Electrochemical Modeling At Time Domain And Frequency Domain And Aging Diagnosis For The Lithium-Ion Power Battery

With the increase in the number of new energy vehicles,the safety accidents of new energy vehicles caused by the thermal runaway of lithium-ion power batteries have become increasingly prominent.According to the accident analysis,the thermal runaway of power batteries is closely related to their degradation in the working process.A variety of side reaction processes and failure forms will occur inside lithium-ion batteries in the aging process,resulting in an increase in the probability of their thermal runaway.Therefore,the aging state estimation and the aging diagnosis of lithium-ion power batteries are of great significance to ensure the safety of new energy vehicles.The establishment of battery models is necessary for the battery management of new energy vehicles.In order to meet the increasingly strict requirements of the battery safety management,it is also very important to establish an electrochemical model that has higher accuracy and is more capable of reflecting the internal mechanisms of batteries.Some problems are still exist in the current research on lithium-ion power battery,including the difficulty of electrochemical model to achieve closed-loop verification of battery external characteristics and internal electrochemical behavior,low efficiency of electrochemical model parameter identification,the inconsistency between battery aging state estimation and aging diagnosis methods and the difficulty of experimental results of aging diagnosis to correspond with their simulation results.Therefore,the work of this thesis is carried out from three aspects: the establishment of models,the identification of model parameters and aging applications of models.In order to solve the problem that the electrochemical model is difficult to achieve closed-loop verification of battery external characteristics and internal electrochemical behavior,an electrochemical modeling method based on the combination of time domain and frequency domain is proposed.Firstly,the model descriptions of double electric layer and passivation layer is introduced into P2 D model to form an extended P2 D model,which achieves the time-domain simulation and the frequency-domain simulation at the high frequency range.Then,in order to improve the simulation efficiency of impedance spectrum,the method of establishing the frequency-domain electrochemical model to directly simulate the impedance spectrum is proposed.The established frequency domain electrochemical models include full-cell electrochemical impedance model and single-electrode electrochemical impedance model.Then,combined with the idea of relaxation time analysis,a twin equivalent circuit model of frequency-domain electrochemical model is established to achieve the time-domain simulation using the frequency-domain model.In order to improve the efficiency of electrochemical model parameter identification,parameter identification methods based on the corresponding relationship between battery characteristics and electrochemical parameters are proposed.Firstly,parameters of electrochemical models are divided into three categories: size parameters,battery state parameters and kinetic parameters.Then,identification methods of kinetic parameters are mainly optimized.For the kinetic parameter identification of the time-domain model,a parameter pre-identification scheme based on voltage characteristics is proposed.For the kinetic parameter identification of the frequency-domain model,a parameter identification scheme based on impedance characteristics is proposed.In order to unify the aging state estimation and aging diagnosis of lithium-ion power battery,aging state estimation and aging diagnosis method based on electrochemical impedance spectroscopy are proposed.For the estimation of the battery aging state,the impedance of each electrochemical process inside the battery and their corresponding parameters are decoupled and extracted by the distribution of relaxation time method,and the estimation of the battery aging state is realized based on Gaussian process regression.For the aging diagnosis of batteries,changes of morphologies at the macro and micro levels and element composition of batteries at different aging stages are first diagnosed completely by methods of CT scanning,optical photograph observation,SEM observation and EDS scanning.Then,combined with the single-electrode electrochemical impedance model and its parameters,three battery internal behaviors which can cause rapid battery failure,including the deformation of battery winding structure,the loss of positive active material and the dissolution of positive active material,are diagnosed without disassembling the battery.The diagnosis results can also correspond to diagnosis results based on the experimental method.