Electric Vehicle Sharing Research Articles

Trip Pricing Scheme for Electric Vehicle Sharing Network With Demand Prediction

With promising benefits such as emission reduction, traffic congestion alleviation and parking space saving, electric vehicle sharing systems have attracted increasing attentions. This paper proposes a Trip Pricing Scheme (TPS) for a large-scale EV-sharing network with Shared Electric Vehicle (SEV) demand prediction. In the proposed system, the SEV traffic demand is firstly predicted through a model which includes a cascade graph convolutional neural network and a long-short term memory neural network. Based on this, the TPS is modelled as a mixed-integer nonlinear programming problem, which aims at maximizing the total system profit from EV sharing business. The proposed TPS determines the optimal combination of the two price adjustment levels, which provides incentives to the spatial-temporal distribution of SEVs’ traffic flows to maximize the system’s profit. Numerical simulations are conducted to validate the effectiveness of the proposed method.

A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing Systems

This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse demand in a network, FFEVSS requires relocation of electrical vehicles (EVs) to charging stations and demander nodes, which is typically done by a group of drivers. A shuttle is used to pick up and drop off drivers throughout the network. The objective of this study is to solve the shuttle routing problem to finish the rebalancing work in minimal time. We consider a reinforcement learning framework for the problem, in which a central controller determines the routing policies of a fleet of multiple shuttles. We deploy a policy gradient method for training recurrent neural networks and compare the obtained policy results with heuristic solutions. Our numerical studies show that unlike the existing solutions in the literature, the proposed methods allow solving the general version of the problem with no restrictions on the urban EV network structure and charging requirements of EVs. Moreover, the learned policies offer a wide range of flexibility, resulting in a significant reduction in the time needed to rebalance the network.

Optimal pricing and fleet management for shared electric vehicle in coupled power and transport networks

This paper considers a group of shared electric vehicles (SEV) that provide transport and energy services through a ride-sourcing platform. Under the novel business model, the transport and power networks are coupled and the resulting market equilibrium depends on the pricing strategies of the distribution network operator (DNO) and the SEV platform operator. To capture the interaction between the DNO and the SEV platform operator, we develop two market competition models, including (a) a Stackelberg competition model where the DNO makes the decision first and the SEV operator reacts accordingly, and (b) a Nash competition model where the DNO and the SEV operator make decisions simultaneously. The strategic interactions of the DNO and the SEV platform operator are analyzed over a coupled transport and power network, which captures the routing and discharging behaviors of EV drivers, the matching process between passengers and drivers, and the corresponding traveler and power flows. We analytically prove that the supply and demand equilibrium between travelers and EV drivers over the transport network always exists. We also numerically validate that the equilibrium solution between the SEV operator and the DNO is stable and unique. By varying the transport demands, we show that the SEV platform operator, the DNO and EV drivers always benefit from the discharge business even though passengers may be slightly worse off. Surprisingly, we find that integrating the discharging service will have diverse impacts on the cruising traffic depending on the demand levels of the transport service: under low demand level, it will reduce the cruising traffic; under medium demand level, it will increase the cruising traffic; under high demand level, it has no impact on the cruising traffic.

Examining tourists’ intention to use electric vehicle-sharing services

Tourists’ mobility is closely linked with the concept of sustainable tourism, since it affects issues that are related with the local economies and the environment. The seasonality of the transport demand in touristic areas results in traffic congestion phenomena and extensive air pollution. At the same time, the touristic areas have to serve the special mobility needs of tourists, in order to be competitive and increase their market. For reducing the negative impacts of demand seasonality and serving the tourists efficiently, innovative mobility concepts and services can contribute. In this framework, a questionnaire study was conducted in the island of Rhodes, Greece aiming to investigate the willingness of tourists to use shared electric vehicles for intra-destination trips. The responses were analyzed descriptively and through statistical modelling techniques. The results of the analysis show that electric car-sharing can expect a greater demand comparing to electric moto-sharing and electric bike-sharing. Also, the results indicate that electric car-sharing will mainly attract a market share from the traditional car rental schemes. Finally, it seems that the intention of tourists to use shared electric vehicles is highly affected by their mobility-related habits and experiences in their hometown.

The parallel mobile charging service for free-floating shared electric vehicle clusters

Due to the simple rental procedures and unrestricted parking spots, free-floating shared electric vehicles (SEVs) are widely selected by travelers. A large number of SEVs gather together and even form SEV clusters at popular destinations. In most studies, the low-charge SEVs are usually relocated to charging stations to recharge their batteries. This paper proposes the parallel mobile charging service to route mobile charging vehicles (MCVs) to charge SEVs at their parking spots. The MCV routes are optimized through a mixed integer nonlinear optimization model where MCVs with limited battery capacity could recharge their batteries at available charging stations and sequentially or simultaneously charge the filtered SEVs at the same SEV cluster node. The parallel mobile charging service proposed without relocating SEVs fully takes advantage of SEV clusters (i.e., cluster-based overlapping idle time windows and small inter-vehicle distance) and MCVs (i.e., charging multiple SEVs simultaneously). An adaptive large neighborhood search algorithm is developed to solve large-scale instances where a generalized cost function is constructed to balance the effects of battery capacity violations and time window violations on solutions. Comparing with the optimization solver CPLEX, the adaptive large neighborhood search algorithm developed demonstrates high performance. In addition, the cluster-based parallel acceleration time ratio is introduced to demonstrate the advantages of parallel services in utilizing the cluster-based overlapping time windows.

Hybrid Research Platform for Fundamental and Empirical Modeling and Analysis of Energy Management of Shared Electric Vehicles

This article presents the results of the development of a hybrid research platform for fundamental and empirical modeling and analysis of energy management of shared electric vehicles. The article describes the hybrid model and its specific features in detail. Within the model architecture, a part of the fundamental model, empirical model and data collection tools were interconnected. The uniqueness lies in the models of electric cars created for a specific vehicle using cost-optimal parameterizations, as well as the implementation of a cloud solution, which is based on custom data communication, custom data logger and cost-optimized parameterization of machine learning algorithms. Experimental verification was performed on a real electric car in public traffic. The car is part of casharing platform.

The Impact of Social Conformity on Adopting Decision of Shared Electric Vehicles: A Choice Experiment Analysis in China.

Social conformity, a psychological phenomenon commonly shared by most individuals, has long been ignored by studies focusing on influencing preferences for shared electric vehicles (SEVs). To fill this gap, this paper divides social conformity into informational conformity and normative conformity, and analyzes their effects on individuals’ choice of SEVs. Respondents were selected randomly in Jiangsu Province, and the data were collected by the choice experiment method. The data were further analyzed by logit models. Results show that social conformity has a significant positive impact on individuals’ choice of SEVs, and informational conformity has a much more profound impact than normative conformity. The driving cost and the convenience of picking up and returning a vehicle also influence consumers’ preferences. In addition, social conformity cannot totally dispel the negative impact of poor experience. Finally, some targeted policy recommendations are proposed.

Grid congestion mitigation in the era of shared electric vehicles

Rapid integration of photovoltaic systems and electric vehicles in low-voltage grids increasingly causes grid congestion. To avoid costly grid reinforcements, distribution system operators are looking into applying electric vehicle smart charging algorithms to shift part of the load to off-peak hours. However, multiple barriers hamper the large-scale implementation of smart charging. These barriers can be alleviated when using electric vehicles in car sharing schemes for smart charging. Shared electric vehicles make up an increasing share of the car fleet and using these vehicles for smart charging exhibits different advantages over using private vehicles, including better predictable departure times and higher acceptance for smart charging. This study proposes a system for grid congestion mitigation using only shared electric vehicles and assesses this system’s techno-economic potential. Results affirm that grid congestion problems can be fully mitigated in most grids using shared electric vehicles at relatively low car sharing adoption rates. Also, the costs increase of using this system is negligible compared to a system with combined grid congestion mitigation of privately-owned and shared EVs.

How to Motivate Consumers’ Intention to Use Shared Electric Vehicles? From the Perspective of Institutional Theory

How to Motivate Consumers’ Intention to Use Shared Electric Vehicles? From the Perspective of Institutional Theory

Optimal Routing and Charging Strategy for Shared Electric Vehicle Service

Optimal Routing and Charging Strategy for Shared Electric Vehicle Service

Computational Methods for Scheduling the Charging and Assignment of an On-Site Shared Electric Vehicle Fleet

We investigate a fleet scheduling problem arising when a company has to manage its own fleet of electric vehicles. Aim is to assign given usage reservations to these vehicles and to devise a suitable charging plan for all vehicles while minimizing a cost function. We formulate the problem as a compact mixed integer linear program, which we strengthen in several ways. As this model is hard to solve in practice, we perform a Benders decomposition, which separates the problem into a master problem and a subproblem and solves them iteratively in an alternating manner. We perform the decomposition in two different ways. First we follow a more classical way, then we enrich the master problem making it stronger but also more complex and the subproblem smaller and simpler to solve. To improve the overall performance, we propose a problem-specific General Variable Neighborhood Search metaheuristic for solving the master problem in earlier iterations. Experimental results show that directly solving the complete mixed integer linear program usually performs well for small to some medium sized problem instances. For larger instances, however, it is not able to find any reasonable primal solutions anymore, while the Benders decomposition scales much better. Especially the variant with the heuristic delivers high quality solutions in reasonable time. The Benders decomposition with the more complex master problem also yields reasonable dual bounds and thus practically relevant quality guarantees for the larger instances.

A coordinated optimization of rewarded users and employees in relocating station-based shared electric vehicles

A coordinated optimization of rewarded users and employees in relocating station-based shared electric vehicles

Deploying battery swap stations for shared electric vehicles using trajectory data

• Proposing a novel Station-to-Point (S2P) Battery Swap Mode. • Extracting battery swap demand of Shared Electric Vehicles (SEVs) from trajectory data. • A data-driven approach to deploying and operating Battery Swap Stations (BSSs). • Setting up 53 scenarios to test the approach and the sensitivity of key parameters. • The desired quality of service was influential to the deployment and operation of BSSs. This paper proposed a novel Station-to-Point (S2P) Battery Swap Mode for Shared Electric Vehicles (SEVs), under which Battery Swap Stations (BSSs) have dedicated delivery vehicles transporting new/used batteries between BSSs and Battery Swapping Demand (BSD) points. We further developed a data-driven BSS location optimization model and day-to-day operation strategy, using a one-month GPS trajectory dataset containing 514 actual SEVs in Beijing. We set up 53 scenarios to test the model. In the baseline scenario, we found that the SEV fleet needed 15 BSSs, and each SEV, on average, needed 1.202 batteries and 0.031 delivery vehicles with the centralized management strategy applied. Through “what-if” scenarios, we found that the key parameters Q (the coverage rate of BSD points), R (the service radius of a BSS), and AADT (the acceptable average delay time) were influential to the outputs of interest.

Competition and coordination strategies of shared electric vehicles and public transportation considering customer travel utility.

With the rise of the sharing economy and the concept of "green environmental protection and low-carbon travel," the emerging project of shared electric vehicles is booming. However, the accompanying coordination problem between shared electric vehicles and public transportation system needs to be urgently solved. In reality, customers' choice of travel mode is influenced by their own travel perceived utility. Thus, this paper will discuss the competition and coordination problem between shared electric vehicles and public transportation system from the perspective of customer travel utility. Considering the travel cost and comfort in the customer travel utility, the game models of shared electric vehicle and public transportation system in different scenarios are established by using competitive game and cooperative game. Then, the equilibrium solutions under different scenarios are obtained by solving the models. The analysis results show that shared electric vehicles would bring some beneficial improvements to the transportation system under certain circumstances. Furthermore, public transportation system should adopt a coordinative strategy with the shared electric vehicles to promote the total customer travel utility for the entire system. It is worth considering the improvement of the service quality of shared electric vehicle and public transportation, which would affect the rate of increasing in the total customer travel utility.

BROMINATED ISOBUTYLENE-CO-PARAMETHYLSTYRENE WITH SUPERIOR IMPERMEABILITY FOR TIRE INNERLINER APPLICATIONS

ABSTRACT Isobutylene elastomers are of great commercial importance in tire applications because of their notable low gas permeability properties, owing to their efficient molecular packing. Brominated isobutylene-co-para-methylstyrene (BIMSM) elastomers are a special class of isobutylene elastomers synthesized by random cationic polymerization of isobutylene and para-methylstyrene (pMS), followed by a selective bromination of the methyl group of the pMS units. BIMSM elastomer, commercially known as Exxpro™ specialty elastomer, exhibits superior heat resistance and aging properties and is much more resistant to chemicals and ozone than butyl- or halobutyl polymers because of its fully saturated backbone structure. The permeability properties of BIMSM elastomers can be tuned by the level of pMS comonomer present in the polymer chain. The pMS comonomer increases the glass transition temperature of the copolymer, and polymers with very low gas permeability needed for demanding tire air retention applications can be produced by suitably selecting the pMS content. A single type of benzylic bromide, but with a versatile functional group, allows for precise control of vulcanization chemistry, potential for other chemical transformations to achieve other reactive groups, and grafting reactions. We present the new material developments to meet the growing market demands and requirements for low maintenance, low inflation pressure loss rate tires and tires for connected autonomous shared electric vehicles.

A Two-Stage Charging Facilities Planning Method for Electric Vehicle Sharing Systems

The large-scale sharing electric vehicle (SEV) need adequate charging infrastructure to maintain the optimal operation. Considering the convenience of SEV users' charging during driving, a two-stage planning method combining charging stations and scattered charging piles for SEV is proposed to balance the economy of SEV operators and the convenience of SEVs users. In the first stage, according to the urban area division and trip characteristics of SEV, the charging power demand of SEV in each SEV placement point can be estimated exactly. Then, aiming for the minimum annual total cost, the mean clustering algorithm and queuing theory are used to determine the location and capacity of charging stations. In the second stage, an improved S-shaped function is used to assess the unsatisfied charging demand points of SEV users, and the compensated charging piles without in the stations are planned based on the mean shift algorithm. Based on the re-division of the service scope, the capacity of the charging stations is updated. Finally, the simulation results verifying the proposed model and algorithm is effective and feasible.

How Does Travel Satisfaction Affect Preference for Shared Electric Vehicles? An Empirical Study Using Large - Scale Monitoring Data and Online Text Mining

How Does Travel Satisfaction Affect Preference for Shared Electric Vehicles? An Empirical Study Using Large - Scale Monitoring Data and Online Text Mining

A Structural Property of Charging Scheduling Policy for Shared Electric Vehicles With Wind Power Generation

In this work, we focus on the optimization of charging scheduling policy for shared electric vehicles (EVs) integrated with wind power generation. This problem is of significant importance nowadays because of the large adoption of EVs in modern societies and the increasing penetration of renewables. A particular challenge of the problem is the large action space, the size of which may increase exponentially with respect to the number of EVs in the system. This makes the problem difficult to solve in practice. A lot of efforts have been made to overcome the above difficulty. The previous study proposed least-laxity-longer-processing-time-first (LLLP) principle, a rule-based algorithm to schedule EVs’ battery charging. The LLLP principle assigns higher priority to vehicles with less laxity and longer processing time. We extend the LLLP principle and further study the structural property of the charging scheduling problem. The main contributions in this work are as follows. First, we show that the LLLP applies to our problem and may be used to narrow down the action space while preserving the global optimality. Second, we provide a modified LLLP algorithm that may construct a policy in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(NT)$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the number of the EVs and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is the number of time steps in the scheduling problem. Third, we use numerical experiments to show that the new algorithm performs better than other existing algorithms, including the least-laxity-shorter-processing-time-first (LLSP) principle, the earliest-deadline-first (EDF) principle, and the latest-deadline-first (LDF) principle. The new algorithm finds near-optimal policies (within 1% performance loss) and is at least 40 times faster than CPLEX. We hope that this work provides insight into the charging scheduling of shared EVs in general.

Integrating Price-Incentive and Trip-Selection Policies to Rebalance Shared Electric Vehicles

Because electric vehicle sharing (EVS) offers the advantages of high flexibility and convenience, it has been receiving increasing attention worldwide as an effective approach to easing traffic congestion and environmental pollution. However, unbalanced electric vehicle distribution is an obstacle in the development of EVS. In this paper, we propose an integrated strategy to mitigate the imbalance issue and enhance customers’ adoption of EVS. We construct an integrated strategy that combines the price-incentive approach with the trip-selection policy and models uncertain travel demand in a continuous trip-adopting process based on our integrated strategy. Aiming to improve EVS operating profits, we apply spatiotemporal nonlinear mixed-integer programming to formulate the travel pricing and rebalancing plan. Additionally, we approximate the model in a tractable form after analyzing the optimal service adoption and develop an efficient exact algorithm to handle the nonlinear items. The computational results of a real-world car2go Amsterdam case study demonstrate several economic and environmental benefits generated by our integrated policy, including (i) higher profits for EVS operators, (ii) improved service satisfaction for consumers, and (iii) a higher level of carbon emissions reduction, from 381 grams per mile to 225 grams per mile, beneficial for the social environment. Moreover, according to the case study, an appropriate initial fleet size, high rebalancing frequency, low labor cost, high potential travel demands, and short charging time also benefit EVS operation.

Service Operations of Electric Vehicle Sharing Systems from the Perspectives of Supply and Demand: A Literature Review

Electric vehicle (EV) sharing not only changes how people travel but also provides economic and environmental benefits. It also creates a series of new issues affecting service operations considering the characteristics of EVs. From the EV-sharing supply side, the issues are unreasonable station design, inconvenient charging, and high vehicle relocation costs, while from the demand side, they include insufficient understanding of the factors affecting EV sharing and inaccurate demand forecasts. These issues have hindered the rapid spread of EV sharing, and if development of this industry is to accelerate, they must be resolved urgently. To this end, the most recent literature on EV-sharing systems based on different methodologies are reviewed from the perspectives of supply and demand. According to research theme, first the literature on the supply side of EV-sharing systems is classified into strategic planning (station design—location, number and charging capacity—and fleet size) and daily operations (vehicle relocation and charging management) and integrated modelling for strategic planning and daily operations. Next, the literature on the demand side of EV-sharing systems is categorized into the analysis of factors affecting EV-sharing demand and the methods for forecasting EV-sharing demand. Optimization models and algorithms are surveyed in each theme. Finally, this study not only considers future research directions for EV-sharing systems from the perspectives of supply and demand but also highlights future research opportunities for EV-sharing systems given autonomous driving technology.