Performance Prediction Of Lithium-ion Power Battery For Electric Vehicles

Environmental pollution and energy shortage are the major challenges facing the development of the automotive industry today.The development of traditional automobile is more and more polluted by environment and the price of petroleum is limited.The research and development of new energy vehicles is more and more important by the large automobile group.Pure electric vehicle(EV),as a vehicle powered only by electricity,has the characteristics of low noise,zero emission and simple structure.It is considered as one of the most promising new energy vehicles.As the main power source of electric vehicles,power battery determines the performance and service life of electric vehicles.At present,lithium ion battery has opened up a new era of environmental protection and energy saving for the automotive industry,but because of safety and service life of the battery energy storage system can not be fully guaranteed in technology,which restricts its wide application.In this paper,through experiments and simulation combined with related theories and algorithms,the factors affecting temperature rise,SOC prediction and fault diagnosis of single lithium power battery are studied.The research results have important practical significance and engineering value for the effective management and performance improvement of lithium-ion power battery energy storage system.The main work and innovations of this thesis are as follows:(1)Conducted a comprehensive evaluation and analysis of various factors by orthogonal design and fuzzy grey correlation analysis and fuzzy analytic hierarchy process of lithium battery temperature rise,provides a theoretical basis for effective thermal management and service life for the improvement of lithium battery.(2)Based on conventional method can estimate the defects of SOC unit in the battery on the proposed genetic algorithm by using the least squares support vector regression algorithm to unit cell SOC reservoir prediction can reduce the estimation error of the battery energy storage unit of SOC.(3)In order to improve the fault diagnosis accuracy of the lithium battery energy storage unit,the EEMD-AR method is used to extract fault features from the voltage signals of the lithium-ion battery energy storage unit.The lithium battery energy storage unit voltage signal is decomposed by EEMD,the decomposition of some relative time axis of symmetry of the IMF component,then the high frequency component of IMF1-IMF3 IMF AR model and extracted from the regression parameters and variance,fault characteristics of lithium battery storage unit can last for extraction.