基于分数阶模型和FOMICKF-FOUKF算法的锂电池SOC估计OA

Aiming at the problems that the offline parameter identification method can only identify a single fixed parameter which leads to insufficiently accurate state of charge(SOC)estimation,and that the traditional Kalman filtering algorithm is poorly adapted to different temperatures,a fractional-order multi-innovation cubature Kalman filtering combined with fractional-order unscented Kalman filtering(FOMICKF-FOUKF)algorithm is proposed to estimate the SOC of lithium-ion batteries.The algorithm adopts a multi-timescale approach to carry out the online parameter identification with the FOUKF in the macro time scale,and the SOC estimation with the FOMICKF in the micro time scale.The convergence ability of different algorithms varies,and in order to verify the convergence of the algorithms,the algorithms were subjected to the convergence comparison experiment.In addition,it is more difficult to accurately estimate battery SOC at different temperatures where the internal polarization reaction varies significantly.SOC estimation experiments were conducted at 0℃,25℃ and 45℃ to verify the adaptability of the proposed algorithm to different temperatures.The results show that the FOMICKF-FOUKF dual Kalman online joint algorithm estimates the SOC with high accuracy and good convergence,and has good adaptability to different temperatures.