Design Of Remote Monitoring System And Study On Fault Diagnosis Method For Electric Vehicle

As deteriorating environment, rising oil price, traditional fuel vehicle uses cost more and more high, pure electric vehicle is to be more rising expectation. On the other hand, with strong support of our country policy in this area, the whole country appears an electric vehicle boom. The governments continuously enlarge investment of EV realm, the key technique of EV is more and more mature, and many electric vehicles have already entered to the stage of demonstration runs. However, from the hole, our EV industry is still in the initial period, it's difficult to guarantee the quality of products. Therefore, it is very necessary for data collection and quality tracking for EV. We design a wireless remote monitoring and fault diagnosis system for EV. It not only makes real-time tracking product quality for EV manufacturer, and also has instruction significance for management and maintenance of EV, and there is a large market value.Wireless remote monitoring system is the trend in all kinds of industry monitoring systems. Currently it has a large number of applications in various fields. As EV is just in the initial period, it's difficult to guarantee the quality of products. Consequently we have to carry on real-time monitoring to vehicles. This paper absorbs experience from the monitoring system of other realms, and adds remote monitoring system on CA6730URE1 electric vehicles which made by Jilin High-tech Electric Vehicle Co., Ltd.Electric vehicle is powered by batteries, so the proper use and maintenance of batteries is the most important part of the vehicle. Closure of batteries, complexity of using conditions and environment often makes maintenance personnel can not determine the battery fault type and the specific reasons. In this paper, based on the wireless remote monitoring system, we did a study on the battery fault diagnosis method for EV, and tested the method by bench and vehicle verification.Main contents and conclusions of this paper are as follows:1. Communication and the overall structural design of remote monitoring system. We have chosen the communication——GPRS for the system by comparing the advantages and disadvantages of communication in various fields. Taking the actual environment and working conditions of electric vehicle which are more complex into account, we use DTU as on-site monitoring module. Therefore, the hardware establishment of remote monitoring has completed. We draw the remote monitoring system topology. All of them lay a solid foundation for the construction of remote monitoring system.2. Software design of remote monitoring system. We described design requirements of the software system to clear design direction before software development. This system uses a C/S model. The GPRS access mode of control center is fixed IP for public network. This mode ensures the stability of data transmission and storage. The development of this system is based on Kingview 6.53 which is industrial control software. The computing power of Kingview is weak, so we joined the joint design with MATLAB when developing. It greatly improves the system's computing power, and provides an important basis for the study on fault diagnosis method.3. The characteristics and failure analysis of lithium-ion power batteries. This paper starts from the lithium-ion batteries works and charge-discharge experiments, details the charge and discharge characteristics and main faults of lithium-ion batteries. In addition, this paper analyzed the battery failure. All of them provide a theoretical and practical basis for the study on batteries fault diagnosis method.4. Study on fault diagnosis method of power batteries. This is the core of the paper. This method is based on fuzzy mathematics, and use fuzzy comprehensive evaluation to build fuzzy diagnosis matrix. This fault diagnosis system develops according to expert system development process. Moreover, we added the function of artificial fault troubleshooting in the inference part, so that the system has more accurate data base, and it improve the accuracy of fault diagnosis. Finally, the method was tested by bench and vehicle verification. They demonstrate that the method can be implemented.