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Enhanced Modeling and Optimization of Batteries Incorporating Lithium ion Elemen...

Enhanced Modeling and Optimization of Batteries Incorporating Lithium ion Elements 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 Uni... ver más

30/09/2018

ETH Zürich

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2018-09-30 No tenemos la información de la convocatoria

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

ETH Zürich

0.00€ | Lider

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Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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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