A Game-theory Analysis of Charging Stations Selection by EV Drivers

We address the problem of Electric Vehicle (EV) drivers' assistance through Intelligent Transportation System (ITS). Drivers of EVs that are low in battery may ask a navigation service for advice on which charging station to use and which route to take. A rational driver will follow the received advice, provided there is no better choice i.e., in game-theory terms, if such advice corresponds to a Nash-equilibrium strategy. Thus, we model the problem as a game: first we propose a congestion game, then a game with congestion-averse utilities, both admitting at least one pure-strategy Nash equilibrium. The former represents a practical scenario with a high level of realism, although at a high computational price. The latter neglects some features of the real-world scenario but it exhibits very low complexity, and is shown to provide results that, on average, differ by 16% from those obtained with the former approach. Furthermore, when drivers value the trip time most, the average per-EV performance yielded by the Nash equilibria and the one attained by solving a centralized optimization problem that minimizes the EV trip time differ by 15% at most. This is an important result, as minimizing this quantity implies reduced road traffic congestion and energy consumption, as well as higher user satisfaction

Infrastructure Topology Optimization under Competition through Cross-Entropy

International audienceIn this article, we study a two-level non-cooperative game between providers acting on the same geographic area. Each provider has the opportunity to set up a network of stations so as to capture as many consumers as possible. Its deployment being costly, the provider has to optimize both the number of settled stations as well as their locations. In the first level each provider optimizes independently his infrastructure topology while in the second level they price dynamically the access to their network of stations. The consumers' choices depend on the perception (in term of price, congestion and distances to the nearest stations) that they have of the service proposed by each provider. Each provider market share is then obtained as the solution of a fixed point equation since the congestion level is supposed to depend on the market share of the provider which increases with the number of consumers choosing the same provider. We prove that the two-stage game admits a unique equilibrium in price at any time instant. An algorithm based on the cross-entropy method is proposed to optimize the providers' infrastructure topology and it is tested on numerical examples providing economic interpretations

A Holistic View of ITS-Enhanced Charging Markets

We consider a network of electric vehicles (EVs) and its components: vehicles, charging stations, and coalitions of stations. For such a setting, we propose a model in which individual stations, coalitions of stations, and vehicles interact in a market revolving around the energy for battery recharge. We start by separately studying 1) how autonomously operated charging stations form coalitions; 2) the price policy enacted by such coalitions; and 3) how vehicles select the charging station to use, working toward a time/price tradeoff. Our main goal is to investigate how equilibrium in such a market can be reached. We also address the issue of computational complexity, showing that, through our model, equilibria can be found in polynomial time. We evaluate our model in a realistic scenario, focusing on its ability to capture the advantages of the availability of an intelligent transportation system supporting the EV drivers. The model also mimics the anticompetitive behavior that charging stations are likely to follow, and it highlights the effect of possible countermeasures to such a behavior

Vehicular Networks with Infrastructure: Modeling, Simulation and Testbed

This thesis focuses on Vehicular Networks with Infrastructure. In the examined scenarios, vehicular nodes (e.g., cars, buses) can communicate with infrastructure roadside units (RSUs) providing continuous or intermittent coverage of an urban road topology. Different aspects related to the design of new applications for Vehicular Networks are investigated through modeling, simulation and testing on real field. In particular, the thesis: i) provides a feasible multi-hop routing solution for maintaining connectivity among RSUs, forming the wireless mesh infrastructure, and moving vehicles; ii) explains how to combine the UHF and the traditional 5-GHz bands to design and implement a new high-capacity high-efficiency Content Downloading using disjoint control and service channels; iii) studies new RSUs deployment strategies for Content Dissemination and Downloading in urban and suburban scenarios with different vehicles mobility models and traffic densities; iv) defines an optimization problem to minimize the average travel delay perceived by the drivers, spreading different traffic flows over the surface roads in a urban scenario; v) exploits the concept of Nash equilibrium in the game-theory approach to efficiently guide electric vehicles drivers' towards the charging stations. Moreover, the thesis emphasizes the importance of using realistic mobility models, as well as reasonable signal propagation models for vehicular networks. Simplistic assumptions drive to trivial mathematical analysis and shorter simulations, but they frequently produce misleading results. Thus, testing the proposed solutions in the real field and collecting measurements is a good way to double-check the correctness of our studie

Charging infrastructure planning and resource allocation for electric vehicles

With the increasing uptake of electric vehicles (EVs) and relative lag in the development of charging facilities, how to plan charging infrastructure and effectively use existing charging resources have become the top priority for governments, related industry and research communities. This study aims to address two key issues related to EV charging - charging station planning and charging resource allocation. The major contributions of the study are: (1) Introduced a model for charging infrastructure planning based on origin-destination data of EV traffic flows. I first showed how to use the gravity model to calculate point-to-point traffic flows from traffic data at each intersection and further induce the origin-to-destination flow data. Then, I introduced an optimization model for charging allocation based on origin-destination traffic flow data and extended it into a formal model for charging station planning by minimizing the total waiting time of EVs. (2) Applied the charging infrastructure planning model to Sydney Metropolitan charging station planning. I selected a set of representative areas from Sydney metropolitan and collected traffic data for these areas. I then used the gravity model to calculate the EV flow for each route based on possible portions of EVs among all traffic. The optimisation constraints under consideration include charging station locations, total budget and feasibility of charging allocations. Optimisation for chargers at each intersection for different scenarios is solved using the least squares method. (3) Designed an algorithm for charging facility allocation to balance the load of charging stations. By considering the maximum driving range, the number of chargers at charging stations, and waiting time and queue length at each charging station, a queue balancing algorithm is proposed. Numerical experiments were conducted to validate the algorithm based on a linear road scenario. I believe that the outcomes of this research have a great potential to be used for government/industry planning of charging stations and improvement of utilization of charging stations resources