## 1 Introduction
Reference [18] uses the mean squared error (MSE) between the experimental voltage and the simulated voltage from a first-order RC equivalent circuit model to describe the characteristics of battery voltages, determining whether there are voltage anomalies, and uses an improved Z-score method to quantitatively evaluate voltage faults. Reference [19] proposes a voltage abnormal detection method for electric vehicle batteries based on modified Shannon entropy and standard deviation, which can predict the exact times and locations of faulty batteries in battery packs ahead of time. Reference [20] uses the modified Shannon entropy to describe the voltage evolution of each battery, diagnoses faulty batteries by observing and analyzing the entropy values of individual batteries, and then evaluates the degree and level of failure through the Z-score and anomaly coefficient. Reference [21] proposes a hybrid fault diagnosis method that combines Multiscale Permutation Entropy (MPE) and the Coefficient of Variation (CV), using the 3\(\sigma\) principle as the threshold for battery failure. Statistical analysis approaches are suitable for univariate datasets, enabling the rapid and effective identification of outliers while also representing the informational characteristics contained in the data. However, interpreting outliers detected by these methods can be challenging, and data distributions, probability models, and parameters are hard to determine, constraining their use. Moreover, as the data dimensions and volumes increase, the efficiency of these anomaly detection approaches decreases. Robust Principal Component Analysis (RPCA) [22] is an unsupervised feature machine learning method that can effectively extract the main information from the observed data with sparse noise and severe outliers, successfully denoising the observed data. Conductingthe RPCA using the Alternating Direction Method of Multipliers (ADMM) can ensure calculation accuracy while achieving a faster computational speed [23]. In summary, to solve the problem of data quality affecting the accuracy of anomaly detection in battery cells, this article proposes a method for data denoising and anomaly detection in series-connected battery cells based on historical operation and maintenance data from a large domestic energy storage station. This method combines RPCA with mathematical statistical methods to achieve screening, identification, and consistency assessments of abnormal battery cells. Firstly, the RPCA is used for the extreme denoising of the voltage observation data, obtaining a baseline charging state curve for a battery cell consistency assessment, which also resolves the problem of the sensor output data being affected by random noise. To further detect and identify abnormal battery cells, the RPCA is used to extract outlier components. Based on the Average Deviation-3\(\sigma\) principle and utilizing Gaussian distribution probability characteristics, battery cells are conducted to screen and the serial numbers of the anomaly cells can be obtained. Finally, by replacing the calculation of the average deviation with variance, range, Euclidean distance, and signal energy, the effectiveness and accuracy of this anomaly detection method for battery cells are compared and verified. Therefore, this article is organized as follows: Section 2 introduces the source and describes the preprocessing of the data. Section 3 describes the process of the anomaly detection method for battery cells. Section 4 provides the experimental analysis and verifications of the proposed method. Finally, Section 5 presents the conclusion.