Domestic Journals Hee-Sung Lim, Jin-Shik Yun, and Kyo-Beum Lee, “State estimation of LiFePO4 battery using a Linear Regression Analysis,” The transactions of The Korean Institute of Electrical Engineers, vol. 71, no. 2, pp. 366–372, Feb. 2022.
2022
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This paper proposes a SOH Estimation of
LiFePO4 battery management systems using a Linear Regression Analysis. Among
the methods of machine learning, supervised learning learns the relationship
between the input data (battery characteristic) and the output data (failure
data) to find a model that is expressed as a rule or function. Unsupervised
learning performs failure diagnosis and prediction by discovering patterns
inherent in changing battery characteristics data during use. The algorithm
estimates DCIR according to the input parameters using linear regression
analysis of supervised learning, and clustering of data to confirm association
with failure causes. The validity of the proposed machine learning algorithm is
verified by experiment.
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