-in hybrid EVs. • Battery capacity and fast charging infrastructures had little influence on EV penetration rate. • Battery and charging technologies would heavily influence the EV diffusion path at the micro level. Technology innovations are expected to overcome several barriers to the uptake of Electric Vehicles (EVs). This paper explored the role of battery and charging technologies in the diffusion of EVs. Specifically, four groups of "what-if" scenario in Beijing were set up to assess the potential impacts of battery cost (i.e., EV price), battery capacity (i.e., driving range), battery swap stations and fast charging posts on the expansion of EV market. An agent-based spatial integrated model (SelfSim-EV) was used to simulate how vehicle consumers might respond to these technological innovations over time. The results suggested that 1) Plug-in Hybrid Electric Vehicle (PHEV) became competitive when its sale price decreased over time at a yearly rate of 8%, due to the decrease in battery cost; 2) Increasing the driving range of Battery Electric Vehicle (BEV) had little influence on the total number of vehicle purchasers, but did increase electricity consumption; 3) Deploying fast charging infrastructures, i.e., battery swap stations and fast charging posts, had little influence on the uptake of EVs at the macro level, suggesting that fast charging facilities might not be necessary at the early stage of EV development. [Display omitted] [ABSTRACT FROM AUTHOR]

Electrifying transportation in the form of the large-scale development of electric vehicles (EVs) plays a pivotal role in reducing urban atmospheric pollution and alleviating fossil fuel dependence. However, the rising scale of EV deployment is exposing problems that were previously hidden in small-scale EV applications, and the lack of large-scale EV operating data deters relevant explorations. Here, we report several issues related to the battery utilization and energy consumption of urban-scale EVs by connecting three unique datasets of real-world operating states of over 3 million Chinese EVs, operational data, and vehicle feature data. Meanwhile, by incorporating climatic data and EV data outside China, we extend our models to several metropolitan areas worldwide. We find that blindly increasing the battery energy of urban EVs could be detrimental to sustainable development. The impact of changes in the energy consumption of EVs would be exacerbated in large-scale EV utilization, especially during seasonal shifts. For instance, even with a constant monthly driving demand, the average energy consumption of Beijing light-duty EVs would change by up to 21% during winter-spring shifts. Our results may also prove useful for research on battery resources, urban power supply, environmental impacts, and policymaking. [ABSTRACT FROM AUTHOR]

• A 3-level fragment division method is proposed for actual vehicle data processing. • The influencing factors of electric vehicle energy consumption are discussed. • A novel data-driven energy consumption prediction framework is proposed. • The driving conditions prediction is integrated in the proposed framework. With increasing mass-adoption of electric vehicles, the energy consumption has become a key performance index to electric vehicle drivers, automakers and policy-makers. Accurate and real-time energy consumption prediction under real-world driving conditions is essential for alleviating the 'range anxiety' and can provide support for optimal battery sizing, energy-efficient route planning and charging infrastructures operation. In this paper, real-world driving data collected from fifty-five electric taxis in Beijing city are obtained and divided into three-level driving fragments. The influencing factors of energy consumption, including vehicle-, environment-, and driver-related factors, are extracted and studied. With the extracted key influencing factors, a novel machine learning-based energy consumption prediction framework integrated with driving condition prediction is proposed and used in actual energy consumption prediction. The real-world trip test results show that a root mean squared error of 0.159kWh (RMSE) and a mean absolute percentage error 12.68% (MAPE) are reached, the RMSE and the MAPE are respectively reduced by 32.05% and by 30.14% compared to the conventional method. [ABSTRACT FROM AUTHOR]