Research On Life Modeling Of Lithium Ion Batteries With High Specific Energy

Power lithium ion battery is an important part of electric vehicle.Its performance directly affects whether the electric vehicle can run safely.The research of this paper is based on the two projects of "Research on the Life Performance of Power Batteries" and"Research on the Life Testing and Modeling of Power Batteries".Two types of lithium-ion batteries with rated capacity of 36Ah and 114Ah are studied.Firstly,the temperature stress,discharge rate stress and coupling stress experiments were designed by orthogonal experiment method,and HPPC test was carried out during the experiment to obtain the characteristic parameters of the battery.Based on the experimental data.,the effects of temperature and discharge rate on the capacity attenuation of batteries were analyzed.Secondly,an empirical model of battery life based on data fitting is established.The attenuation mechanism of batteries at different temperatures was analyzed.It was found that the attenuation mechanism of batteries at low temperatures was different from that at normal and high temperatures.In this paper,only the life models of batteries at normal and high temperatures were established,and the mechanism analysis was emphasized at low temperatures.The life model of single stress battery was established:the life model of temperature stress battery based on modified Arrhenius equation and the life model of discharge rate battery based on inverse power law theory.The idea of life modeling of stress cell with variable temperature was put forward.Thirdly,a data-driven battery life prediction method is established,and the support vector machine machine algorithm is used to establish the battery life prediction model.The established battery life prediction method can effectively predict the battery life before the turning point of battery recession occurs,and the prediction accuracy is less than 9%.In order to avoid the local optimal solution,the grid search method based on cross validation is applied to optimize the model parameters in the process of modeling.However,the method can not predict the battery life after the turning point of recession effectively.In order to solve this problem,the relationship between DC internal resistance and capacity degradation was studied.The DC internal resistance of batteries under different working conditions was tested,and the relationship between the change of DC internal resistance and capacity decline was proposed.That is,the inflection point of increasing DC internal resistance basically corresponds to the inflection point of capacity decline,which can be used as the characteristic parameter of battery health diagnosis,and the problem that the inflection point of capacity decline can not be effectively predicted in data-driven battery life prediction method was solved.Finally,the battery life prediction method based on the fusion model is established,and the battery life prediction model is established by using particle filter algorithm.In order to solve the problem that the computational load of importance sampling increases with time,sequential importance sampling algorithm is applied;resampling technology is applied to solve the problem of particle degradation during the implementation of the algorithm;double exponential model is used for battery degradation model,and the prediction accuracy of battery life is less than 2%based on particle filter algorithm.This method can not only effectively predict the data after the inflection point of battery capacity decline,but also greatly improve the prediction accuracy compared with the data-driven battery life prediction method.