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SOC Estimation of FOMIAUKF Algorithm Based on Fractional Order
Lithium-ion Battery Model
HAN Jianqiu, XING Likun, REN Hengqi, LI Xiaolong, WANG Baode
2024, 49(21):
18-24.
DOI: 10.16638/j.cnki.1671-7988.2024.021.004
Aiming at the problems of low accuracy of traditional Kalman filter algorithms in
estimating SOC of lithium-ion batteries and poor adaptability to different temperatures, the
fractional-order multi-innovations adaptive unscented Kalman filter (FOMIAUKF) algorithm is
proposed to estimate the SOC of lithium-ion batteries. Firstly, adaptive genetic algorithms are used
to identify the circuit model parameters under dynamic stress test conditions, and then a
comparative experiment of SOC estimation of FOMIAUKF, FOUKF, and MIUKF algorithms is
carried out in the U.S. federal city under operating conditions. Then, a comparison experiment of
SOC estimation of FOMIAUKF, FOUKF, and MIUKF algorithms is carried out under the operating
conditions of the U.S. federal city. The final results show that the FOMIAUKF algorithm has good
adaptability at 0, 25, 45 ℃ temperatures, and the average absolute errors of the estimated SOC are 1.66%, 0.27% and 0.25%, respectively, and the root-mean-square errors are 1.72%, 0.41% and
0.39%, respectively, which are the lowest among the three algorithms, which is of great
significance for the estimation of the SOC of lithium-ion batteries.
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