Pure Electric Vehicles Lithium Battery Research Of Fault Diagnosis System

At present, the rapid development of social economy make our country energy shortage, environment pollution and so on. In terms of new energy vehicles, pure electric vehicles as to its environmental pollution is small, higher energy efficiency advantages received widespread attention and application. In the development of electric vehicles, the key part is the battery and battery management system. Lithium-ion batteries for its light quality, small size, density, green environmental protection, and a series of advantages are widely used in pure electric vehicles. In this paper, in order to improve the safety and reliability of battery management system, design the hardware and software of battery management, and studied the fault diagnosis system. Fault diagnosis is the core and the most important part in battery management technology. Fault diagnosis technology is a new technology developed in recent years, through the diagnosis, the system can realize the prediction of the breakdown and hidden trouble to the battery, can timely detect and isolate the battery failure, the maintenance work to a minimum, thus to ensure the safe and reliable operation of the electric car.In this paper, starting from the basic features and working principle of lithium-ion battery, analysis of lithium battery will be the main faults, and introduces the lithium batteries battery parameters of battery management system. The application of battery management system CAN effectively improve the battery in the use of safety, efficiency and life problem, used for data communication of CAN network system and the PC data acquisition software of data reception. Fault diagnosis model is established, and put forward the fault diagnosis expert system based on fuzzy neural network method. Fault membership degree is the key to solving the battery fault diagnosis, this paper use a fuzzy neural network fault diagnosis expert system, the method of determining the symptoms of membership degree to solve the fault membership degree, fault diagnosis rules determine the battery, the battery failure prediction and the judgment. Based on Yuan Zheng SEV400 energy battery management system, a large number of research and experimentation, build battery history database, and the experimental data input to the fault diagnosis model, the experimental results are consistent, proved the feasibility of the system.Finally, the experimental results show that the system of the diagnosis results are basically in line with the actual, basic requirements for fault prediction of diagnosis.