Research On Joint Estimation Algorithm Of Lithium-ion Batteries SOC And SOH

With the development of the automobile industry,the shortage of non-renewable energy sources such as coal and oil,as well as social problems such as climate and environment have become increasingly prominent.The vigorous development of new energy vehicles is an important way to effectively alleviate and improve the above problems.As the most important part of new energy vehicle products at this stage,lithium-ion batteries have developed an optimized battery joint estimation algorithm to improve the battery estimation accuracy,which has theoretical research on further improving the performance of electric vehicles and promoting the industrialization of new energy vehicles.Significance and engineering application value.Relying on a school-enterprise cooperation project,this paper takes the vehicle lithium-ion battery as the research object to estimate the state,and designs an optimization algorithm to ensure that the state of the battery can still be accurately estimated under different conditions.Based on the battery charging and discharging experiments,an equivalent circuit model of the battery was built,and the dual extended Kalman filter algorithm was used to realize the joint estimation research of the state of charge（SOC）and the state of health（SOH）of the battery.Since the algorithm idealizes the derivation process,it will bring many errors.Therefore,an improved joint algorithm based on the particle filter algorithm is proposed to estimate the battery state.The research results show that the algorithm proposed in this paper can effectively improve the accuracy of battery state of charge estimation and state of health estimation.The specific research contents are as follows:（1）Firstly,the development situation and research status of new energy vehicles and batteries at home and abroad are analyzed and summarized.The components and working principles of lithium-ion batteries are introduced.With the help of the existing equipment in the laboratory,lithium-ion batteries are selected,a battery testing experimental platform is built,a series of experiments are designed and completed,and the performance characteristics of lithium-ion batteries are analyzed.（2）Next,analyze the most commonly used battery models at home and abroad,and finally choose the second-order RC equivalent circuit model as the research object of this paper while taking into account the complexity and accuracy.Combined with the experimental data,the internal parameters of the battery model were identified offline and online respectively,and the equivalent circuit model was built and verified based on Matlab/Simulink software.The accuracy of offline identification.is better than online identification.（3）Then estimate the battery state,analyze the principle of Kalman filter,establish the state equation and output equation of the battery system based on the extended Kalman filter algorithm,and realize the estimation of the battery SOC,but the parameters of the algorithm can only take fixed values,so on this basis,establish a double extended Kalman filter algorithm to realize the alternate update between the currently estimated model parameter values q and the battery SOC,and then the joint estimation of the battery SOC and SOH is realized through the calculation formula of SOH.The results show that the accuracy of the algorithm estimates is higher than the extended Kalman filter algorithm.（4）Finally,use the particle filter algorithm and the unscented Kalman particle filter algorithm to estimate the battery SOC,and compare with the estimation results in the previous chapter.The disadvantage of this kind of algorithm is that the internal parameters of the battery model cannot be estimated online.Therefore,in order to improve the estimation accuracy,it is proposed to estimate the internal parameters of the battery online based on the unscented Kalman particle filter algorithm and combine the unscented Kalman filter algorithm to form an improved method.The algorithm realizes the joint estimation of battery SOC and SOH.The estimation results show that the accuracy of the proposed improved algorithm is higher than that of the double extended Kalman filter algorithm and the unscented Kalman particle filter algorithm.At the end of the paper,the robustness of the battery is verified under the unknown initial SOC and different temperatures to ensure the applicability of the algorithm under different conditions.