scholarly journals A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1358 ◽  
Author(s):  
Zizhou Lao ◽  
Bizhong Xia ◽  
Wei Wang ◽  
Wei Sun ◽  
Yongzhi Lai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Recursive Least Squares ◽  
Forgetting Factor ◽  
Novel Method ◽  
Variable Forgetting Factor

scholarly journals A Battery SOC Estimation Method Based on AFFRLS-EKF

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5698
Author(s):  
Ming Li ◽  
Yingjie Zhang ◽  
Zuolei Hu ◽  
Ying Zhang ◽  
Jing Zhang
Keyword(s):  
Least Squares ◽  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
Recursive Least Squares ◽  
Estimation Accuracy ◽  
Forgetting Factor ◽  
Battery Charge ◽  
Estimation Strategy ◽  
Soc Estimation

The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and it is tough to model it accurately, which leads to the estimation deviation of the battery SOC. Recursive least squares (RLS) algorithm with fixed forgetting factor is widely used in parameter identification, but it lacks sufficient robustness and accuracy when battery charge and discharge conditions change suddenly. In this paper, we proposed an adaptive forgetting factor regression least-squares–extended Kalman filter (AFFRLS–EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions. The simulation results show that the SOC estimation strategy of the AFFRLS–EKF based on accurate modeling can effectively improve the estimation accuracy of SOC.

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