Can fusing Li-ion cell impedance in BMS algorithms lead to further optimization of next generation EV battery system?

In addition to the costs, range anxiety and fast-charging capability, the safety of Li-ion batteries (LIBs) is most important. In the proposed conference contribution, we introduce the practical application use cases of Electrochemical Impedance Spectroscopy (EIS) in electric vehicles (EVs) and how EIS can further complement the current BMS data driven algorithms, enabling better use of the existing LIB by fully understanding its limits. Such sensor fusion data coupled with physical, machine learning, and AI algorithms can further accelerate acceptance of EVs without compromising battery health and safety.

One of the main functions of a battery management system (BMS) is to ensure that every Li-Ion cell within the battery pack functions in its Safe Operating Area (SOA) since operation outside the SOA can lead to severe consequences as critical as thermal runaway (TR).

Despite the nominal SOA, each cell within the pack would operate under a different mission profile, depending on the vehicle and/or the driving/charging profile. The different stress applied to the cells can lead to a scenario in which each cell’s internal state, including the extent of lithium plating or electrode cracking, can vary significantly both at an intra-pack and inter-pack level. Hence, accurate estimation of battery performance and prediction of battery failures require a non-invasive method to gather information about the cell’s state at a microscale, complementing the already available battery parameters.

EIS is a powerful technique to characterize electrochemical systems for e.g., Li-ion cell. EIS captures the response of the cell over a broad frequency range, with different frequencies correlating to distinct physical, chemical and mechanical changes in the active material.

In addition to the above, the conference contribution primarily emphasizes on potential key application use cases of EIS in EVs and the possible EIS implementation propositions in real EV applications.