E-MOBILE
Enhanced Modeling and Optimization of Batteries Incorporating Lithium-ion Elements
| Coordinatore | EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Switzerland [CH] |
| Totale costo | 1˙492˙800 € |
| EC contributo | 1˙492˙800 € |
| Programma | FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | ERC-2013-StG |
| Funding Scheme | ERC-SG |
| Anno di inizio | 2013 |
| Periodo (anno-mese-giorno) | 2013-10-01 - 2018-09-30 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH
Organization address
address: Raemistrasse 101 contact info |
CH (ZUERICH) | hostInstitution | 1˙492˙800.00 |
Mappa
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Obiettivo del progetto (Objective)
'Developing rechargeable batteries with larger storage capacity, higher output power, faster charge/discharge time, and longer calendar lifetime could significantly impact the economical and environmental future of the European Union. New generations of lithium-ion batteries (LIBs) based on nanostructured electrodes are perfect candidates to supply all-electrical vehicles and favor the usage of renewable energies instead of fossil fuels. Hence, the global LIB revenue is expected to expand from $11 billion in 2011 up to $50 billion in 2020. The goal of this project is therefore to provide an advanced simulation and optimization platform to design LIBs with improved performance and increase the competitiveness of Europe in this domain. The proposed computer aided design (CAD) tool must satisfy three key requirements in order to reach this ambitious objective: (i) computational efficiency, (ii) results accuracy, and (iii) automated predictability. Massively parallel computing has been identified as the enabling technology to handle the first requirement. The second one will be addressed by implementing a state-of-the-art device operation model relying on a multi-scale resolution of the battery electrodes, a detailed description of the electron and ion motions, a material parametrization derived from ab-initio quantum transport techniques, and a validation of the approach through comparisons with experimental measurements. Finally, to meet the last requirement, the operation model will be coupled to a genetic algorithm optimizer capable of automatically predicting the LIB configuration that best matches pre-defined performance targets. The resulting CAD tool will be released as an open source package so that the entire battery community can benefit from it.'
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