| 纯电动客车荷电状态低故障预测 |
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| 引用本文： | 方卫东、陈汉林、陈子标.纯电动客车荷电状态低故障预测[J].福建工程学院学报,2020,0(4):353-357. |
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| 作者姓名： | 方卫东、陈汉林、陈子标 |
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| 作者单位： | 福建工程学院电子信息工程教研室 |
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| 摘 要： | 针对纯电动客车荷电状态(SOC)低故障预测问题，在分析其开始充电SOC序列的周期性波动规律及变化趋势的基础上，构建差分自回归滑动平均模型(ARIMA)进行车辆SOC的短期预测，最后比较预测结果与故障阈值以判断是否发生故障。以某车企的纯电动客车为例进行实证分析，研究结果表明:差分自回归滑动平均模型的故障预测真阳率为96.4%，误诊率为10.3%，说明方法具有良好的预测可行性。
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| 关 键 词： | SOC低 故障预测 ARIMA 纯电动客车 |
Research on fault prediction of low state of charge for pure electric buses |
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| FANG Weidong,CHEN Hanlin,CHEN Zibiao.Research on fault prediction of low state of charge for pure electric buses[J].Journal of Fujian University of Technology,2020,0(4):353-357. |
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| Authors: | FANG Weidong CHEN Hanlin CHEN Zibiao |
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| Affiliation: | School of Information Science and Engineering, Fujian University of Technology |
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| Abstract: | Aiming at the problem of fault prediction of the low state of charge (SOC) of electric buses, a differential autoregressive moving average model (ARIMA) was constructed for the short-term prediction of the SOC of pure electric buses. The construction of the model was based on the analysis of the periodic fluctuation law and change trend of the SOC sequence when the charge started. Finally, the prediction results were compared with the fault threshold value to determine whether a fault occurs. The pure electric bus from a certain automobile enterprise was taken for case analysis. Research results show that the true positive rate of the fault prediction of the differential autoregressive moving average model is 96.4%, and the misdiagnosis rate is 10.3%, which shows that the method has high prediction feasibility. |
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| Keywords: | low SOC fault prediction ARIMA pure electric bus |
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