一种基于斯皮尔曼秩相关结合神经网络的电池组内部短路故障检测算法

电池组是电动汽车能源系统的重要组成部分，保障其安全性对电动汽车的智能化发展和人的生命财产都具有重要的意义，检测和保障能源系统中电池组的安全性已成为动力电池领域内的研究热点。神经网络被应用于电池组的各项数据检测中，但在电池组内部短路故障中基于相关系数等信号处理的方法仍广泛使用，其实现方案往往存在针对特定对象、需要特定环境、泛用性能较差等问题。基于此，该文融合相关系数和神经网络的特点，提出一种基于斯皮尔曼秩相关结合三通道卷积双向门控循环神经网络(TBi-GRU)的电池组内部短路故障检测算法。首先，基于斯皮尔曼秩相关系数，滑动窗口联合无量纲化，标准化多维度的电池组运行特征；接着利用提取的正常状态下电池组运行特征训练TBi-GRU神经网络；然后基于已训练好的TBi-GRU模型检测内部短路状态下的电池组运行特征，结合预测结果与各通道的动态阈值对电池组状况进行检测。通过理想条件的仿真分析与实际环境的平台验证，验证了该方法能够充分结合斯皮尔曼秩相关系数的鲁棒性强和TBi-GRU神经网络泛用性强的特点，识别出电池组的内部短路故障。

An Internal Short Circuit Fault Detecting of Battery Pack Based on Spearman Rank Correlation Combined with Neural Network

Battery pack is an important part of the energy system of electric vehicles. Ensuring its safety is of great significance to the intelligent development of electric vehicles and human life and property. Detecting and guaranteeing the safety of battery pack in the energy system has become a research hotspot in the field of power batteries. Neural network is widely used in battery data detection, but the signal processing method based on correlation coefficient is still widely used in battery short circuit fault, and its implementation scheme often has some problems, such as targeting specific objects, requiring specific environment, and poor performance in general use. Based on this, this paper combines the characteristics of correlation coefficient and neural network, a neural network fault detection algorithm for internal short circuit in battery packs based on Three-channel parallel Bidirectional Gating Recurrent Unit (TBi-GRU) is proposed. Firstly, based on Spearman's rank correlation coefficient, the sliding window is combined with dimensionless and standardized multi-dimensional battery pack operating characteristics. Then, the TBi-GRU neural network is trained by using the extracted operating characteristics of the battery in the normal state. Then, based on the trained TBi-GRU model, the operating characteristics of the battery packs under the internal short circuit state are detected, and the condition of the battery string is detected by combining the prediction results with the dynamic thresholds of each channel. Through simulation analysis of ideal conditions and platform verification of actual environment, it is proved that this method can fully combine the strong robustness of Szpilman's rank correlation coefficient and the strong universality of TBI-GRU neural network to identify accurately the battery pack's internal short circuit fault.