Physics-informed deep learning for lithium-ion battery diagnostics using electrochemical impedance spectroscopy

Abstract

Lithium-ion batteries (LIBs) are prevalent energy storage devices in industrial fields and modern life, but are subjected to capacity degradation during operation due to the varying internal states. To ensure the efficiency, safety and reliability of LIBs, LIBs diagnostics by analyzing the internal states and estimating the capacity is crucial. However, model-based methods and deep learning models for LIBs diagnostics are mainly based on charging and discharging curves that contain limited information on battery internal states, and suffer from either model inaccuracy or limited interpretability. To achieve accurate and interpretable LIBs diagnostics, a novel physics-informed deep learning (PIDL) framework is proposed in this study using the electrochemical impedance spectroscopy (EIS), which is a powerful, non-invasive and information-rich technique. The EIS together with three physically informative parameters, which are extracted using model-based methods to investigate the varying internal states, are taken as inputs for capacity estimation, and two data fusion manners are proposed and investigated. Besides, to extract physically interpretable and conducive latent features, physical regularization and multi-task learning are incorporated, which make effective use of three information sources: physical knowledge, measurements and domain knowledge. Moreover, to evaluate the predictive uncertainty of capacity estimation, the deep ensemble strategy is adopted. The effectiveness and superiority of the PIDL framework are demonstrated using EIS datasets of eight commercially LIBs collected from the University of Cambridge.