Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation

Accurate lifetime prediction of lithium-ion batteries accelerates battery optimization and improves safety [1-4]. Although this task is challenging due to complicated and convolved degradation mechanisms, various studies have demonstrated the potential in using data-driven approaches [5-13], physics-based approaches [14-18], and hybrid approaches [19-26]. For accurate battery health monitoring, diagnostic techniques such as Differential Voltage Fitting (DVF) [27-30], Incremental Capacity Analysis (ICA) [31, 32], Electrochemical Impedance Spectroscopy (EIS) [10, 33-35], and Hybrid Pulse Power Characterization (HPPC) [36, 37] were developed for physics-based feature extraction during battery operation. Further optimization of these diagnostic techniques includes novel State of Health (SoH) feature development [38-41] and diagnostic time reduction [42, 43]. Compared to the extensive research on lifetime prediction during operation, there have been few studies on lifetime prediction during the manufacturing process (i.e., extreme early cycle life prediction) because of the limited availability of public manufacturing data. In fact, the cycle life can vary greatly based on the protocol used during formation, in which a passivation layer of Solid Electrolyte Interphase (SEI) is rapidly formed on the anode to limit further degradation during use. For example, Weng et al. [44] showed that the Nickel Manganese Cobalt (NMC)/graphite pouch cells with the fast formation protocol proposed by Wood et al. [45, 46] had in average 25% longer cycle lives than the pouch cells with a baseline formation protocol when aging the cells in both room temperature and high-temperature (45