Decentralized control of electric vehicles in a network of fast charging stations

Abstract

To facilitate the adoption of electric vehicles (EVs) and their plug-in hybrid (PHEVs) counterparts and to avoid straining the capacity of the power grid there is a strong need for developing a network of fast charging facilities and coordinate their service. Incorporation of EVs in the vehicle fleet would decrease green house gas emissions and overall dependency on fossil fuels.A key issue in charging EVs is that the corresponding time is fairly large, which can lead to very long delays. Hence, for the network of charging stations to provide good quality of service to customers, we first propose an admission control mechanism based on pricing for a single charging station. Subsequently, we develop a decentralized routing scheme of EV drivers, employing a game theoretic model. The latter entices drivers through price incentives to require charging from less busy stations, thus leading to a more efficient utilization of power across the network, while it enhances profit for the charging facilities operator. Of note, the proposed scheme does not require advanced monitoring tools for power usage and pricing calculations. The drivers receive and send back the necessary information through the a communications infrastructure and the routing is initiated only when the network has exceeded a critical threshold. The numerical results illustrate the discussed benefits of the proposed scheme.

Reducing Range Anxiety by Unifying Networks of Charging Stations

“…In a network of charging station, the satisfaction level of the EVOs relies on the appropriate location of charging station, availability of the service when it is required, cost, and timing of getting the service [56], [57].…”

Electricity costs for a Level 3 electric vehicle fueling station integrated with a building

“…If special EVSE with bi-directional capabilities are installed, a PEV battery can be discharged for the purposes of supplying electricity in support of improving the performance of a building [27][28][29], micro-grid [30][31][32], or macro grid [33] as well as reducing the need for electric energy storage in systems with high renewable penetrations [34]. Other work has focused on pricing methods for public EVSE to minimize cost of operation [35], improve customer access to EVSE [36], improve return on work place EVSE investment while remaining cost competitive with gasoline vehicles [37], improve overall grid operation [38], and developing refueling algorithms that reduce the impact of local distribution circuits [39].…”

Smart Electric Vehicle Charging Through Cloud Monitoring and Management

“…However, all the charging stations require strict timing constraints in their charging patterns so that the xEVs could be charged serviced reliably [10]. As the charging stations are coupled to the mains supply system the power drawn by them in different time instants should not induce encumbrance on the back end smart grid (SG) system [11]. Thus, it is the call of the hour to develop a centralized EV/PHEV charging management system which controls demand and supply problem within the dedicated time frames.…”

“…The interpolated FFT algorithm employed by authors of [7] attempts to devise an cost effective interactive charging strategy which maintains grid stability by minimizing the grid load factor, and ceils the optimal battery charging rate within admissible limits. The work in [11] demonstrates the optimization of charging cost of PHEV fleet through moving window optimization scheme. The authors in [21] adopted a dynamic programming approach to simulate a bidirectional control flow model (V2G also enabled) that coordinates the charging scheme.…”