Our objective is to considerably extend battery life and make the battery system safer within long-term operation of stationary and automotive use cases.
Among our technical improvements:
Complete avoidance of foreseeable critical safety issues not linked to severe mechanical impacts.
Extension of the first-life battery lifetime by at least 20% and capture of failure mode with 100% accuracy.
NEMO’s solutions are expected to be validated by industrial partners and to take a considerable share of the market in the future.
Our solutions will position the European BMS industry at the forefront of digital battery management innovations and allow them to take a maximum share of the BMS market estimated to €3.5 bn by 2026. These performance improvements will further increase social acceptance and uptake of the electrification of the European energy system.
NEMO especially contributes to:
Accelerate roll out of electrified mobility through increased attractiveness regarding improvements of e-vehicles operation.
Improved Life Cycle Assessment of the final product segment of the battery value chain and accelerated roll-out of circular designs though innovations that allow for a straight-forward second life usage with economic guarantees.
Increased exploitation and reliability of batteries through demonstration of innovative use cases of battery integration in stationary energy storage and e-vehicles.
We aim to leverage in-situ and in-operando EIS sensing, along with active cell switching for balancing at cell-level and sufficient computing power, to execute real-time models and algorithms.
Towards achieving these goals, 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.
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.