| Acronym: PredictEV |
| Main Objective: • Characterize the fundamental benefits of using real-time information on the state and location of individual EVs in combination with power grid data and dynamic digital maps towards ensuring superior flexibility, cost effectiveness, energy efficiency, security and reliability in urban environments. • Devise new distributed prediction tools and dissemination techniques for dynamic management of urban power systems, including EV charging, demand-side management, energy storage, and combined heat and power stations. • Design key components for gathering, sharing and processing large quantities of real-time data collected by EVs and other vehicles in urban environments. The outcomes are reliable predictions that can be used in advanced smart grid operation. • Provide guidelines to improve the network infrastructure that supports urban power grids, while avoiding costly infrastructure reinforcements (e.g. new power lines and transformers) and increasing the networks’ ability to integrate distributed renewable energy sources. • Deliver proof-of-concept validation through an experimental vehicular networking test-bed of about 500 taxi cabs, private cars, electrical vehicles, bicycles and buses circulating in the city of Porto, which aims to provide low-carbon mobility services for citizen and tourists. |
| Reference: 2011-90060 |
| Funding: CISCO |
| Start Date: 01-07-2012 |
| End Date: 01-12-2013 |
| Team: Joao Francisco Cordeiro de Oliveira Barros |
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| Local Coordinator: Joao Francisco Cordeiro de Oliveira Barros |
| Links: Internal Page |
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Associated Publications
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