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Herein, a methodology to investigate aging of commercial cylindrical Li‐ion cells is introduced. Distribution of relaxation time (DRT) method is applied to deconvolute electrochemical impedance spectroscopy (EIS) measurements and separate those polarization effects that are usually overlapped in the frequency domain by means of a peak‐based representation. Half‐cells are built at the beginning and end of life to link the electrochemical and aging processes occurring at anode and/or the cathode sides. Moreover, lab‐made full‐cells are exploited to verify the reproducibility when compared with cylindrical cells. The results of an extensive analysis of around 500 EIS spectra return an unambiguous attribution of different electrochemical processes to different time constants and ultimately to different DRT peaks. Digital imaging validates graphite degradation, mainly related to lithium plating. Scanning electron microscopy validates the degradation at NMC cathode, mainly attributed to particle cracking. It is concluded that DRT peaks allow to characterize cell aging and their tracking can help to develop more reliable state of health estimators.

Accurate state of health (SoH) estimation is crucial to optimize the lifetime of Li-ion cells while ensuring safety during operations. This work introduces a methodology to track Li-ion cells degradation and estimate SoH based on electrochemical impedance spectroscopy (EIS) measurements. Distribution of relaxation times (DRT) were exploited to derive indicators linked to the so-called degradation modes (DMs), which group the different aging mechanisms. The combination of these indicators was used to model the aging progression over the whole lifetime (both in the “pre-knee” and “after-knee” regions), enabling a physics-based SoH estimation. The methodology was applied to commercial cylindrical cells (NMC811|Graphite SiOx). The results showed that loss of lithium inventory (LLI) is the main driving factor for cell degradation, followed by loss of cathode active material (LAMC). SoH estimation was achievable with a mean absolute error lower than 0.75% for SoH values higher than 85% and lower than 3.70% SoH values between 85% and 80% (end of life). The analyses of the results will allow for guidelines to be defined to replicate the presented methodology, characterize new Li-ion cell types, and perform onboard SoH estimation in battery management system (BMS) solutions.