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H2020

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HORIZON-CL5-2022-D2-01-09
Physics and data-based battery management for optimised battery utilisation (Batteries Partnership)
ExpectedOutcome:Project results are expected to contribute to all of the following expected outcomes:
Sólo fondo perdido 0 €
Europeo
Esta convocatoria está cerrada Esta línea ya está cerrada por lo que no puedes aplicar. Cerró el pasado día 06-09-2022.
Se espera una próxima convocatoria para esta ayuda, aún no está clara la fecha exacta de inicio de convocatoria.
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Presentación: Consorcio Consorcio: Esta ayuda está diseñada para aplicar a ella en formato consorcio..
Esta ayuda financia Proyectos:

ExpectedOutcome:Project results are expected to contribute to all of the following expected outcomes:

New physics and data-based approaches for battery management, with the potential to enhance performances, lifetime, reliability and safety of battery systems for transport and stationary applications.New physics and data-based approaches for battery management facilitating predictive maintenance, and/or knowledge-driven end-of-life management of battery systems, and/or the development of more accurate degradation models.
Scope:Battery management plays an essential role by ensuring an efficient and safe battery operation. However, current battery management systems (BMS) typically rely on semi-empirical battery models (such as equivalent-circuit models) and on a limited amount of measured data. Consequently, there is currently a lack of knowledge about the overall state of the battery in operation, resulting in suboptimal utilisation.

Projects are expected to substantially advance the state of the art in the field of battery management, by developing innovative physics and data-based approaches, both at the software and hardware levels to ensure an... ver más

ExpectedOutcome:Project results are expected to contribute to all of the following expected outcomes:

New physics and data-based approaches for battery management, with the potential to enhance performances, lifetime, reliability and safety of battery systems for transport and stationary applications.New physics and data-based approaches for battery management facilitating predictive maintenance, and/or knowledge-driven end-of-life management of battery systems, and/or the development of more accurate degradation models.
Scope:Battery management plays an essential role by ensuring an efficient and safe battery operation. However, current battery management systems (BMS) typically rely on semi-empirical battery models (such as equivalent-circuit models) and on a limited amount of measured data. Consequently, there is currently a lack of knowledge about the overall state of the battery in operation, resulting in suboptimal utilisation.

Projects are expected to substantially advance the state of the art in the field of battery management, by developing innovative physics and data-based approaches, both at the software and hardware levels to ensure an optimised and safe utilisation of the battery system during all modes of operation.

Projects should pave the way towards next-generation BMS, which will leverage on an increased computational capability enabling the execution of advanced software, and on the ability to acquire, communicate and analyse large amount of data. Those next-generation BMS will lead to significantly enhanced performances, lifetime, reliability and safety of the battery system, by a dynamic update of battery usage limitations and the possibility to widen the battery operating range in a controlled manner. Moreover, they will provide open access to an increased amount of FAIR[1] data (which can possibly be processed offline), enabling the development of effective degradation models (thus reducing the investments costs of storage systems by mean of improved sizing during the design phase), and facilitating predictive maintenance and end-of-life management.

Projects are expected to develop technologies at both the software and hardware levels, with a validation through a lab-scale prototype at TRL 4. Several of the following items should be addressed: the development and implementation of physics-based battery models (e.g., ageing phenomena models); adaptable battery models (e.g., based on operation data); sensor-based solutions at the battery system level (e.g., with respect to sensor integration, communication with the battery management, data fusion, data analysis); advanced state estimators (e.g., state of health, state of function, state of energy, state of power, state of safety); methods for the prognosis of remaining useful lifetime and ageing; methods for the early detection or prediction of failures; solutions for the management of special situations (e.g., unbalanced or dysfunctional cells). Project results should be applicable to a broad range of transport or stationary applications.

The selected projects are invited to participate to BRIDGE[2] activities when considered relevant.

This topic implements the co-programmed European Partnership on ‘Towards a competitive European industrial battery value chain for stationary applications and e-mobility’.


Specific Topic Conditions:Activities are expected to achieve TRL 4 by the end of the project – see General Annex B.




Cross-cutting Priorities:Digital AgendaArtificial IntelligenceCo-programmed European Partnerships


[1]FAIR (Findable, Accessible, Interoperable, Reusable)

[2]https://www.h2020-bridge.eu/

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Temáticas Obligatorias del proyecto: Temática principal:

Características del consorcio

Ámbito Europeo : La ayuda es de ámbito europeo, puede aplicar a esta linea cualquier empresa que forme parte de la Comunidad Europea.
Tipo y tamaño de organizaciones: El diseño de consorcio necesario para la tramitación de esta ayuda necesita de:
Empresas Micro, Pequeña, Mediana, Grande
Centros Tecnológicos
Universidades
Organismos públicos

Características del Proyecto

Requisitos de diseño: Duración: Requisitos técnicos: ExpectedOutcome:Project results are expected to contribute to all of the following expected outcomes: ¿Quieres ejemplos? Puedes consultar aquí los últimos proyectos conocidos financiados por esta línea, sus tecnologías, sus presupuestos y sus compañías.
Capítulos financiables: Los capítulos de gastos financiables para esta línea son:
Madurez tecnológica: La tramitación de esta ayuda requiere de un nivel tecnológico mínimo en el proyecto de TRL 4:. Es el primer paso para determinar si los componentes individuales funcionarán juntos como un sistema en un entorno de laboratorio. Es un sistema de baja fidelidad para demostrar la funcionalidad básica y se definen las predicciones de rendimiento asociadas en relación con el entorno operativo final. leer más.
TRL esperado:

Características de la financiación

Intensidad de la ayuda: Sólo fondo perdido + info
Fondo perdido:
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The funding rate for RIA projects is 100 % of the eligible costs for all types of organizations.
Condiciones: No existe condiciones financieras para el beneficiario.

Información adicional de la convocatoria

Efecto incentivador: Esta ayuda no tiene efecto incentivador. + info.
Respuesta Organismo: Se calcula que aproximadamente, la respuesta del organismo una vez tramitada la ayuda es de:
Meses de respuesta:
Muy Competitiva:
No Competitiva Competitiva Muy Competitiva
No conocemos el presupuesto total de la línea pero en los últimos 6 meses la línea ha concecido
Total concedido en los últimos 6 meses.
Minimis: Esta línea de financiación NO considera una “ayuda de minimis”. Puedes consultar la normativa aquí.

Otras ventajas

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