As discussed above, the faults diagnosis and abnormality of battery pack can be detected in real time. In addition, timely detection and positioning of faults and defects of cells can improve the health and safety of the whole battery pack.
In battery system fault diagnosis, finding a suitable extraction method of fault feature parameters is the basis for battery system fault diagnosis in real-vehicle operation conditions. At present, model-based fault diagnosis methods are still the hot spot of research.
In this paper, the state-of-the-art battery fault diagnosis methods are comprehensively reviewed. First, the degradation and fault mechanisms are analyzed and common abnormal behaviors are summarized. Then, the fault diagnosis methods are categorized into the statistical analysis-, model-, signal processing-, and data-driven methods.
For the voltage abnormality, an accurate detection and location algorithm of the abnormal cell voltage are attained by combining the data analysis method and the visualization technique. Firstly, the faulty or abnormal battery cells’ voltage is roughly identified and classified using the K-means clustering algorithm .
Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses. In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles.
Therefore, effective abnormality detection, timely fault diagnosis, and maintenance of LIBs are key to ensuring safe, efficient, and long-life system operation [14, 15]. Battery fault diagnosis can assess battery state of health based on measurable external characteristics, such as voltage and current [16, 17].