What is WINSmartEV™?
WINSmartEV™ is a smart, grid friendly, garage-friendly and user friendly research platform being developed in UCLA that allows plug-in devices or EVSmartPlugs™ to perform remote monitoring and control of EV charging through a smart communications network called WINSmartGrid™. These edge-of-the-network EV plug-in devices in UCLA collect critical data including energy consumption and various power-quality related variables and upload the data to a centralized database controlled by a database server. The WINSmartEV™ Research Network monitors the charging, schedules optimized aggregated charging sequences, and executes the schedule via the control network. The approach is also able to incorporate market and demand considerations into the scheduling of charging. WINSmartEV™ has about one year and a half year of research data collected on the UCLA campus.
EVSmartplug™ is the smart electric receptacle for EV charging using the WINSmartEV™ network. EVSmartPlug™ and WINSmartEV™ are trademarks of UCLA SMERC.
Vehicle to Grid (V2G)
Vehicle-to-Grid (V2G) research at UCLA SMERC is focused on demonstrating the possibility of V2G power flow from popular EVs. We are exploring how to achieve the maximum V2G power flow from each vehicle, while addressing challenges including response time and power sharing control. The SMERC research in V2G is also focused on facilitating a variety of applications, such as reactive power compensation, voltage regulation and distributed storage, to strengthen the power grid and lead EV usage to an entirely new, smarter era.
As an example of our work, UCLA SMERC delivered a Mitsubishi MiEV and customized V2G box to the Los Angeles Department of Water and Power. The vehicle uses a CHAdeMO port to feed energy back to the grid. It can perform a constant 1.0 kW V2G power flow into the grid, with this power able to be remotely controlled. Our customized box serves as a prototype to demonstrate V2G integration in distribution networks.
Scope of Research Distributed storage: With the introduction of V2G, EVs are virtual distributed generators. They can store power within their batteries and feed the power back to the grid when necessary. As V2G usually introduces voltage change, back-feeding power to the grid from multiple EVs needs to be carefully controlled to meet distribution network voltage constrains.
Stochastic modeling of user behavior: One of the major difference between an EV and a stationary battery is the fact that the EVs power availability is not guaranteed. On the other hand, energy available for V2G is highly stochastic. This stochastic nature of EV owner behavior is currently being studied for better EV integration to power grid.
Load sharing control: When several EVs are generating power and supporting multiple loads, the load sharing of each EV needs to be carefully studied. As loads are always fluctuating, controllers need to be designed to share the load proportionally among multiple EVs.
Ancillary Services: To maximize the EV owner benefits, the potential of using EVs to provide ancillary services needs to be researched. The ancillary services that an EV can provide are highly dependent on the V2G response time. Furthermore, algorithms need to be designed that take into consideration both maximizing ancillary services and meeting EV-owner energy demands.
Papers published under this project:
 Yubo Wang, Bin Wang, Chi-Cheng Chu, Pota, H.R and Rajit Gadh, "Optimal Energy Management
for Microgrid with Stationary and Mobile Storages", Energy and Buildings, 2016, vol 116, pp. 141-150
 Yubo Wang, Bin Wang, H. Nazaripouya, Chi-Cheng Chu, Rajit Gadh, "Optimal Energy
Management for Microgrid with Stationary and Mobile Storages," IEEE PES T&D 2016 (accepted)
 Yubo Wang, Sheikh, O., Boyang Hu, Chi-Cheng Chu, Rajit Gadh, "Integration of V2H/V2G hybrid
system for demand response in distribution network," IEEE SmartGridComm 2014, pp.812-817
 Yubo Wang, Nazaripouya, H., Chi-Cheng Chu, Rajit Gadh, Pota, H.R., "Vehicle-to-grid automatic
load sharing with driver preference in micro-grids," IEEE ISGT-Europe 2014, pp.1-6
 Bin Wang, Rui Huang, Yubo Wang, H. Nazaripouya, C. Qiu, Chi-Cheng Chu, Rajit Gadh,
"Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors,"
IEEE PES T&D 2016 (accepted)
California constitutes a significant automotive market - a place where demanding and energy-conscious consumers come together with creative designers from Hollywood, resulting in an environment rich in ideas on automotive innovation. As a result, California is home to some of the most significant innovations in EVs including Tesla and Fisker. As these innovations come on line their integration into the smart grid of the future becomes the next big challenge. We are developing a scalable and robust architecture utilizing wireless and RF-monitoring and control technologies derived from our REWINS™ research called WINSmartGrid™ that allows smart vehicle and energy storage and consumption management for vehicles in home or in the office. As part of the challenging long-term research project, we are developing a series of demonstrations both at home and in the office. The first phase - developing an on-campus demonstration within UCLA - requires conducting research and demonstration on UCLA's internal electric vehicle (EV) fleet and charging stations at UCLA for its integration with our local utility's managed grid.
The objective of this project is to reduce energy cost and usage and to increase the stability of local power system by managing the charging operations of the EVs. This will be accomplished using the smart grid wireless system under development at UCLA called WINSmartGrid™.
In this project, EV usage information and electric grid status will be collected wirelessly to determine better efficient and economic charging operation of the EVs. Due to different grid stability/reliability, geographical location of the EVs and driving patterns of the EVs, effective management of charging and backfill operations may be used to lower electricity rates and flatten electric load curve. Each EV will be equipped with a handheld device to allow the driver to receive instructions or seek advice to better manage his/her EV's battery charging/backfill process.
For example, an alert can be issued to the driver when the battery capacity is below a threshold level. The alert can include a list of near-by charging station's location, distance, current and projected energy cost based on the time of the day and use an intelligent cloud-computing the driver the optimum course of action.
The batteries on the EVs when not in driving status can also be collectively used to serve as the energy storage which can backfill into the local electric grid to prevent power outage during peak demand. In this scenario, an alert is issued to the driver when a predicted instability in the grid is detected. The alert can instruct the driver to bring the vehicle to the appropriate charging station to serve as backfill battery.
Existing EVs and charging stations usage patterns will be studied to determine the appropriate sensors and wireless communication modules to be installed. Communication and alerting systems will be implemented by integrating WINSmartGridTM with our local utility's Advanced Metering Infrastructure (AMI) and the Demand Respond project.
Major areas of this research/demonstration include:
WinSmartGrid™ Technology - WinSmartGrid™ platform is used as the infrastructure to i) connect to EV electric power sensors, GPS chips, and other EV data and ii) control and utilize the wireless network for communication iii) allow data filtration, aggregation and messaging, and iv) provide a portal for data integration and decision making.
Smart Energizing - the management of EV batteries' charging rate and extent of the charge backfill based on various data from grid stability, energy cost, vehicle location, battery status, driver's preference, and driving patterns.
Grid Balancing - grid management and prediction of peak and off-peak hours to store excess capacity, or to handle demands for large numbers of EVs charging efficient, economically and safely.
UCLA-WINRFID™ Technology - including RFID tags/readers on the EVs and charging stations to track and identify usage and preference information of each EV. Automatic charge/discharge intelligence stored within smart RFID tags managed by UCLA-WINRFID Technology.
Cyber Security - study and integration of cyber security technologies for secure wireless communication between battery and infrastructure or between two batteries, as part of the smart grid architecture.
The demonstration and results of this project will provide vast amounts of data, information and knowledge to allow an effective and large scale roll-out of grid-integrated EVs across the region and in the country.