State-of-Health Estimation And Remaining Useful Life Prediction Method Of Lithium-Ion Battery

Lithium-ion batteries are widely used in electric vehicles,communications equipment,aerospace and other fields because of their high energy density,long cycle life and high safety performance.As the core component of the system,battery degradation will affect the normal operation of the whole system,and even cause serious safety accidents and economic losses.State of Health(SOH)estimation and Remaining Useful Life(RUL)prediction of lithium-ion batteries can effectively predict the degradation degree of their performance,which is conducive to realizing condition-based maintenance and improving the reliability of the system,and has important research and practical value.In this paper,lithium-ion batteries are taken as the research object,and two core issues,SOH estimation and RUL prediction,are studied in depth.The main research contents are as follows:Firstly,lithium-ion batteries RUL prediction method based on empirical degradation model is given.Based on the analysis of degradation data of lithium-ion batteries,an empirical degradation model of battery capacity is established from the perspective of degradation rate to overcome the problems of poor universality,complex modeling and inadequate prediction accuracy of existing prediction models.The model can be applied to the same type of lithium-ion batteries under standard operating conditions.The lifetime curve of the batteries can be directly simulated by knowing the initial capacity of the batteries,with good practicability.Secondly,a fusion SOH estimation method for lithium-ion batteries based on empirical degradation model and error compensation model is established.Although the empirical degradation model can predict the global degradation trend of batteries,it can not accurately describe the differences and non-linear degradation phenomena in the degradation process of batteries.Based on the empirical degradation model and the error compensation model based on data-driven method,the degradation information which can not be described in the empirical degradation model is supplemented.The error compensation model comprehensively describes the impact of actual operation environments on battery degradation,and fundamentally improves the accuracy of SOH estimation in the process of lithium-ion battery life degradation.Finally,a method for predicting SOH full-life cycle degradation interval of lithium-ion batteries based on Monte Carlo theory is proposed.On the basis of the fusion SOH estimation method established in the previous article,combined with the Monte Carlo idea,a large number of working condition features are randomly generated within a certain working conditions to simulate the degradation characteristics of lithium-ion batteries under similar operating environments.Finally,the degradation interval of SOH is predicted under the full-life cycle of lithium-ion batteries,which has certain guiding significance for the health management of the same type of lithium-ion batteries.Based on the open data set of lithium-ion batteries provided by NASA PCoE Research Center,the proposed method is verified experimentally.The results show that the proposed method has good applicability and predictive performance.Figure [33] table [10] reference [88].