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A comprehensive understanding of aging mechanisms and degradation diagnosis under fast charging in battery electric vehicles is needed. However, there is a lack of tools to capture both macroscopic and microscopic parameters, still focusing on experiment results analysis. To get a comprehensive degradation insight for improved service life, this study explores aging mechanisms in LiNi0.5Co0.2Mn0.3O2 graphite batteries under different fast charging conditions (0.6C to 2C) for up to 1000 equivalent full cycles (EFCs). An improved digital twin is used to quantify aging effects and identify aging modes, combining electrochemical techniques with post‐mortem analysis for assessing chemical and structural degradation. After 1000 EFCs, the dominant aging modes are loss of active material (LAM) in the negative electrode and loss of lithium inventory (LLI), surpassing LAM in the positive electrode. Compared to constant current charging, multistep fast charging effectively mitigates Li plating effects and graphite cracking, resulting in a 13% reduction in LAM. Additionally, optimizing the depth of discharge leads to at least 4% reductions in LLI and 16% in LAMpos. This study proves the electrochemical digital twin's potential for quantifying aging effects and serves as a basis for future physics‐informed machine learning to predict aging behavior and modes. [ABSTRACT FROM AUTHOR]

The key to advancing lithium‐ion battery (LIB) technology, particularly with respect to the optimization of cycling protocols, is to obtain comprehensive and in‐depth understanding of the dynamic electrochemical processes during battery operation. This work shows that pulse current (PC) charging substantially enhances the cycle stability of commercial LiNi0.5Mn0.3Co0.2O2 (NMC532)/graphite LIBs. Electrochemical diagnosis unveils that pulsed current effectively mitigates the rise of battery impedance and minimizes the loss of electrode materials. Operando and ex situ Raman and X‐ray absorption spectroscopy reveal the chemical and structural changes of the negative and positive electrode materials during PC and constant current (CC) charging. Specifically, Li‐ions are more uniformly intercalated into graphite and the Ni element of NMC532 achieves a higher energy state with less Ni─O bond length variation under PC charging. Besides, PC charging suppresses the electrolyte decomposition and continuous thickening of the solid‐electrolyte‐interphase (SEI) layer on graphite anode. These findings offer mechanistic insights into Li‐ion storage in graphite and NMC532 and, more importantly, the role of PC charging in enhancing the battery cycling stability, which will be beneficial for advancing the cycling protocols for future LIBs and beyond. [ABSTRACT FROM AUTHOR]

As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles to more dynamic profiles constructed to mimic real operation when ageing the Li-ion batteries. However, in most cases, when ageing the batteries, the same exact profile is just repeated until the battery reaches its end of life. Using data from batteries aged in this fashion to create a model, there is a very real possibility that the model will rely on the built-in repetitiveness of the profile. Therefore, this work will examine the dependence of the performance of a multiple linear regression on the number of charges used to train the model, and their location within the profile used to age the batteries. The investigation shows that it is possible to train models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only one randomly sampled partial charge is needed to achieve errors smaller than 1%. Lastly, as the number of randomly sampled partial charges used to train the model increases, the dependence on particular partial charges tends to decrease. [ABSTRACT FROM AUTHOR]