Journal
JOURNAL OF ENERGY STORAGE
Volume 39, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.est.2021.102535
Keywords
State of charge (SOC); Power lithium-ion battery; Unsymmetrical Thevenin model; Auto-tuning multiple forgetting factors recursive least squares; Adaptive time scale dual extend Kalman filtering; Sliding window forgetting factor approximate total recursive least squares
Categories
Ask authors/readers for more resources
In this paper, an improved equivalent circuit model called the Unsymmetrical Thevenin model is introduced for more precise SOC estimation. Different methods, such as AMFFRLS, ATSDEKF, and SWFFATRLS, are proposed for model parameter identification and updating, leading to better SOC estimation results compared to traditional methods.
In this paper, we introduce the Unsymmetrical Thevenin model, an improved equivalent circuit model to obtain a more precise SOC estimation. We first propose an Auto-tuning Multiple Forgetting Factors Recursive Least Squares (AMFFRLS) for model parameter identification, then, we proposed an Adaptive Time Scale Dual Extend Kalman Filtering (ATSDEKF) to update the model parameters and Sliding Window Forgetting Factor Approximate Total Recursive Least Squares (SWFFATRLS) to update the maximum available capacity of a lithium-ion battery to obtain more accurate state of charge (SOC) estimation. Numerical experiments demonstrate that the proposed method can get better SOC estimation results compare to the traditional ones. Except for extreme temperatures, such as at 0 degrees C, the root mean square error (RMSE) of the Unsymmetrical Thevenin model is below 1.2%, which is much smaller than the most common Thevenin model with fixed parameters based on Extend Kalman Filtering (EKF).
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available