The invention provides an automobile battery fault prediction method and device. The method comprises the following steps: collecting time sequence voltage sampling data and battery state sampling data; performing characteristic decomposition and characteristic selection on the time sequence voltage sampling data to obtain strong characterization characteristic data; constructing a feature data set according to the strong representation feature data and the battery state sampling data; constructing an original fault detection classification model based on a long short-term memory network; training the original fault detection classification model according to the feature data set to obtain a trained fault diagnosis classification model; acquiring a real-time battery voltage value of the to-be-diagnosed vehicle; predicting whether the battery state has a fault according to the real-time battery voltage value and a fault diagnosis classification model; and if yes, outputting battery fault early warning information. According to the method and the device, the battery fault can be quickly and accurately predicted in real time based on the battery voltage data, the dependence on the sensor precision and the environmental change is reduced, and the reliability and the stability of fault detection are improved.
本申请提供一种汽车电池故障预测方法及装置,该方法包括:收集时序电压采样数据和电池状态采样数据;对时序电压采样数据进行特征分解与特征选择,得到强表征特征数据;根据强表征特征数据和电池状态采样数据,构建特征数据集;基于长短期记忆网络构建原始故障检测分类模型;根据特征数据集对原始故障检测分类模型进行训练,得到训练好的故障诊断分类模型;采集待诊断车辆的实时电池电压值;根据实时电池电压值和故障诊断分类模型预测电池状态是否出现故障;如果是,则输出电池故障预警信息。该方法及装置能够基于电池电压数据快速准确地进行电池故障实时预测,降低了对传感器精度和环境变化的依赖性,提升了故障检测的可靠性和稳定性。
Automobile battery fault prediction method and device
一种汽车电池故障预测方法及装置
2024-11-26
Patent
Electronic Resource
Chinese
IPC: | G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen / G01R Messen elektrischer Größen , MEASURING ELECTRIC VARIABLES / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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