A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window

Abstract

Abstract The vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults.

Keywords

• fault diagnosis
• isolation forest algorithm
• power battery
• sliding windows