ENERGETIC – Next Generation Battery Management System

The requirements of the BMS are getting more and more advanced to maximise the performance of lithium-ion batteries. Currently, the BMS only receives little information from a real battery, which makes it difficult to determine the ageing and safety status of a battery. Furthermore, an on-board BMS cannot currently store or process large amounts of data during vehicle operation, so the real-time capability and data utilisation rate are low. For an efficient battery management, it is necessary to in-deth study the mechanisms such as battery ageing and thermal runaway. On the other hand, there are huge challenges in the research of accurate state estimation, including smart charging, fast charging, thermal management and extending useful life.

The integration of new technologies such as digital twins (DT) and artificial intelligence into the BMS is thus essential for data management, as well as the use of edge and cloud computing. ENERGETIC's vision includes not only monitoring and predicting the remaining useful life of a lithium-ion battery with a digital twin, but also diagnosing and thus analysing the causes of degradation using explainable AI models. This involves the development of new sensor technologies, the combination and validation of multi-physical and data-driven models, information fusion through artificial intelligence, real-time testing and the development of intelligent digital twins.

As part of the European project, HKA's IEEM is developing a hardware abstraction layer platform for recording real and newly developed sensor data and is simulating the energy demand in the first lifetime during various driving cycles.