Methodologies and integrated architecture for EIS sensing on smart battery cells

Batteries play a key role in advancing the transition to sustainable and intelligent mobility. Enhancing battery performance and improving safety, reliability, and useful life through monitoring of key parameters in operando and in situ, requires innovative methods. Electrochemical impedance spectroscopy (EIS) is a technique widely used at the laboratory-level for battery cell monitoring / diagnosis, but its design and validation of online in-situ measurement systems are characterized by important challenges because of complex hardware-software interactions and non-idealities. The first step towards this goal is to develop an integrated co-simulation framework to support the design, debugging, and validation of EIS measurement systems. The proposed framework models the hardware and software components of an EIS-based system, allowing to simulate and analyze the impedance measurement process in its entirety. It is a diagnostic tool for discovering subtle non-ideal behaviors of a circuit and is useful for the refinement of the measurement system. The framework is used as a digital twin of the system for the development of an integrated chip based on electrochemical cal impedance spectroscopy that could be inserted at the cell level and could be extremely useful for improving battery performance. The simulation framework has been fundamental for the realization of the final integrated chip based on EIS that is currently in fabrication. The chip, in conjunction with an efficient algorithm for the data post-processing, allows us to monitor in real-time the battery impedance. The algorithm used shows remarkable performance for EIS application. In the future, these impedance data could be used to extrapolate battery parameters.