An integrated optimization-simulation framework for vehicle and personnel relocations of electric carsharing systems with reservations

One-way electric vehicle carsharing systems are receiving increasing attention due to their mobility, environmental, and societal benefits. One of the major issues faced by the operators of these systems is the optimization of the relocation operations of personnel and vehicles. These relocation operations are essential in order to ensure that vehicles are available for use at the right place at the right time. Vehicle availability is a key indicator expressing the level of service offered to customers. However, the relocation operations, that ensure this availability, constitute a major cost component for the provision of these services. Therefore, clearly there is a trade-off between the cost of vehicle and personnel relocation and the level of service offered. In this paper we are developing, solving, and applying, in a real world context, an integrated multi-objective mixed integer linear programming (MMILP) optimization and discrete event simulation framework to optimize operational decisions for vehicle and personnel relocation in a carsharing system with reservations. We are using a clustering procedure to cope with the dimensionality of the operational problem without compromising on the quality of the obtained results. The optimization framework involves three mathematical models: (i) station clustering, (ii) operations optimization and (iii) personnel flow. The output of the optimization is used by the simulation in order to test the feasibility of the optimization outcome in terms of vehicle recharging requirements. The optimization model is solved iteratively considering the new constraints restricting the vehicles that require further charging to stay in the station until the results of the simulation are feasible in terms of electric vehicles' battery charging levels. The application of the proposed framework using data from a real world system operating in Nice, France sheds light to trade-offs existing between the level of service offered, resource utilization, and certainty of fulfilling a trip reservation. (C) 2016 Elsevier Ltd. All rights reserved.

Toward Ultrafast Charging of Electric Vehicles

Massimiliano Capezzali, Stefano Grioni, Hardi Hõimoja, Hans Björn Püttgen, Alfred Rufer, Michail Vasiladiotis

The paper is devoted to the problems arising from the ultrafast (≤ 10 min) charging of an electric vehicle (EV). An ultrafast charging station (UFCS) must provide high power output with minimal influence on the electricity transmission system, which can only be achieved by the application of energy storage acting as an additional buffer between the vehicle and the grid. Besides storage, interfaces between a fast charging station and the outside environment (vehicle, utility grid) must be designed to fulfil a set of requirements. The main challenge is to be found within the specification of parameters for the design of future energy supply systems, providing for fast charging of the vehicle batteries while avoiding solicitations of the local distribution system which exceed its instantaneous power capabilities. The possible impact of an UFCS on the power distribution system is analysed with the stochastic approach, based on the utilisation of such a station. The general aspects of highly variable load profile clearly include the use of energy storage means that must be specified regarding both the energy storage and the instant power capabilities. Different technologies are analysed in terms of performances and costs. In conclusion, one of the important problems facing electric vehicles is the possibility of short-time charging, both as seen from the EV battery itself as well as from the local supply system. In this context, large load variations as seen from the local power system, at multiple levels, must be carefully assessed with special attention to feasible load changes at the coupling points. By addressing these technical issues as well as the financial constraints, the research aims at providing viable solution for broad ultrafast charging systems integration into the power distribution infrastructure.

2012

Optimizing Locations for a Vehicle Sharing System

Michel Bierlaire, Prem Kumar

Car-sharing business provides members access to a fleet of shared-use vehicles in a network of locations on a short-term, as-needed basis. It allows individuals to gain the benefits of private vehicle use without the costs and responsibilities of ownership. The primary advantage of car sharing as opposed to the car rental business is that it provides flexibility of using the vehicles for shorter periods of time. Secondly if you are a registered customer of the company, the process of accessing and using the vehicle is quite simple, self-serving and hassle-free. One of the main problems faced by the car-sharing businesses is that of finding the best stations to place the facility. These locations should be chosen by attractiveness of various parameters such as the socio-demographic-economic profile of the population that resides or works at the location and accessibility of other forms of public transport in that location. However the presence of one or more stations in the vicinity and the type of vehicles placed in a station are also critical for decision-making. It must be noted that the attractiveness factor of a parameter would be shared between different stations if they are present in the sphere of influence of that parameter. This study analyzes the performance of an electric car sharing service across different stations in and around the city of Nice. This service is operated by a major public transport operator in France and commenced its operations in April 2011. The main measure of performance of the stations is the average number of rides (usages) per day. The objective of this study is two-fold one, to analyze the performance of the car-sharing service across all stations and estimate the key drivers of demand, and secondly, to use these drivers to identify future station locations, such that the overall system performance is maximized. The methodology used by us to optimize the station locations follows two steps. In the first step, we perform an extensive data analysis and determine all those factors that we expect to be driving the demand for the service and build a linear regression model with station performance as the dependent variable that is explained by a host of other independent variables, such as public transport ridership, share of car-users in the locality, share of high income / education groups in the locality population, population density and presence of other mobility generators such as hotels, colleges and commercial centers. In the second and final step, we use the attractiveness of the different localities to optimally locate the new stations for the service. The main trade-off decision made by the model involves locating more stations in highly attractive localities versus locating new stations in less attractive but untapped localities. We validated and reported our results on a set of new stations that were opened quite recently, after the commencement of our study.

2012

Location Optimization for a Vehicle Sharing Service

Michel Bierlaire, Prem Kumar

2012

Toward Ultrafast Charging of Electric Vehicles

Massimiliano Capezzali, Stefano Grioni, Hardi Hõimoja, Hans Björn Püttgen, Alfred Rufer, Michail Vasiladiotis

2012

Optimizing Locations for a Vehicle Sharing System

Michel Bierlaire, Prem Kumar

2012

Location Optimization for a Vehicle Sharing Service

Michel Bierlaire, Prem Kumar

2012