Electric Car-sharing Systems Research Articles

Routing and charging scheduling for the electric carsharing system with mobile charging vehicles

Electric carsharing systems are expected to be an optional alternative to private vehicles for decreasing the urban traffic congestion and emissions. However, the temporal and spatial imbalance of the charging demand of shared electric vehicles adds to the managerial complexity of electric carsharing systems. This paper integrates mobile charging vehicles into the electric carsharing system to address this imbalance. Mobile charging vehicles can dwell at stations to provide elastic charging capacity, and thereby decrease both the waiting time of shared electric vehicles at busy stations and the investments in fixed charging piles at suburban stations. In this paper, a mixed integer linear programming formulation is proposed based on a time-space network, in which the routes of shared electric vehicles, charging schedules of shared electric vehicles, and routes of mobile charging vehicles are optimized simultaneously. Then, an algorithm based on Lagrangian relaxation is proposed. Specifically, the proposed formulation is decomposed into three independent subproblems. We propose three exact algorithms for these subproblems, and a tailored multistep repair algorithm is designed to generate feasible solutions. A case study in Hefei, China demonstrates the performance of the proposed algorithm and the effects of the number of SEVs, the number of MCVs, the number of fixed charging piles, trip component, battery capacity, and revenue on the operation of the electric carsharing system.

Efficiency-oriented vehicle relocation of shared autonomous electric fleet in station-based car-sharing system

Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators. To address the problems above, a “demand forecast-station status judgement-vehicle relocation” multistage dynamic relocation algorithm based on the automatic formation cruising technology was proposed in this study. In stage one, a novel trip demand forecast model based on the long short-term memory network was established to predict users' car-pickup and car-return order volumes at each station. In stage two, a dynamic threshold interval was determined by combining the forecast results with the actual vehicle distribution among stations to evaluate the status of each station. Then vehicle-surplus, vehicle-insufficient, vehicle-normal stations, and the number of surplus or insufficient vehicles for each station were counted. In stage three, setting driving mileage and carbon emission as the optimization objectives, an integer linear programming mathematical model was constructed and the optimal vehicle relocation scheme was obtained by the commercial solver Gurobi. Setting 43 stations and 187 vehicles in Jiading District, Shanghai, China, as a case study, results showed that rapid vehicle rebalancing among stations with minimum carbon emissions could be realized within 15 min and the users’ car-pickup and car-return demands could be fully satisfied without any refusal.

On the approximability and energy-flow modeling of the electric vehicle sharing problem

The electric vehicle sharing problem (EVSP) arises from the planning and operation of one-way electric car-sharing systems. It aims to maximize the total rental time of a fleet of electric vehicles while ensuring that all the demands of the customer are fulfilled. In this paper, we first show that the EVSP is NP-hard to approximate to within a factor of n1−ϵ in polynomial time, for any ϵ>0, where n denotes the number of customers. In addition, we also show that the problem does not have a monotone structure, which can be detrimental to the development of heuristics employing constructive strategies. Moreover, we propose a novel approach for modeling the EVSP based on energy flows. Based on this new model, we propose a relax-and-fix strategy and an exact algorithm. Our computational results show that our formulation outperforms the previous best-performing formulation in the literature in the number of optimal solutions obtained, optimality gaps, and computational times. Previously, 32.7% of the instances remained unsolved (within a time limit of one hour), while our formulation obtained optimal solutions for all instances. To stress our approaches, two more challenging new sets of instances are generated, for which we can solve 49.5% of the instances, with an average optimality gap of 2.91% for those not solved optimally.

Optimization of vehicle charging and dynamic relocation in free-floating electric carsharing systems with advanced reservations

The electric free-floating carsharing (FFCS) system is an emerging type of shared mobility and is widely adopted in urban areas due to its eco-friendliness, effectiveness, and flexibility. However, as most trips in FFCS are uni-directional, the distributions of vehicles in FFCS are usually spatially uneven. The operators of FFCS must relocate cars during the operation period to maintain the availability of cars to the users and improve the profitability of the system for dealing with imbalanced vehicle distribution caused by uneven demand. To optimize the relocation operations for FFCS operators, this study formulates the problem as a mixed-integer linear programming (MILP) model, which simultaneously addresses dynamic relocation, battery charging of electric vehicles, and scheduling of relocation staff in an FFCS with advanced reservation. A novel, efficient algorithm using a new structure of relocation tuples (RTs) is proposed to solve the problem that facilitates the search for feasible solutions using an RT exchange-based local search algorithm. Acceleration techniques are introduced to strengthen the solution search in large-scale scenarios. Numerical examples are used to evaluate the performance of the solution algorithm and validate the benefit of the proposed methodology. This shows that the methodology could be used by operators for decision-making at different levels.

Joint relocation and pricing in electric car-sharing systems

In this paper we study the integrated planning problem of determining car-sharing prices between zones of the operating area and routing employees (operators) to relocate cars in preparation for future uncertain demand. We present a novel two-stage integer stochastic programming model for this problem together with a heuristic algorithm, based on Adaptive Large Neighborhood Search (ALNS), to obtain solutions to realistically sized instances. We test the ALNS heuristic on a set of instances generated based on data from a real car-sharing organization and show that it outperforms a commercial solver.

Shared travel demand forecasting and multi-phase vehicle relocation optimization for electric carsharing systems

ABSTRACT Electric shared mobility is flourishing in urban transportation. However, the problem of uneven vehicle distribution and untimely vehicle charging hampers user trip experience and system operation efficiency. To overcome these challenges, this study proposed a multi-phase vehicle relocation optimization approach for one-way station-based carsharing systems. In phase one, a micro-level shared travel demand forecasting model was developed to capture the number of orders in the short-term future. In phase two, stations were divided into different categories based on the results of user travel demand forecast. In phase three, the minimization of driving mileage and carbon emissions was taken as the optimization objective, and a solution method combining Gurobi solver and charging priority ranking was designed. Finally, the effectiveness and advantages of the proposed model and algorithm were comprehensively validated through a case study using real passenger orders and geographic data from the city of Shanghai, China.

Simulating one-way electric carsharing systems with a multi-agent model

Simulating one-way electric carsharing systems with a multi-agent model

Joint optimization of infrastructure deployment and fleet operations for an electric carsharing system by considering multi-type vehicles

Electric carsharing (ECS) is considered an effective option to alleviate resource and environmental issues in urban transportation. In this study, we develop a mixed-integer optimization model to jointly optimize infrastructure deployment (station construction and fleet configuration) and fleet operations (vehicle assignment and charging strategy) for the ECS to maximize profits. The model further considers users’ diversified demands and incorporates multi-type electric vehicles into the problem formulation. The user-based assignment and on-demand charging strategy are also integrated into the model to improve the operational efficiency. A specialized logic-based Benders decomposition algorithm is customized to effectively solve the model. A real-life case in Lanzhou, China, is presented to demonstrate the proposed methods. Results indicate that the model has better performance and can increase profits by 20% compared with conventional methods, as hybrid fleet deployment is more energy-saving and can save up to 10% of the energy costs compared with a homogeneous fleet.

One-way station-based electric carsharing service design considering route selection based on road congestion

ABSTRACT In the field of one-way station-based electric carsharing systems (OSECSs), determining the optimal service design is a major challenge faced by operators. To address this issue, This study proposes a simulation-optimisation framework that takes into account several factors, such as fleet size, station capacity, rental pricing, vehicle relocation, and staff rebalancing to determine the optimal service design. Specially, the simulation module of this framework is used to evaluates the performance of different carsharing service designs by taking into account the selection of optimal routes based on road congestion, nonlinear charging profiles, and elastic demand influenced by price and travel time. In the optimisation module of this framework, the study introduces a novel greedy genetic search algorithm to solve the optimisation problem. The proposed simulation-optimisation framework is tested on a carsharing network with 15 stations in Chengdu, China, and the numerical experiment shows its efficiency. Additionally, the study analyzes the impact of different degrees of demand and travel time variation, demand scenarios, and road congestion scenarios on the OSECS. By doing so, it provides a more comprehensive understanding of the system and the factors that affect its performance.

On the complexity and modeling of the electric vehicle sharing problem

We introduce the electric vehicle sharing problem (EVSP), a problem that arises from the planning and operation of electric car-sharing systems with one-way rental of vehicles. The problem aims at finding the maximum total daily rental time in which customers’ demands are assigned to the existing fleet. In addition, either all the customer’s demands are completely fulfilled or the customer does not use the system at all. We show that the EVSP is NP-hard, and we provide four mixed-integer linear programming formulations based on space–time network flow models, along with some theoretical results. We perform a comprehensive computational study of the behavior of the proposed formulations using two benchmark sets, one of which is based on real-world data from an electric car-sharing system located in Fortaleza, Brazil. The results show that our best formulation is effective in solving instances where each customer has only one demand. In general, we were able to optimally solve at least 55% of the instances within the time limit of one hour.

A rolling-horizon decision framework for integrating relocation and user flexibility in one-way electric carsharing systems

One-way electric carsharing system has become the most widely used form of carsharing service in practice. However, the demand uncertainty, car imbalance, parking shortage, and battery charging jointly impose great challenges upon the operation. Many carsharing operators, e.g., GoFun, offer incentives to flexible users for accepting alternative itineraries and also adopt a full reservation policy. In this paper, we propose a rolling-horizon decision framework that integrates the operator’s car and staff relocation decisions, user flexibility, and charging of electric cars in the real-time setting. We formulate the problem at each decision epoch as a time–space network flow model that simultaneously optimizes the decisions. We develop an iterated local search heuristic algorithm to solve the model for large-scale practical problems. Based on theoretical insights and clustering of stations, we develop new local search procedures customized for the dynamic carsharing problem. We use real-world data from GoFun as an example to conduct extensive computational experiments. The results show that our algorithm significantly outperforms a particle swarm optimization algorithm and the popular greedy policy, and also has a low efficiency loss compared to the off-line heuristic solution. The transportation network size, clustering size, and operational efficiency are revealed to have significant impacts on the profit and service level.

A mathematical programming model for optimal fleet management of electric car-sharing systems with Vehicle-to-Grid operations

Electric car-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits in urban areas. Among different types, the one considered in this paper is the so-called one-way car-sharing system whereby a user can begin and end a trip at any station of the system. At the same time, the Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for smart power grid control. A management system that combines car-sharing system operations and V2G technology is a recent challenge for academia and industry. In this work, a mixed integer linear programming formulation is proposed to find the optimal management of electric vehicles in a one-way car-sharing system integrated with V2G technology. The proposed mathematical model allows finding the optimal start-of-day electric vehicles distribution that maximizes the total revenue obtained from system users and V2G profits through daily electric vehicles charging/discharging schedules. These schedules are based on mean daily users' electric vehicles requests and electricity prices. The model can be applied to evaluate the possible average daily profitability of V2G operations. In order to test the model performance, we applied it to a small-size test network and a real-size test network (the Delft network in the Netherlands). Under the model assumptions, the adoption of V2G technology allows to fully cover the daily charging costs due to users’ trips and to obtain V2G profits by taking advantage of electric vehicles unused time without significantly reducing the satisfied car-sharing system demand. Most of the energy purchased to charge the electric vehicles batteries is provided back to the grid during energy peak load demand, creating benefits also for energy providers.

A Mixed-Integer Program (MIP) for One-Way Multiple-Type Shared Electric Vehicles Allocation With Uncertain Demand

This paper proposes a mixed-integer program (MIP) to address a challenge issue of vehicle upgrade policy in current electric car rental market, i.e., idle luxury and high-end vehicles can be used as ordinary vehicles when the demand for ordinary vehicles is high. This model essentially is to maximize the total profit through balancing the demand and supply with a comprehensive consideration of revenue of rental operation, cost of potential demand loss, dispatching cost, energy consumption per kilometer, mileage limitation of electric vehicles (EVs), and the uncertainty of user demand for multi-type EVs. The proposed MIP model can be solved by CPLEX or Gurobi to search for the global optimal solution. Finally, real data from an electric carsharing system (ECS) with three types of EVs and 20 carsharing stations was used to validate the model. As a result, the daily increase of profit could reach 11.09% with an average of 8.08%.

Dynamic pricing, vehicle relocation and staff rebalancing for station-based one-way electric carsharing systems considering nonlinear charging profile

ABSTRACT This study focuses on the imbalance of vehicles in one-way station-based electric carsharing systems (OSECSs). A simulation-based optimization framework is proposed to determine optimal OSECS operation strategies, including dynamic pricing, vehicle relocation, and staff rebalancing. In this framework, the simulation module considers the nonlinear charging profiles for electric vehicles (EVs), EV-dependent road congestion, and price-dependent elastic demands to evaluate the performance of operation strategies. The optimization module triggered by the simulation model provides the optimized configuration of operation strategies based on the genetic algorithm and simultaneous perturbation stochastic approximationTo eliminate the decision variable combinatorial explosion for the optimization problem, we apply the K-means clustering algorithm to cluster stations into small groups of cluster zones, reducing the dimensions of decision variables. A case study in Chengdu demonstrates the proposed simulation-optimization framework’s efficiency and further analyzes the impact of different operation strategies, nonlinear charging mode, and EV-dependent road congestion for the OSECSs.

An optimization Model Combining Operator-Based Relocation With User-Based Relocation for Electric Carsharing Systems

This paper proposes an optimization model combining operator-based relocation with user-based relocation for electric carsharing systems, aiming to improve the low usage of vehicles and the imbalance between supply and demand. First, the back-propagation neural network prediction model of vehicle quantity demand and the distribution fitting model of energy demand are constructed. In the operator-based scheme, optimization models of inter-regional and intra-regional relocation are constructed. In the user-based scheme, a multi-objective optimization model considering the operator profit and user experience is proposed. Taking the actual operation data of an electric carsharing company in Shanghai as an example, the mixed scheduling strategy of operator-based relocation and user-based relocation is validated. Results show that: (i) users are very sensitive to price changes, and station distance and price incentive will change users’ pickup and return behavior; (ii) compared with systems without vehicle relocation, the mixed scheduling strategy can reduce the using failure rate by 6.68% and increase net profit by 42.6% in the best situation; and (iii) the using-failure rate alert line, as a switching mechanism between user-based and operator-based relocation, can play a regulatory role, and with the loosening of the alert line, the frequency of operator-based relocation intervention increases, and the using-failure rate decreases further.

Heterogeneous fleet management for one-way electric carsharing system with optional orders, vehicle relocation and on-demand recharging

As a part of the trend of shared transport, carsharing has received increasing attention over the recent years. We address the operation management problem of a heterogeneous electric vehicle fleet in a one-way carsharing system with relocation and on-demand recharging activities, to fulfill the optional rental orders that are known in advance. We present a mixed-integer linear programming formulation for the problem with the objective to maximize the overall profit, which can be solved to the optimum for instances with up to 50 rental orders by CPLEX within an hour. To deal with large-scale problems, we apply the Dantzig–Wolfe decomposition technique and propose an approach which hybridizes the ant colony optimization (ACO) metaheuristics into the column generation framework to quickly solve the pricing subproblems. The evaluation experiments using randomly generated instances of different scales have shown that, the proposed hybrid approach is both efficient and efficacious: (1) the gap between the solutions by the hybrid approach and the optimal solutions is below 5% in average for small instances; (2) for large instances with over 100 rental orders, the overall performance of the hybrid approach is significantly better than those of CPLEX and ACO metaheuristics, with rather short time consumption. Furthermore, a case study is conducted based on practical transportation data of Beijing, using the net profit and order fulfillment rate as performance criteria. Insightful findings have been revealed by sensitivity analysis.

Electric car sharing stations site selection from the perspective of sustainability: A GIS-based multi-criteria decision making approach

It is undoubtedly that the environmental and economic benefits will increase by integrating electric vehicles, which are the vehicles of the future, with the car-sharing system. The problem of determining the locations of electric vehicle charging stations (EVCS) and service areas of the car-sharing system appear with the integration. Environmental and energy concerns are the biggest driving factor behind electric car-sharing systems. In this study, Geographic Information Systems (GIS) based fuzzy multi-criteria decision making (MCDM) method is proposed for the solution of site selection problems of electric car-sharing stations (ECSS). To do so, a three-step solution methodology is developed: (i) determining 20 sub-criteria and weighting the criteria with fuzzy Analytic Hierarchy Process (f-AHP), (ii) obtaining a suitability map for potential ECSS via GIS, (iii) sorting the performance levels of ECSSs assigned according to the suitability map by Elimination and Choice Translating Reality (ELECTRE). The proposed methodology is applied for Istanbul, a metropolitan city in Turkey as a case study. The results show that the most suitable areas for ECSSs in the European and Anatolian side, Istanbul is an intercontinental city, are the south-east part and south-western part, respectively.

Dynamic Inventory Relocation for a One-way Electric Car-Sharing System with Uncertain Demands

Dynamic Inventory Relocation for a One-way Electric Car-Sharing System with Uncertain Demands

Real-time Vehicle Relocation and Charging Optimization for One-way Electric Carsharing Systems

Real-time Vehicle Relocation and Charging Optimization for One-way Electric Carsharing Systems

Routing and Charging Scheduling for the Electric Carsharing System with Mobile Charging Vehicles

Routing and Charging Scheduling for the Electric Carsharing System with Mobile Charging Vehicles