Study On Key Technologies Of Lithium Ion Battery Life Prediction

By analysis of oxygen The use of lithium-ion batteries plays an important role in our lives,such as mobile phones,computers,electric vehicles and electrical equipment.However,its own safety and reliability,is also the concern of many public and scholars in recent years.Therefore,the prediction of the remaining service life of lithium-ion batteries has become a hot research issue in the field of electronic system fault prediction and health management.The accurate prediction of the residual life of lithium-ion batteries and the expression of uncertainty of the results can greatly improve the reliability of the system.In order to accurately predict the remaining service life of lithium-ion batteries,this paper chooses the particle filter algorithm as the basis.Firstly,the composition and working principle of lithium-ion battery are introduced,and the failure mechanism of lithium-ion battery is analyzed.The residual life of lithium-ion battery is predicted by particle filter algorithm,which is characterized by the capacity attenuation of lithium-ion battery.Because the particle filter algorithm strongly depends on the model of lithium-ion battery and there is a lack of particles in the process of resampling,Therefore,an improved particle filter algorithm,unscented particle filter algorithm(UPF),is proposed to predict the residual cycle life of lithium-ion battery.on the premise of sacrificing part of the computational complexity,the particle degradation problem is solved,so as to improve the filtering accuracy.In view of the inevitable defects of a single algorithm,and in order to increase the diversification of particles,this paper proposes a hybrid method based on model and data-driven,that is,artificial immune particle filtering algorithm.Through the data-driven characteristics and the model of the research object,the prediction performance of the algorithm is improved by taking its respective advantages.The algorithm has the function of global diversity optimization.in this paper,this characteristic is used to improve the diversity of particlesubsets,select better particles from a large number of particles for lithium-ion battery residual life prediction analysis,improve the prediction accuracy.Finally,the uncertain and qualitative quantitative estimation of the results of life prediction is carried out,which provides decision-making reference information for the maintenance of lithium-ion battery system.