Reduction of electrochemical impedance spectroscopy measurement time for lithium-ion batteries based on compressive sensing

Abstract

This work proposes the application of compressive sensing (CS) to reduce the measurement time in electrochemical impedance spectroscopy (EIS) for lithium-ion batteries. EIS is a non-destructive frequency response technique that provides valuable information on the state and degradation mechanisms occurring inside a battery. However, EIS measurement time is lengthy, making it impractical for evaluating the state of operating cells. CS is a signal-processing technique that enables the efficient acquisition and reconstruction of signals from a reduced number of measurements. The study aims to identify a suitable transform domain using dictionary learning that facilitates the adoption of CS techniques for the compression of the EIS data obtained from lithium-ion batteries. Thanks to the reduced number of EIS measurements, the proposed CS-based EIS achieves approximately 40% reduction in measurement time for open-source and in-house collected data, respectively, with minimal accuracy degradation.