Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

Hao Wang, Yanping Zheng and Yang Yu
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Hao Wang: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Yanping Zheng: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Yang Yu: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China

Mathematics, 2021, vol. 9, issue 15, 1-12

Abstract: In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

Keywords: adaptive forgetting factor; simulated annealing optimization; online identification; unscented Kalman filter (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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