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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.

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, k-Nearest 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.

WINSmartEV™ 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.

With an increased number of plug-in electric vehicles (PEVs) on the roads, PEV charging infrastructure is gaining an ever-more important role in simultaneously meeting the needs of drivers and those of the local distribution grid. However, the current approach to charging is not well suited to scaling with the PEV market. If PEV adoption continues, charging infrastructure will have to overcome its current shortcomings such as unresponsiveness to grid constraints, low degree of autonomy, and high cost, in order to provide a seamless and configurable interface from the vehicle to the power grid. Among the tasks a charging station will have to accomplish will be PEV identification, charging authorization, dynamic monitoring, and charge control. These will have to be done with a minimum of involvement at a maximum of convenience for a user. The system proposed in this work allows charging stations to become more responsive to grid constraints and gain a degree of networked autonomy by automatically identifying and authorizing vehicles, along with monitoring and controlling all charging activities via an RFID mesh network consisting of charging stations and in-vehicle devices. The proposed system uses a ZigBee mesh network of in-vehicle monitoring devices which simultaneously serve as active RFID tags and remote sensors. The system outlined lays the groundwork for intelligent charge-scheduling by providing access to vehicle’s State of Charge (SOC) data as well as vehicle/driver IDs, allowing a custom charging schedule to be generated for a particular driver and PEV. The approach presented would allow PEV charging to be conducted effectively while observing grid constraints and meeting the needs of PEV drivers.

Plug-In Electric Vehicle (PEV) charging today happens with little knowledge of the state of the vehicle being charged. In order to implement smart charging algorithms and other capabilities of the future smart grid, provisions for remote PEV monitoring will have to be developed and tested. The UCLA Smart-grid Energy Research Center (SMERC) is working on a smart charging research platform that includes data acquired in real time from PEVs being charged in order to investigate smart charging algorithms and demand response (DR) strategies for PEVs in large parking garage settings. The system outlined in this work allows PEVs to be remotely monitored throughout the charging process by a smart-charging controller communicating through a mesh network of charging stations and in-vehicle monitoring devices. The approach may be used for Vehicle to Grid (V2G) communication as well as PEV monitoring.

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.

With an increased number of Electric Vehicles (EVs) on the roads, charging infrastructure is gaining an ever-more important role in simultaneously meeting the needs of the local distribution grid and of EV users. This paper proposes a mesh network RFID system for user identification and charging authorization as part of a smart charging infrastructure providing charge monitoring and control. The Zigbee-based mesh network RFID provides a cost-efficient solution to identify and authorize vehicles for charging and would allow EV charging to be conducted effectively while observing grid constraints and meeting the needs of EV drivers.