Electric Vehicle Sharing Research Articles

Modelling the complementarity and flexibility between different shared modes available in smart electric mobility hubs (eHUBS)

eHUBS are physical locations that integrate two or more electric shared mobility modes. As they provide transport users easier access to a wide range of transport modes, multimodal behaviour is expected to be more common. However, this issue has not been addressed in previous stated preference studies on mode choices involving innovative transport modes. In this study, multimodal behaviour is explicitly addressed both in measurement and in modelling by adopting the multiple discrete–continuous (MDC) modelling framework in contrast to discrete choice models. Instead of asking transport users to indicate the most preferred alternative, they were allowed to choose more than one alternative by allocating trips between several modes. This study aims to answer two questions: 1) whether there is complementarity between the multiple shared modes offered in eHUBS and 2) how would transport users adapt when one of the shared modes that they plan to use becomes unavailable. Using stated mode choice data of non-commuting trips from transport users whose current mode is driving a private car in Manchester, UK, several models under the MDC framework were estimated including Multiple Discrete-Continuous Extreme Value (MDCEV) model, mixed MDCEV model, and the extended Multiple Discrete Continuous (eMDC) model. The results show that there is complementarity between shared electric vehicle (EV) and electric bike (e-bike) offered in the eHUBS. In addition, the research show that the flexibility between those two shared modes is stronger than assumed in the MDCEV model, and common preference heterogeneity cannot fully account for this phenomenon.

Navigating Transitions: How Electric Vehicle Sharing Is Shaping Sustainable Mobility in Chinese Cities

Navigating Transitions: How Electric Vehicle Sharing Is Shaping Sustainable Mobility in Chinese Cities

Data-driven vulnerability analysis of shared electric vehicle systems to cyberattacks

Disruptions to electric vehicle charging infrastructure can hinder consumers’ confidence and impact the performance of shared electric vehicle (SEV) systems. This study focuses on SEV systems’ vulnerability to emerging cyberattacks at the system level utilizing city-scale SEV trajectory data in Beijing, China. A data-driven simulation model is developed to account for realistic charging demand–supply interactions extracted from the data. The results show that among different types of disruptions, cybersecurity threats present unique challenges to SEV systems. Compared with attacking the most highly utilized charging stations, attacking popular ones on the city’s outskirts is more likely to significantly increase stress on the charging infrastructure and reduce the accessibility of SEVs. Meanwhile, the interactions between SEVs and infrastructure could either amplify the impacts by triggering cascading failures or relieve the adversarial effects. These findings provide insights into designing policies and solutions to enhance the resilience of SEV systems against cyberattacks.

Optimizing Station Placement for Free-Floating Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Beijing FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Beijing, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies.

Carbon, Particulate Matter, and Heavy Metal Human Toxicity Footprint of IoT-based Micro Electric Vehicle (EV) Sharing for Urban Mobility

Purpose : The purpose of this study is to calculate and compare carbon footprint, particulate matter footprint, and heavy metal human toxicity footprint with operation data of IoT-based Micro Electric Vehicle (EV) Sharing for Urban Mobility, which was conducted in Chungbuk Innovation City (Jinchen Ducksaneup, Eumsung Mangdongmyun), Ochangeup (Cheongju), Osongeup(Cheongju) as a MOLIT Smart City Challenge Project, with petrol and diesel vehicles.Methods : In the assessment area, ten new Micro Electric Vehicles were used within the ‘TAYOU’ platform, which is an IoT-based micro electric vehicle sharing platform for smart mobility. These vehicles were operated for two months (1,021 times and 1,996 km). Data including vehicle numbers, charging start and end times, operating durations, return times, distances traveled (km), user information (name, driver's license, telephone number), and more were collected. Using this data, the carbon, particulate matter, and heavy metal human toxicity footprint were calculated and compared with those of petrol and diesel vehicles.Results and Discussion : The carbon footprints were 188.74 kg CO<sub>2</sub> eqv., 409.67 kg CO<sub>2</sub> eqv., 389.55 kg CO<sub>2</sub> eqv., in IoT-based Micro Electric Vehicle (EV), and petrol and diesel vehicle, respectively. The particulate matter footprint was 263.36 gPM<sub>2.5</sub> eqv., 21.37 gPM<sub>2.5</sub> eqv., and 439.47 gPM<sub>2.5</sub> eqv., respectively. The heavy metal human toxicity footprint was calculated at 0.029 g1.4 DCB eqv., 8.26 g1.4 DCB eqv., and 7.42 g1.4 DCB eqv., respectively; even though the pilot project was completed in just two months, the results showed a meaningful reduction in carbon, particulate matter, and heavy metals. If the service system can be extended, we can expect a more significant decrease in all environmental footprints. In the transition period from petrol and diesel vehicles to electric vehicles for carbon neutrality in road transportation systems, these results showed that electric vehicles can contribute significantly to reducing carbon, particulate matter, and heavy metals.Result : Carbon, particulate matter, and heavy metals reduction efforts are continuously needed in transportation. This study calculated and compared carbon, particulate matter, and heavy metal human toxicity footprints with electric, petrol, and diesel vehicles. As a result, in the future, this new small electric mobility (“TAYOU” Smart mobility vehicle) sharing service system can support and contribute to reducing carbon, particulate matter, and heavy metals emissions and footprints.

Environmental benefits and supply dynamics of electric vehicles sharing: From a systematic perspective of transportation structure and trip purposes

This study elucidates the environmental benefits and the supply dynamics of electric vehicle sharing (EVS) across various trip modes and purposes. A comprehensive survey involving 1,784 participants is carried out to gauge travelers’ latent preferences to EVS. An integrated model of the structural equation modeling (SEM) and the partial proportional odds model (PPOM) is developed to identify the interactive relationships between EVS and other trip modes. A before-and-after analysis is conducted to unveil the environmental benefits of EVS across different trip modes and purposes, based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). In particular, marginal effect analysis is performed to assess the environmental benefits and supply dynamics as EVS adoption levels vary. The findings highlight a remarkable reduction in energy consumption and CO2 emissions in Beijing due to EVS operations, amounting to reductions of 168,000 kgce and 406,000 kgCO2, respectively. This study provides policymakers with insights into EVS’s environmental potentials and optimization recommendations.

A joint model of infrastructure planning and smart charging strategies for shared electric vehicles

This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles (SEVs). The model takes into account two prevalent smart charging strategies: the Time-of-Use (TOU) tariff and Vehicle-to-Grid (V2G) technology. We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users, utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset. Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations. For SEV operators, the use of TOU and V2G strategies could potentially reduce charging costs by 17.93% and 34.97% respectively. In the scenarios with V2G applied, the average discharging demand is 2.15 ​kWh per day per SEV, which accounts for 42.02% of the actual average charging demand of SEVs. These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.

Optimal performance selection of sustainable mobility service projects based on IFSS - Prospect theory - VIKOR: A case study of electric vehicle sharing program.

Current mobility trend indicates that the number of private cars will decline in the near future. One of the reasons for this trend is the development of Mobility as a Service (MaaS), which in conjunction with information and communication technologies (ICT) drive the application of transport services in smart city, respond to environmental issues, and provide users with reliable mobility. Electric vehicle sharing (EVS) travel has been regarded as a feasible mainstream model of sustainable mobility services in the future, which can effectively improve the utilization rate of motor vehicles, solve the problems of traffic congestion, environmental pollution and urban land, and promote low-carbon and sustainable development. To help electric vehicle operators improve service quality, the establishment of EVS program service performance evaluation is an urgent problem to be solved. Based on this, this paper firstly constructs the evaluation index system from 5 aspects: electric vehicle, charge station, user experience, payment and intelligent services through literature review and Delphi method. Secondly, the criteria importance though intercriteria correlation (CRITIC) and the improved G1 method are introduced to overcome the shortcomings of the single method, and the combined weights are calculated by the multiplication normalization method. Finally, a decision model based on intuitionistic fuzzy soft set (IFSS)-prospect theory and VIse Kriterijumski Optimizacioni Racun (VIKOR) method is constructed to select the best service performance of EVS program, and its feasibility and effectiveness are verified by sensitivity analysis and comparative analysis. The result shows that EVCARD is the best performing EVS program, and shared electric vehicle and charge station are the key factors to be considered in the selection. This study provides scientific and feasible guidance for the optimal service performance selection of EVS programs, which is of great significance for users to choose EVS programs.

Reliability Evaluation of Electric Vehicle Sharing Considering Charging Load Transfer in a Distribution Network Containing Microgrids

Reliability Evaluation of Electric Vehicle Sharing Considering Charging Load Transfer in a Distribution Network Containing Microgrids

Congestion-Aware Rebalancing and Vehicle-to-Grid Coordination of Shared Electric Vehicles: An Aggregative Game Approach

Congestion-Aware Rebalancing and Vehicle-to-Grid Coordination of Shared Electric Vehicles: An Aggregative Game Approach

A Charging Planning Method for Shared Electric Vehicles with the Collaboration of Mobile and Fixed Facilities

Faced with the charging difficulties of free-floating shared electric vehicles and the high cost of single-demand mobile charging, this paper proposes a cooperative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the system charging total cost minimization model, which searches for the optimal charging scheme and number of mobile charging vehicles. The lower level model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for the mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% when compared to the fixed-only charging scheme, and by 14.9% when compared to the mobile-only charging scheme.

How does travel satisfaction affect preference for shared electric vehicles? An empirical study using large-scale monitoring data and online text mining

Shared electric vehicles (SEVs) are an emerging mode of transportation that offers advantages in environmental protection. It is unclear what role travel satisfaction plays in operations. User's feedback is an important factor to the future development of SEVs. How to accurately collect user's feedback and predictions is the focus of attention. In this paper, we innovatively used sentiment analysis to construct the travel satisfaction index based on text data from the largest social network platforms in China. In addition, we used a vector autoregression model to analyse and confirm that users' trip satisfaction is an important factor influencing subsequent use of SEVs. A large-scale data set of travel records combined with point of interest information of SEVs that covered 1.64 million records of 3,100 vehicles. The results showed that low rates of satisfaction of SEVs are attributed to the fault rate of vehicles and poor services. Arranging SEV services around restaurants and commercial areas will result in higher user's satisfaction. Moreover, an increase in user's satisfaction will increase the usage frequency of SEVs in future trips. An increase in satisfaction will reduce rental times of returning users in the short term, but it has no effect on travel distance.

A Study on the Formation and Distribution Mechanisms of the Demand for Shared Electric Vehicles

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.

A hybrid approach based energy management for building resilience against power outage by shared parking station for EVs

A major contribution of this work is to develop an efficient hybrid technique for building energy resilience against natural disasters, which cause power outages for commercial and residential buildings. The novelty of the proposed method is the combination of Chicken Search Optimization and the Spike Neural Network learning algorithm. The buildings are designed using shared electric vehicle parking station. Buildings are modeled with peer-to-peer functionality. Electric vehicle inside the parking station are available in a variety of forms, as the commercial building utilizes parking station at day time, also the residential building mostly utilizes at midnight. These variations help vehicles to be utilized for additional hours. A building is maintained through power management options to deal with these interruptions. The choices include peer-to-peer building operation, electric vehicle charging and discharging, partial charge capability, load reduction, and load adjustment. The proposed technique makes use of building components and does not require any additional components for building energy management. The main contribution of this paper is to diminish energy costs and increase energy resilience to natural disasters. Resiliency is described as the ability to restore harmful loads while experiencing minimum power loss after successive power outages. The performance of the proposed system is evaluated using the MATLAB platform and compared to existing systems.

Smart Markets for Real-Time Allocation of Multiproduct Resources: The Case of Shared Electric Vehicles

Motivated by the deep transformation of the transportation sector toward electric and shared mobility, this research develops a decision support system that enables real-time allocation of shared electric vehicles (SEVs) to either the rental market or aggregated as a virtual power plant to the electricity market. The multipurposing of SEVs beyond the traditional scope of mobility services can boost utilization and profitability for fleet operators while also facilitating the integration of variable rate renewable energy sources. This SEV setting embodies the problem of real-time multiproduct resource allocation via smart markets. We develop an approach that leverages highly digitalized real-time markets and intelligent decision making to enable dynamic multiproduct resource allocation. The tool continuously evaluates market attractiveness and strategically places iterative bids on the resource owner’s behalf until all resources have been committed for a specific period. Our key contribution is to provide a blueprint for similar multi-product resource allocation settings, and demonstrating its efficacy in the domain of SEV fleets by developing tailored analytics and machine learning-based solutions.

Exploring the potential of shared electric vehicles from e‐mobility hubs as an alternative for commute and food shopping trips

AbstractElectric shared mobility hubs, called eHUBs, offer users access to a range of shared electric vehicles, including e‐bikes, e‐cargobikes, and e‐cars. Through the diversity of modes offered, eHUBs provide mobility solutions for different target groups and trip purposes. In this study, potential users’ willingness to use shared electric vehicles from eHUBs as either a commute or food shopping trip alternative was analysed using logistic regression methods. Results indicated that half of respondents were willing to use shared electric vehicles for at least a few of their regular commute or food shopping trips, although this proportion dropped substantially if considering the use of shared vehicles in combination with public transport. Across modes and trip purposes, holding a pro‐shared mobility attitude and belonging to the youngest age group strongly increased the willingness to use shared modes. Yet, while eHUBS may offer a potential alternative for at least some of people's regular commute or food shopping trips, cross‐mode shifts may be limited. That is, car drivers show a greater interest in shared e‐cars, whereas cyclists show a greater interest in e‐bikes and e‐cargobikes with public transport. Further influential factors, as well as implications for both shared mobility providers and local authorities, are discussed.

Spatiotemporal clustering analysis of shared electric vehicles based on trajectory data for sustainable urban governance

Shared electric vehicles (SEVs) have emerged as a promising mode of transportation with the growth of EVs and the sharing economy. However, current challenges, such as suboptimal station planning and inflexible operation schedules, are limiting the user experience and the growth of the industry. Therefore, our study adopts the affinity propagation (AP) clustering algorithm to divide the stations into two categories: the residential station and the business office station. We then apply the Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm to analyze the spatiotemporal distribution characteristics of SEVs from three aspects: hotspot area, travel volume and travel speed, based on trajectory data from the two types of stations. Results suggest that the distribution hotspot and travel volume of SEV at residential and business office stations is similar during the morning peak, concentrated in office, residential and transportation hub areas, whereas it is obviously different during the evening peak. Unlike the commuting demand of users in residential stations, users in business office stations prefer to use SEVs to travel to leisure and entertainment areas. Furthermore, we found that the driving speed of SEVs is primarily determined by actual road conditions and not significantly affected by station type. These findings provide valuable insights for SEV operators to optimize station layouts and spatial planning of vehicles during peak periods.

Design and Optimization of Solar-Powered Shared Electric Autonomous Vehicle System for Smart Cities

Design and Optimization of Solar-Powered Shared Electric Autonomous Vehicle System for Smart Cities

Sharing Is Caring: An Economic Analysis of Consumer Engagement in an Electric Vehicle Sharing Service

A growing population and increasing consumer demand have created unprecedented pressures on the environment and natural resources. The private vehicles market, one of the largest markets in the world, is associated with considerable environmental costs. Sharing electric vehicles, where consumers can enjoy the benefits of a greener vehicle without owning it, has emerged as an innovation that can reduce some of the environmental costs of ownership. However, uncertainty around the determinants of participation remain. This study employs an econometric model using survey and experimental data that were collected at the initial stages of the roll-out of an electric-vehicle-sharing service in Malta, in order to identify the psychological factors that determine the willingness to use and to pay for such a service, the propensity to walk to a car-sharing station, as well as the likelihood of scrapping a privately owned vehicle. The findings suggest that engagement in the car-sharing market is more likely to take place among those who have a lower psychological attachment to the private car, are already using multiple transport methods and are sharing a car with other household members. A large number of cars per household and a high use are negatively associated with uptake. The results also suggest that consumers who care about the environment are more likely to engage in car sharing.

Travel Intention with Shared Electric Vehicles Based on Theory of Multiple Motivations for Urban Governance

Travel Intention with Shared Electric Vehicles Based on Theory of Multiple Motivations for Urban Governance