Battery management plays an essential role by ensuring an efficient and safe battery operation. However, current battery management systems (BMS) typically rely on semi-empirical battery models (such as equivalent-circuit models) and on a limited amount of measured data. Consequently, there is currently a lack of knowledge about the overall state of the battery in operation, resulting in suboptimal utilisation.
Projects are expected to substantially advance the state of the art in the field of battery management, by developing innovative physics and data-based approaches, both at the software and hardware levels to ensure an optimised and safe utilisation of the battery system during all modes of operation.
Projects should pave the way towards next-generation BMS, which will leverage on an increased computational capability enabling the execution of advanced software, and on the ability to acquire, communicate and analyse large amount of data. Those next-generation BMS will lead to significantly enhanced performances, lifetime, reliability and safety of the battery system, by a dynamic update of battery usage limitations and the possibility to widen the battery operating range in a controlled manner. Moreover, they will provide open access to an increased amount of FAIR[[FAIR (Findable, Accessible, Interoperable, Reusable)]] data (which can possibly be processed offline), enabling the development of effective degradation models (thus reducing the investments costs of storage systems by mean of improved sizing during the design phase), and facilitating predictive maintenance and end-of-life management.
Projects are expected to develop technologies at both the software and hardware levels, with a validation through a lab-scale prototype at TRL 4. Several of the following items should be addressed: the development and implementation of physics-based battery models (e.g. ageing phenomena models); adaptable battery models (e.g. based on operation data); sensor-based solutions at the battery system level (e.g. with respect to sensor integration, communication with the battery management, data fusion, data analysis); advanced state estimators (e.g. state of health, state of function, state of energy, state of power, state of safety); methods for the prognosis of remaining useful lifetime and ageing; methods for the early detection or prediction of failures; solutions for the management of special situations (e.g. unbalanced or dysfunctional cells). Project results should be applicable to a broad range of transport or stationary applications.
The selected projects are invited to participate to BRIDGE[[https://www.h2020-bridge.eu/]] activities when considered relevant.
This topic implements the co-programmed European Partnership on ‘Towards a competitive European industrial battery value chain for stationary applications and e-mobility’.