数据采集异常下的车用动力电池状态监测与故障诊断

States Monitoring and Fault Diagnosis of Vehicular Power Batteries Under Abnormal Data Acquisition

实时准确的状态监测对车载动力电池至关重要,其依赖于大量传感器采集的信息数据。在长期使用中,高频振动和连接器松动使局部传感器失效,导致数据采集异常。由于针对数据缺失和更新停滞异常的相关研究较少,该文提出一种基于双向长短期记忆网络和最小二乘支持向量回归的异常数据监测与校正方法。建模和参数辨识分别采用戴维宁模型和数据驱动方法,同时输入和状态估计算法用于电池状态估计。实验中,该方法在6种混合异常测试条件下的估计误差保持在5%左右,其有效性得到验证。

Real-time and accurate state monitoring is essential for vehicular power batteries,which depends on the information data collected by a large number of sensors.In long-term use,high-frequency vibration and loose connectors make local sensors fail,which leads to abnormal data collection.Considering the fact that researches on the data missing and stagnation of the data update are few,a method of monitoring and correcting abnormal data based on the bi-directional long short-term memory network and the least square support vector regression is proposed.Thevenin model is used in the modeling,and the recursive least square method is used in parameter identification.The simultaneous input and state estimation(SISE)algorithm is used in the battery states estimation.In the experiment,the estimation error of the proposed method is kept at about 5%under six cases of mixed abnormal test conditions,therefore it is proved to be effective.