Descripción del proyecto
Herramientas avanzadas de modelización para mejorar el rendimiento de las baterías
Aunque el rendimiento y la seguridad de las baterías son cada vez más urgentes, los actuales sistemas de gestión de baterías se quedan cortos. En este contexto, el equipo del proyecto NEMO, financiado con fondos europeos, pretende redefinir el estado de la técnica en la gestión de baterías aprovechando modelos avanzados basados en la física y los datos, complementados con técnicas de estimación del estado. El equipo del proyecto introducirá soluciones innovadoras de «software» y «hardware» que ofrecerán una potente herramienta para mejorar el rendimiento y la longevidad de las baterías en un panorama energético en constante evolución. En concreto, utilizará un espectro de sensores que capten información sobre el rendimiento de las baterías, así como sensores especializados de espectroscopia de impedancia electroquímica. Las soluciones allanarán el camino hacia nuevos modelos de rendimiento, vida útil y seguridad.
Objetivo
NEMO project aims at advancing the state of the art of battery management systems (BMS) by engaging advanced physics-based and data-driven battery models and state estimation techniques. Towards achieving this goal, the consortium tends to provide efficient software and hardware to handle, host, process, and execute these approaches within high-end local processors and cloud computing.
NEMO benefits from a wide range of sensor information acquired at high frequencies in addition to dedicated electrochemical impedance spectroscopy (EIS) sensors which allow for the identification of ongoing electrochemical reactions inside each individual battery cell. Capable hardware for storing and processing such measurements will be provided by the tier1 members of this industry onboard the consortium.
The availability of such diverse physical information on batteries onboard makes room for developing cutting-edge performance, lifetime, and safety battery models and state estimators within NEMO, and validating them on two different BMS configurations. Physics-based performance model parameters continuously get updated as the battery ages, so that performance and safety state indicators maintain the least possible error. The data-driven approaches exploit mathematical algorithms to be trained upon the large datasets made available from historical or laboratory-generated battery information. Combinations of coupled physics-based and data-driven approaches are also foreseen to be implemented within NEMO as another innovation of the project to propose next-generation BMS.
Solutions offered by NEMO considerably extend battery life and make the battery system safer within long-term operation since every individual cell is monitored, controlled, and studied. NEMO’s ambitious solutions for stationary and automotive use cases are expected to be validated by industrial partners and to take a considerable share of the market in later years.
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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinador
1050 Bruxelles / Brussel
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