WINSmartEV TM is a smart electric vehicle charging system that has been built and is currently in operation. It is a software and network based EV charging system designed and built around the ideas of intelligent charge scheduling, multiplexing (connecting multiple vehicles to each circuit) and flexibility. This paper gives an overview of this smart charging system with an eye toward its unique features and capabilities.
WINSmartEV is a software based plug-in electric vehicle (PEV) monitoring, control, and management system. It not only incorporates intelligence at every level so that charge scheduling can avoid grid bottlenecks, but it also multiplies the number of PEVs that can be plugged into a single circuit. This paper proposes, designs, and executes many upgrades to WINSmartEV. These upgrades include new hardware that makes the level 1 and level 2 chargers faster, more robust, and more scalable. It includes algorithms that provide a more optimal charge scheduling for the level 2 (EVSE) and an enhanced vehicle monitoring/identification module (VMM) system that can automatically identify PEVs and authorize charging.
As Electric Vehicles (EVs) increase, charging infrastructure becomes more important. When during the day there is a power shortage, the charging infrastructure should have the options to either shut off the power to the charging stations or to lower the power to the EVs in order to satisfy the needs of the grid. This paper proposes a design for a smart charging infrastructure capable of providing power to several EVs from one circuit by multiplexing power and providing charge control and safety systems to prevent electric shock. The safety design is implemented in different levels that include both the server and the smart charging stations. With this smart charging infrastructure, the shortage of energy in a local grid could be solved by our EV charging management system.
This paper describes the core cellphone application algorithm which has been implemented for the prediction of energy consumption at Electric Vehicle (EV) Charging Stations at UCLA. For this interactive user application, the total time of accessing database, processing the data and making the prediction, needs to be within a few seconds. We analyze four relatively fast Machine Learning based time series prediction algorithms for our prediction engine: Historical Average, kNearest Neighbor, Weighted k-Nearest Neighbor, and Lazy Learning. The Nearest Neighbor algorithm (k Nearest Neighbor with k=1) shows better performance and is selected to be the prediction algorithm implemented for the cellphone application. Two applications have been designed on top of the prediction algorithm: one predicts the expected available energy at the station and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is about one second for both applications.
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