A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window,Energy Science & Engineering

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A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window
Energy Science & Engineering ( IF 3.5 ) Pub Date : 2023-10-13 , DOI: 10.1002/ese3.1593
Xianfu Cheng ₁ , Xiaojing Li ₁ , Xiaodong Ma ₁

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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.

中文翻译：

孤立森林算法与滑动窗口相结合的电池故障诊断与预警方法

汽车的动力电池是由大量的电芯串联或并联而成。由于制造工艺误差和使用环境的不同，电芯之间存在差异，电池组会出现不一致的问题，从而增加安全隐患。因此，对动力电池的不一致性进行识别和预警具有重要的现实意义。基于车联网平台的数据，提出一种改进的孤立林动力电池异常单体识别与预警方法，利用滑动窗口（SW）对数据集进行分割，实时更新诊断模型的数据。 -时间。对正常电芯和异常电芯的评分进行分析，确定故障阈值为0.75。结果表明，该算法的查全率和查准率分别为0.91和0.95，比其他方法更适合不一致的电池单体识别。如果SW大小为15，则预警效果最好。在车辆报警发生之前，该算法可以实现早期故障预警，从而有效避免不一致故障带来的安全问题。

更新日期：2023-10-13

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