doi:10.16186/j.cnki.1673-9787.2022030035

Received:2022-03-14

Revised:2022-12-23

Online Date:2023-11-30

Estimation the state of charge of lithium battery based on variable forgetting factor and improved extend Kalman filter（Online）

ZHANG Tao, CHEN Dongming, HOU Pengpeng, WANG Yaobin

School of Electrical Engineering and Automation， Henan Polytechnic University， Jiaozuo 454000， Henan， China

Abstract:To figure out the problem of imprecise estimation of state of charge(SOC) of lithium-ion battery, this paper put forward estimate the SOC by the combination of the variable forgetting factor-recursive least squares(VFF-RLS) and the multi-information extended Kalman filter(MIEKF) algorithm. Primarily, VFF-RLS was used to identify model parameters, the ratio of the old and new data was varied in real-time on the basis of the estimation error. After, MIEKF algorithm was used to estimate SOC. integrated with multi-information identification theory, this method works by reusing error information to reduce the influence of individual necrosis data on the experiment. MIEKF algorithm was used to estimate SOC, and the error information was reused to reduce the influence of individual necrosis data on the experiment. Then setting up the experimental platform, selecting the appropriate working condition experiment according to the battery manual to, the platform was used to conduct experiments on the selected battery and calculate according to the set input current, compared with other algorithms. Test results show that under the complex conditions, compare with traditional algorithm, the improved algorithm of the apparent acceleration parameter identification, SOC estimation stage, the maximum error of the volatility is 0.533%, range decreased significantly, and the actual value error is bigger, still can fast convergence, improved algorithm is practical and feasible, and can be used in the actual battery research.

Key words:Lithium-ion battery;variable forgetting factor;state of charge;adaptive filtering;square root filtering

CLC：TM912