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 LiNi_0.5Co_0.2Mn_0.3O₂ 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 LAM_pos. 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.

Battery design is linked to real-world applications using a battery-design-aware machine learning framework built on a digital twin model, ultimately contributing to safer and longer-lasting next-generation batteries.