Methodologies and integrated architecture for advance sensing on next-generation smart battery cells

Batteries are the catalyst for the shift towards sustainable and smart mobility, as well as supplying clean, affordable, and renewable energy sources. To improve the battery performance at level of management and control of the system, new methodologies need to be found to monitor in operando and in situ the batteries' state parameters, thus strengthening safety, reliability, and cycle life of batteries. The vision of this PhD project is to integrate smart functionalities at the cell level and study possible data fusion algorithms to create predictive models of the battery state parameters. In particular, the thesis is focused on the investigation of the Elecrochemical Impedance Spectoscopy (EIS) as diagnosis tool for the online monitoring of the battery. The first step towards this goal is to define the sensing technologies to be integrated in a single battery cell to enable the in operando and in situ measurement of battery physical quantities. EIS can respond to the challenges of shortening the measurement time and reducing the dimensions of the system to be integrated into the battery cell. A novel approach to perform broadband EIS based on a multi-band multisine excitation signal is proposed to optimize the measurement time and signal-to-noise ratio (SNR). The related EIS-based sensing system based on a sigma-delta architecture is developed and tested as impedance demonstrator for a future integration at the cell level. Finally, experimental EIS data of lithium battery cells are collected and evaluated, to demonstrate that the proposed methodologies are suitable for the online battery monitoring.