Comparative Optimization Of Model Parameters And Synchronous Online Prediction Of The State Of Charge Of Ternary Lithium-ion Batteries

As a new generation of clean energy,lithium-ion batteries are energy storage systems that are environmentally friendly with high economic benefits and broad application prospects.The accuracy and robustness in the estimation of the state of charge（SOC）of lithium-ion batteries directly affect the operating efficiency and safety of their discharge process.The accuracy and robustness of the SOC estimation of lithium-ion batteries directly affect the operating efficiency and safety of their discharge process.This thesis conducts a series of studies on ternary lithium-ion batteries for electric vehicles by improving traditional methods to ensure an accurate and robust SOC estimation.The research presented in this thesis is as follows:（1）By comparing the basic working principles of lithium-ion batteries with a self-designed fixed order charge-discharge（FOCD）experiment,the working characteristics of the voltage,current,and internal resistance at each specific charging state node are studied.The factors affecting the efficiency of batteries during charge-discharge cycles are determined.The characteristic temperature test is conducted to simulate the environment’s temperature variations to study the relationship between the parameters of the battery.The equivalent circuit model is constructed based on the results of the characteristics of the parameters of the battery.According to the circuit principle and the general characteristics of the ternary lithium-ion battery,the state-space equations of each model are established.The optimal solution expressed by the model is selected by considering the internal resistance effect,polarization effects,and self-discharge effect of the battery.（2）After analyzing and comparing the traditional equivalent circuit models,the Thevenin equivalent circuit model is selected and improved to characterize the dynamical working process of the battery.A bidirectional Thevenin equivalent circuit model is proposed to improve the model parameter expression under different working conditions.Also,by analyzing the advantages and disadvantages of the traditional recursive least squares method with a forgetting factor,a fading memory recursive least square（FMRLS）method with a self-iterating update based on data characteristics is established to simulate and test the equivalent circuit model.（3）A square root unscented Kalman filtering（SRUKF）algorithm based on the charging and discharging characteristics of ternary lithium-ion batteries is designed.A dual online system（DOS）is constructed with the FMRLS method,as DOS-SRUKF.By combining the characteristics of these algorithms,the error of model parameters from the transfer process is reduced,and the dual transfer optimization of parameters and error covariance matrix is established.The mutation estimation error caused by voltage and current fluctuations is highly reduced.The accuracy of SOC estimation of the proposed DOS-SRUKF is improved with high robustness.（4）The results of the proposed DOS-SRUKF for the SOC estimation are verified under the hybrid pulse power characterization（HPPC）and dynamic stress test（DST）working conditions.Also,the results are compared with the traditional UKF algorithm.By comparing the estimation effects,the results show that the DOS constructed with the FMRLS method for the online parameter identification and SOC estimation effectively reduces the hysteresis of the entire process.Also,it improves the accuracy and robustness of the SOC estimation of lithium-ion batteries.