Fractional Order System Modeling And State Of Charge Estimation Of Lithium-Ion Battery

Due to the excessive dependence of fuel vehicles on fossil energy,fossil energy is nonrenewable energy,and the combustion of fossil energy will cause irreversible damage to the ecological environment.In recent years,pure electric vehicles have become the main development direction of the automobile industry in various countries because of their environmental friendliness.As the only energy source for pure electric vehicles,lithium-ion batteries and battery management systems have become the research hotspots of many scientists.Among them,the State of Charge(SOC)of lithium-ion batteries is the focus and basis of research on battery management systems.Because SOC cannot be directly measured by the instrument,it is necessary to model lithium-ion batteries and then use relevant algorithms to obtain them.Accurate estimation of SOC can ensure driving safety and extend the life of lithium-ion batteries.In order to accurately estimate SOC,the specific research content of this paper is as follows:1.The theory of fractional calculus is introduced,and by analyzing the electrochemical impedance spectrum of lithium-ion batteries,it is explained that the model of lithium-ion batteries is more suitable for fractional expression.This paper proposes to use the fractional order particle swarm optimization algorithm to identify the fractional order equivalent circuit model of lithiumion batteries through the Hybird Pulse Power Characterization(HPPC)test,and finally verifies the accuracy of the identified fractional order equivalent circuit model by open-circuit voltage test.2.According to the established fractional order equivalent circuit,an improved fractional order H∞ filtering algorithm is proposed,that is,a sliding mode observer is added on the basis of the original fractional order H∞ filtering algorithm,and the fractional order extended Kalman filter algorithm is compared through the urban Dynamomenter Driving Schedule(UDDS)and Dynamic Stress Test(DST)working conditions.The accuracy and robustness of the fractional order H ∞filtering algorithm and the improved fractional order H∞ filtering algorithm,experiments show that the improved fractional order H∞ filtering algorithm has high accuracy and strong robustness.3.Considering the high accuracy of unscented Kalman filtering in dealing with the problem of nonlinear systems,the improved fractional order unscented Kalman filtering algorithm is derived according to the fractional order equivalent circuit model,a sliding mode observer is added on the basis of the original fractional order unscented Kalman filtering algorithm,and the accuracy and robustness of the fractional order extended Kalman filter algorithm,fractional order unscented Kalman filter algorithm and improved fractional order unscented Kalman filter algorithm are compared under UDDS conditions and DST conditions.The results show that the improved fractional order unscented Kalman filtering algorithm has good accuracy and robustness.