Abstract: It is widely acknowledged that lithium-ion batteries are extensively utilized in energy storage systems. Timely and accurate diagnosis of minor-faults in lithium-ion batteries is crucial to ensure the safe and stable operation of energy storage systems. However, minor-faults diagnosis is extremely challenging because of their inconspicuous early characteristics. The mainstream threshold method is unable to set reasonable thresholds, resulting in a low fault detection rate and a high false alarm rate. Therefore, a minor-faults hierarchical diagnosis method of lithium-ion batteries based on data driven is proposed. The diagnostic process is divided into two stages: first-level diagnosis and second-level diagnosis, which are specifically designed to achieve comprehensive detection and accurate identification of minor faults, respectively. First-level diagnosis: the sample entropy and correlation coefficients of the battery voltage sequence are calculated to quickly detect the minor-faults. Second-level diagnosis: fault characteristics accumulation method is proposed to accurately diagnose minor-faults, whose input is the sample entropy and correlation coefficients from the first-level diagnosis. The results indicate that the proposed method can diagnose internal short circuit, external short circuit, poor contact faults, with a fault detection rate of 96.3% and a false alarm rate of 2.3%.