新能源汽车故障诊断系统设计与优化OA

In response to the problem of fault diagnosis in the operation of new energy vehicles,this paper collects real-time vehicle operation data based on the on-board CAN bus.After completing signal preprocessing and feature extrac-tion,a multi-source data fusion model is constructed,and combined with convolutional neural networks(CNN)and long short-term memory networks(LSTM),effective recognition of complex temporal features is achieved.The simula-tion analysis and real vehicle verification results show that the designed diagnostic system exhibits high recognition ability in various typical fault scenarios such as overheating of power batteries,insulation faults,and abnormal vibration of drive motors,with an overall accuracy rate of 96.8%.The research results indicate that the system can provide reliable tech-nical support for the intelligent operation and security of new energy vehicles,and also lay the foundation for future re-search on remote fault warning and health management based on cloud platforms.