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BattMaxLife

Financiado

Life-long health monitoring and assessment for lifetime maximization of Li-ion b...

Life-long health monitoring and assessment for lifetime maximization of Li-ion batteries Lithium-ion (Li-ion) batteries have an integrated battery management system (BMS) whose performance is critical in maximizing the available life of the batteries and increasing their sustainability. The BMS monitors state paramete... ver más

31/08/2025

AAU

231K€

Presupuesto del proyecto: 231K€

Líder del proyecto

AALBORG UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-03-24

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

I+D

Cerrada hace 2 años

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1 Participantes

AAU

230.77K€ | Lider

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Duración del proyecto: 29 meses Fecha Inicio: 2023-03-24
Fecha Fin: 2025-08-31

Líder del proyecto

AALBORG UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 231K€

Descripción del proyecto Lithium-ion (Li-ion) batteries have an integrated battery management system (BMS) whose performance is critical in maximizing the available life of the batteries and increasing their sustainability. The BMS monitors state parameters, such as state-of-charge (SOC) and state-of-health (SOH), for safety and performance optimization purposes. The accuracy of these parameters plays a critical role in maximizing the available battery lifetime. This MSCA fellowship project aims to maximize the available lifetime of Li-ion batteries (and subsequently the lifetime of their applications) by making the battery impedance accessible in real-time for the BMS state estimation algorithms by using a novel ternary-sequence impedance measurement method that is integrated onboard the battery pack. The impedance-based SOC and SOH estimation algorithms will offer higher accuracy compared to the available traditional algorithms. Thus, the BMS will be able to optimize the battery's use and consequently maximize its life in its primary application (i.e., first-life application), such as in electric vehicles. Despite lacking the performance for the primary application, the battery is still likely to be used in a less demanding application, i.e., stationary energy storage. With adequate health information available (using the novel impedance-based algorithms) at the end of its first-life, the battery will become an attractive candidate for second-life application, as reliable predictions can be made about its remaining lifetime, safety, and economic value. With first-life maximized and second life provided for the battery pack, the lifetime of the battery is significantly prolonged before the eventual recycling, increasing Li-ion batteries' sustainability This highly innovative project has a high scientific, economic, and societal impact. Furthermore, the project will allow the applicant to enrich his technical and managerial skills thus boosting his career prospects and enlarging his horizon

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