Optimized Battery Modeling Using Adaptive Forgetting Factor in Recursive Least Squares Algorithm

Pursuant to the more critical modelling criteria for accuracy, setup effort, computationally complexities, and simplicity of adoption, the Equivalent Circuit Model (ECM) is the recommended option for online applications. Variable Forgetting Factor Recursive Least Square (VFFRLS) is the approach that...

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Vydáno v:2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 s. 1 - 6
Hlavní autoři: Chinta, Srikiran, Bhupathi, Hari Prasad, Yeditha, Swarna Kumari, Biradar, Vijayalaxmi, Suman, Sanjay Kumar, Bhagyalakshmi, L.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 09.04.2025
Témata:
Accuracy
ANN
Computational modeling
Convergence
Covariance matrices
DNN
ECM
Equivalent circuits
FFRLS
Filtering
Integrated circuit modeling
Kalman filters
Noise
RC-ECMs
SOC
State of charge
SVM
VFFRLS
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Shrnutí:Pursuant to the more critical modelling criteria for accuracy, setup effort, computationally complexities, and simplicity of adoption, the Equivalent Circuit Model (ECM) is the recommended option for online applications. Variable Forgetting Factor Recursive Least Square (VFFRLS) is the approach that is presented for recognising the model variables. This is due to the reality that the values of the model variables are variable. Not linear Kalman filtering approaches may be utilised for determining the state of charge (SOC) of a battery, and the VFFRLS approach that was proposed can input its recognised findings into ECM. On the other hand, the execution of the Kalman filter is highly dependent on the parameters of the procedure's noise covariance matrix (Q) and the noise level covariance matrices (R). The incorrect quantity for such vectors raises the estimate error while simultaneously lowering the pace of convergence rate. We use the trial-and-error approach to investigate the effect that such matrices have on the estimate of SOC in this study. To guarantee the reliability and fast convergence achieved by the filtering for the purpose of estimating the SOC of the lithium-ion battery, filtering modification is thus important. In terms of minimising mistakes in voltage estimating, the suggested VFFRLS approach outperforms the FFRLS method by a wide margin. When compared to FFRLS, VFFRLS obtains an accuracy that is about 90% higher, demonstrating how well it maintains accurate voltage estimate during the discharge procedure.
DOI:10.1109/OTCON65728.2025.11070505