Design And Optimization Of State Of Energy Estimation Algorithm For Power Lithium-ion Batteries

With the introduction of the "double carbon" concept,the pace of development of China’s new energy industry is accelerating,and the application of power lithium-ion batteries is becoming more and more extensive.The state of energy(SOE)is an important basis for system energy scheduling and one of the most critical parameters to be estimated by the Battery Management System(BMS).To accurately estimate the SOE,the following work has been carried out.(1)The open circuit voltage(OCV),Hybrid Pulse Power Characteristic(HPPC),and charge-discharge experiments are designed under various temperatures and charge-discharge magnification conditions.This makes it possible to obtain the characteristics of the battery under different temperatures and charge-discharge magnification conditions to achieve highprecision lithium-ion battery equivalent modeling.(2)Considering the obvious difference in battery open circuit voltage at different temperatures,an improved second-order RC equivalent circuit model with temperature consideration is built to accurately describe the power lithium-ion battery’s characteristics.In view of the "data saturation" phenomenon of the traditional recursive least squares method,the forgetting factor recursive least square method’s parameter identification model is built.The forgetting factor weakens the influence of the "old data" to improve the ability of the algorithm to obtain information from the "new data",thus improving the accuracy of the equivalent battery model and laying a foundation for the accurate estimation of the battery SOE.(3)To solve the problems of slow convergence speed and low robustness of CKF algorithm in SOE estimation under complex operating conditions,a fuzzy adaptive variable window multiple innovation optimization model is constructed to achieve real-time and highprecision SOE estimation.The fuzzy adaptive algorithm is designed to detect the convergence of SOE estimation results based on covariance matching criteria in real time,to address the slow convergence rate of the traditional cubature Kalman filter algorithm.The convergence rate of the traditional cubature Kalman filter algorithm is improved by dynamically adjusting the covariance of the observation noise.The standard cubature Kalman filter algorithm’s drawback of employing a single innovation for a posteriori update is addressed by integrating the double weight multiple innovation theory.The variable window adaptive adjustment technique allows for a dynamic change in innovation length,which enhances the traditional algorithm’s robustness and SOE’s estimation accuracy.(4)Given the low accuracy of SOE estimation under aging conditions,a joint estimation model of maximum available energy and SOE is constructed to correct the maximum available energy of the battery in real time.A two-layer filter is constructed based on the improved cubature Kalman filter algorithm to achieve the joint estimation of SOE and maximum available energy,which increases the estimation accuracy of SOE.The mutual correction of the SOE and the battery’s maximum available energy are realized through the estimation results of each filter,and the accurate estimation target of the power lithium-ion battery SOE under aging conditions is attained.(5)The experimental settings at various temperatures are designed to complete the experimental verification of the algorithm in this study to confirm the effect of the algorithm on SOE estimation.The experimental data are obtained under different temperature conditions;the improved and traditional algorithms are compared and analyzed under different conditions.The verification results show that the fuzzy adaptive algorithm can effectively improve the convergence speed of the cubature Kalman filter algorithm.After optimizing the variable window double weight multi-innovation theory,the accuracy of SOE estimation is significantly improved,and the proposed method has higher robustness.In the aging state,the joint estimation of SOE and maximum available energy with a two-layer filter can improve the SOE estimation effect of the traditional cubature Kalman filter algorithm.The experimental verification results indicate that the fuzzy adaptive variable-window dual-weight multi-innovation cubature Kalman filter algorithm can provide results for SOE estimation of power lithium-ion batteries with high precision and resilience.Moreover,the joint estimation of SOE and maximum available energy by the double-layer filter has a noticeable optimization effect on SOE estimation results and can achieve accurate estimation of SOE under battery aging conditions.In this study,the research results provide a theoretical basis for the safe application of power lithium-ion batteries and the efficient operation of the battery management system.