Development And Application Of Remote Monitoring System For New Energy Vehicle

With the pursuit of low-carbon travel,new energy vehicles have become an important means of transportation on the road.Although the rapid development of automobile technology has improved the quality of new energy vehicles to a certain extent,at present,new energy vehicles still have problems such as low safety,lack of intelligence and low positioning accuracy.In response to these problems,this paper developed a new energy vehicle remote monitoring system.In addition to remotely monitoring vehicle driving data to ensure safe driving,the system can also support remote control of intelligent functions such as doors and lights.At the same time,to improve the accuracy of positioning new energy vehicles,the system uses a new positioning method by performing in-depth processing on the collected vehicle data.Firstly,this paper analyzes the functional requirements,specifications and communication protocols of remote monitoring terminals and platforms,and proposes a remote monitoring system implementation plan.According to the corresponding implementation scheme,the terminal hardware and software are developed,and the functions of vehicle positioning data collection,CAN bus data collection,4G remote data transmission,etc.are realized,and the vehicle data is received and saved through remote monitoring platform software development.Provide users with a visual web monitoring interface.Then,based on the low positioning accuracy and easy positioning failure of the remote monitoring terminal,based on the GNSS information,wheel speed,steering wheel angle and heading angular velocity data collected by the new energy vehicle remote monitoring system,a multi-data fusion positioning method is obtained.When the GNSS information received by the terminal is valid,the dual extended Kalman filter fusion is enabled to obtain the positioning data with high precision and used for BP neural network model for data training;when the GNSS information received by the positioning module fails,the well-trained BP neural network algorithm corrects the heading angular velocity value and realizes reliable positioning under GNSS information for a long time failure.Finally,through the real vehicle test,the results show that when the GNSS information of the terminal is valid,the GNSS information,wheel speed,steering wheel angle,and yaw rate data collected by the fusion terminal can effectively improve the positioning accuracy and accuracy of the vehicle;the GNSS information is invalid.By relying on wheel speed,steering wheel angle,and yaw rate data,new energy vehicles can be positioned for up to 15 minutes without GNSS information.