Distributed Electric Vehicle Assignment and Charging Navigation in Cyber-Physical Systems

With a large-scale penetration of electric vehicles (EVs) in the transportation sector, it becomes challenging to guide all EVs heading to the suitable fast-charging stations (FCSs) with the largest satisfaction efficiently. In this paper, a distributed EV assignment and charging navigation framework is proposed under the context of cyber-physical systems. Making full use of sensing, information, communication, and control technologies, multiple agents, including EV drivers, FCSs, EV assignment centers, and distribution network operators, can cooperate with each to formulate the final decision efficiently. First, a distributed crowd-sensing technology is proposed to help each EV assignment center perceive the traffic conditions of the whole transportation system without obtaining private information of EVs. Then, an EV assignment and charging navigation model is formulated based on crowd-sensing results, a queueing model considering impatient leaving and the electricity network operation. To solve the EV assignment and charging navigation model efficiently, a distributed solution is proposed based on generalized benders decomposition and distributed biased min-consensus. The proposed framework and methodologies are verified in case studies. It can be concluded that compared with the conventional centralized optimization strategy, the computational time of the proposed method is improved significantly. Compared with the nearby assignment strategy, the proposed strategy can increase the charging utility for EV owners, relieve transportation network congestion, improve charging utilization for FCSs and enhance stability for distribution networks.

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