The Research Of Hybrid Electric Vehicles Fault Diagnosis Based Neural Networks

With the coming of the 21 centuries, mankind pays more attention to the environment of survival, the valid utilization of resources, the air pollution. For the economy energy , environmental protection , realizing the society can keep on developing, automobile industry have begun to research and develop the hybrid electric vehicles (HEV) actively in our country. For ensuring safety to HEV's the whole and the part system, it is urgent to set up a series of auto fault diagnosis system in the process of its developing and researching, in order to getting the conclusion to improve design or adopt the necessary measure to prevent the occurrence of the system accident, which need to research and apply the fault diagnosis technique.The auto fault diagnosis technique is a kind of synthesizes applied technique with regarding engineering mathematics , reliability theories , information theories as the foundation and regarding electronics technique , computer technique , artificial intelligence technique as the means. It has developed quickly in past decade. The some new theories and methods have produced, such as principle component analyses , genetic algorithm , wavelet theory , artificial neural network N fuzzy system , pattern classification adaptive control theory, nonlinear system theory, etc. Among them the research of the artificial neural network develops quickly, which make a new way for fault diagnoses.This paper analyses the development of fault diagnosis technique in domestic and international, the category and characteristics of auto fault , the method of auto fault diagnosis , the basic diagnosis process and theories method of the fault diagnosis system. According to characteristics of the neural network, it is indicated that the combination between the neural network and the fault diagnosis have possibility and inevitability. Basing on discussing the basic theories of neural network, it is defined that neural network can be applied for fault diagnosis in three ways.The BP network of forward type have the very strong mode identification and classific ability. Form appliance aspect this paper analyzes network layer number , nerve cell number of inside laye, original weigh , train speed, expecting error when designing BP network and puts forward the improvement method. The result based on the simulation of the auto fault diagnosis expert system example shows it has practicability and diagnosis result have veracity and reliability.There are much available information in auto fault diagnosis, only when the available and mulriple information is fused, can the precision and credibility be improved. Based on the analyses of single neural network characteristic, the model of integrated neural network is put forward in this paper. At the same time, the way to establish model, the principle to compose and the strategy to realize are given in this paper. The result based on the simulation of the auto engine fault diagnosis example shows it is a effective way and can acquire superior estimate and sentence of fault information.