电池安全问题是阻碍新能源汽车退役电池梯次再利用的关键因素，而电荷状态、电压和温度是判断电池安全状态的重要参数。基于此，提出基于实车数据的电池联合故障诊断。首先从实车数据平台获取数据，经过数据的预处理和螳螂算法优化K近邻（dung beetle optimizes K-nearest neighbor, DBO-KNN）算法进行特征提取，然后将提取的特征输入到建立的差分整合移动平均自回归（autoregressive integrated moving average model, ARIMA）故障诊断模型中，实现对电池单体的低压和过压的实时诊断和精准定位，最后通过电压、电池荷电状态（state of charge, SOC）和温度进行联合判断是否有触发热失控的风险，根据危险程度发出不同的报警等级。算例分析了故障特征提取准确率高达98.97%，不仅能精准定位单体发生异常的位置，还能提前9 s发生报警，有效预防了电池发生热失控的风险，验证了本文方法的有效性。实现了工程实际应用方面的较好效果，为未来动力电池梯次循环利用以及安全预警平台的研发奠定了基础。

Battery safety issue is a key factor that hindered the reuse of retired batteries on new energy vehicles, while charge state, voltage and temperature are important parameters to judge the safety state of batteries. Based on this, a joint fault diagnosis of battery based on real vehicle data was proposed. Firstly, the data was obtained from the real vehicle data platform, and the feature extraction was carried out by data preprocessing and dung beetle optimizes K-nearest neighbor (DPO-KNN) algorithm. Then, the extracted features were input into the established autoregressive integrated moving average model, autoregressive integrated moving average model (ARIMA) fault diagnosis model to realize real-time diagnosis and accurate location for low and overvoltage of battery cells. Finally, voltage, state of charge (SOC) and temperature were combined to determine whether there was a risk of triggering thermal runaway, and different alarm levels were issued according to the degree of danger. It can not only accurately locate the abnormal position of the monomer, but also alarm 9 s in advance, effectively preventing the risk of thermal runaway of the battery. It achieved a good effect in engineering practical application, and lied a foundation for the future step recycling of power batteries and the research and development of safety warning platform.