We believe that an EVSE may recognize the types of PEVs connected to charge without direct communication, but based on the PEV’s unique implementation of communication protocols, and distinct charge profiles. The goal of this research project are to determine how these implementations manifest themselves in the physical characteristics of communication signals, and in charging profiles in the first few minutes after a PEV connects to and EVSE for charge. We are in a uniquely strong position to perform this research because our university is the center of vehicle‐to‐grid technology. Our research group builds and deploys electric vehicle charging stations for our campus and for world‐wide v2g technology trials. We also manage a fleet of three dozen electric vehicles operating on our campus. All this infrastructure is readily available to collect perform extensive data collections of actual electric vehicle charging activity.

For collecting data and testing recognition algorithms, we will utilize the University of Delaware V2G project’s extensive network of EVSEs. We will configure several of the EVSEs to perform auxiliary reporting. These reports will be preprocessed via custom programs that we will develop so they may be easily imported into data‐mining tools to define an algorithm for recognizing PEVs. Using the results, we will configure several EVSEs to perform recognition. The accuracy of the recognition algorithm will be verified using planned, observed plug‐in events, and reporting tools such as PlugShare. The algorithm shall be considered sufficiently robust when, for all plug in events, the EVSE recognizes all EVs when the type is in the system, and correctly identifies when an unfamiliar type of EV is connected.

The broader impact of this work is to enable parties managing PEVs (such as a utility or aggregator) to project power demand, avoid faults and circuit overloads, and optimize charging for all vehicles. In addition to publishing this work, we plan to develop open‐source tools that will enable other researchers to duplicate and expand on our experiments. On a broader scope, the outcomes of this project comprise a resource for those developing smart grids. In particular, the knowledge and tools resulting from our research may be used for greater integration of distributed generation in electric distribution systems, implementation of smart metering, and large‐scale load management.