A Precise FCC Estimation Algorithm Based on Recursive Least-Squares Identification of Li-Ion Batteries with Adaptive Forgetting Factor Tuning

2016 ◽  
Keyword(s):  
Least Squares ◽  
Estimation Algorithm ◽  
Recursive Least Squares ◽  
Li Ion Batteries ◽  
Forgetting Factor ◽  
Li Ion ◽  
Adaptive Forgetting Factor ◽  
Adaptive Forgetting

scholarly journals Adaptive Forgetting-factor RLS-based Initialisation Per-tone Equalisation in Discrete Multitone Systems

1970 ◽  
Vol 5 (2) ◽  
pp. 107-117
Author(s):  
Suchada Sitjongsataporn
Keyword(s):  
Least Squares ◽  
Correlation Matrix ◽  
Recursive Least Squares ◽  
Square Root ◽  
Signal Flow Graph ◽  
Forgetting Factor ◽  
Discrete Multitone ◽  
Adaptive Forgetting Factor ◽  
Simulation Results ◽  
Adaptive Forgetting

An adaptive forgetting-factor inverse square-root recursive least squares (AF-iQRRLS) with inverse of correlation matrix updating is presented for per-tone equalisation in discrete multitone-based systems. The proposed inverse covariance update of the square-root covariance Kalman filter is introduced to prepare for the signal flow graph (SFG). This reduced derivation of adaptive inverse square-root recursive least squares algorithm can modify via SFG. In order to reduce the computational complexity, the forgetting-factor parameter for each group called per-group forgettingfactor (PGFF) approach based on AF-iQRRLS algorithm is introduced. The forgetting-factor from the middle of each group is selected as a representative in order to find an optimal forgetting-factor parameter by using AF-iQRRLS algorithm. After convergence, it is fixed for remaining tones of whole group. Simulation results reveal that the trajectories of modified PGFF of the proposed algorithm for each individual tone can converge to their own equilibria. Moreover, the performance of the proposed algorithms are improved as compared with the existing algorithm.

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

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1733
Author(s):  
Hao Wang ◽  
Yanping Zheng ◽  
Yang Yu
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Unscented Kalman Filter ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Forgetting Factor ◽  
Soc Estimation ◽  
Online Identification ◽  
Adaptive Forgetting Factor ◽  
Adaptive Forgetting

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.

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