Research On Modeling Of Power Lithium Battery For Echelon Utilization

With the development of new energy technologies,the production of lithium batteries has exploded.When the capacity of the lithium battery decays to about 80% of the factory value,it will be eliminated,and directly discarding it will cause waste of resources and environmental pollution.In order to realize the efficient use of retired lithium batteries,this paper has established a high-precision static and dynamic battery model through the research and analysis of retired lithium batteries(different working conditions).The model also considers the battery's temperature characteristics and SOC application characteristics,which lays a good theoretical foundation and a wide range of application prospects for the echelon utilization of retired lithium batteries.main tasks as follows:First,the working principle and performance parameters of retired lithium batteries are analyzed to clarify the operating characteristics of retired lithium batteries.After comparing the advantages and disadvantages of several battery equivalent circuit models,considering the huge influence of temperature on the retired lithium battery and the polarization reaction inside the battery,this paper establishes a multi-temperature second-order RC equivalent circuit model,which not only It can truly simulate the internal dynamic characteristics of the battery,and solves the problems of complicated calculation and low accuracy of most models.In order to verify the accuracy of the established retired lithium battery model,this article exponentially fits the battery voltage data,after calculating the identification results,the method of directly comparing the model parameters to verify the model.Experimental results show that the identification error does not exceed 1%,so the multi-temperature second-order RC equivalent circuit model established for retired lithium batteries in this paper is accurate and reasonable.Secondly,due to the influence of various factors in the actual working process of the lithium battery,the parameters continue to change,so this paper conducts an in-depth study on the online identification method that can realize dynamic identification.In this paper,the recursive least squares method is introduced into the forgotten factor for online identification of the battery model.This method strengthens the ability to obtain new data and solves the problem of recognition confusion caused by data accumulation in the recursive least squares method.In order to meet the requirements of the retired lithium battery in practical applications,this article puts the retired lithium battery at multiple temperatures to conduct static and dynamic working conditions experiments,and performsparameter identification on the experimental data.The experimental results show that at low temperatures,the maximum error of the two battery identifications is controlled at about 2%.As the temperature increases,the error gradually decreases,and the minimum error can reach about 0.3%.Therefore,this online identification method can accurately identify the model parameters of the retired lithium battery in actual work.At the same time,it summarizes the changing rules of retired lithium battery model parameters under different SOC and different temperatures,which lays a foundation for the application of retired lithium battery SOC.Finally,in order to solve the inaccuracy of the current single algorithm to estimate SOC,this paper uses the method of combined extended Kalman filter algorithm and recursive least squares method with forgetting factor to estimate the SOC of retired lithium battery.This method substitutes the SOC of the retired lithium battery into the extended Kalman filter state equation,and then uses the characteristics of the parameters that can be continuously corrected by the online identification algorithm to estimate the SOC value of the retired lithium battery under different working conditions.Accurate estimation of SOC.The experimental results show that,at low temperatures,the maximum SOC estimation error of the two lithium batteries does not exceed 2.3%.As the temperature increases,the error becomes smaller and smaller,and the minimum error is about 0.5%.To further verify the practicability of the joint algorithm,this paper identifies a custom DST random operating data.Experimental results show that the joint algorithm still has strong accuracy.Therefore,the joint algorithm used in this paper is accurate and feasible to estimate the SOC of retired batteries,which provides a reliable theoretical basis for the application of SOC in the echelon utilization of retired lithium batteries.In summary,this paper establishes a high-precision static and dynamic decommissioned lithium battery model at multiple temperatures,and uses an improved identification algorithm for parameter identification to improve the accuracy of identification,and then uses the fusion of the two algorithms to jointly estimate the retired lithium The SOC of the battery under different working conditions improves the estimation accuracy,and has good application prospects in the echelon utilization of retired lithium batteries.