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

Descripción del proyecto

Prolongación de la vida útil de las baterías de iones de litio

Las baterías de iones de litio impulsan nuestro mundo moderno, pero su vida útil limitada es cada vez más preocupante. La precisión de parámetros cruciales como el estado de carga y la condición es vital para el rendimiento y la sostenibilidad de las baterías. Sin embargo, hay un problema crítico: la vida útil limitada de estas baterías. Con el apoyo de las Acciones Marie Skłodowska-Curie, el equipo del proyecto BattMaxLife integra un novedoso método de medición de la impedancia en los conjuntos de baterías, lo que mejora la precisión de los parámetros, optimiza el uso de las baterías y, en última instancia, prolonga su vida útil, lo que revoluciona la forma en que aprovechamos la energía. Gracias a las capacidades mejoradas del sistema de gestión de las baterías (BMS, por sus siglas en inglés), las baterías de iones de litio pueden optimizarse para sus aplicaciones principales, como los vehículos eléctricos, lo que prolonga considerablemente su primera vida útil. Al prolongar la vida útil de las baterías de iones de litio, es posible reducir los residuos y conservar los recursos.

Objetivo

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