Fan Yunsheng, Huang Zhiwu, Li Heng, Yuan Wei, Yan Lisen, Liu Yongjie, Chen Zheng
School of Automation, Central South University, Changsha, 410075, China.
School of Electronic Information, Central South University, Changsha, 410075, China.
Sci Rep. 2025 May 27;15(1):18534. doi: 10.1038/s41598-025-03227-w.
The attainment of fault detection within battery packs is of paramount importance in ensuring the safe and reliable operation of electric vehicles. However, in the early stages of battery failure, its manifestations are often not obvious, making it difficult for conventional algorithms to detect them in time. Thus, this paper proposes a novel fault detection framework for battery packs to reduce detection time and eliminate false alarms. This framework employs a feature-augmented autoencoder that can effectively utilize the early-stage features for fault detection. The commonly used autoencoder is improved by applying a memory module to amplify the fault features between normal and abnormal cells and employing a similarity loss function to enhance the similarity of features for normal batteries. Then, the augmented features are input into the local outlier factor algorithm for battery fault detection. Real operational data from electric vehicles are used to train and test the autoencoder, covering the battery voltages, current, and temperature data. The results show that the proposed strategy can detect battery faults at most 10 hours earlier, and pinpoint the faulty battery without false alarms when compared to the existing methods.
在确保电动汽车安全可靠运行方面，实现电池组内的故障检测至关重要。然而，在电池故障的早期阶段，其表现往往并不明显，这使得传统算法难以及时检测到故障。因此，本文提出了一种用于电池组的新型故障检测框架，以减少检测时间并消除误报。该框架采用了一种特征增强自动编码器，能够有效利用早期阶段的特征进行故障检测。通过应用一个记忆模块来放大正常电池和异常电池之间的故障特征，并采用相似性损失函数来增强正常电池特征的相似性，对常用的自动编码器进行了改进。然后，将增强后的特征输入到局部离群因子算法中进行电池故障检测。利用电动汽车的实际运行数据对自动编码器进行训练和测试，这些数据涵盖了电池电压、电流和温度数据。结果表明，与现有方法相比，所提出的策略能够最多提前10小时检测到电池故障，并能准确找出故障电池且无误报。
