Project description
Advanced modelling tools to boost battery performance
While battery performance and safety have become increasingly urgent, current battery management systems fall short. In this context, the EU-funded NEMO project seeks to redefine the state-of-the-art in battery management by harnessing advanced physics-based and data-driven models, complemented by state estimation techniques. The project is set to introduce innovative software and hardware solutions, offering a powerful tool to enhance battery performance and longevity in our ever-evolving energy landscape. Specifically, it will use a spectrum of sensors capturing battery performance information as well as specialised electrochemical impedance spectroscopy (EIS) sensors. The solutions will pave the way for new performance, lifespan, and safety models.
Objective
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 ActionsCoordinator
1050 Bruxelles / Brussel
Belgium