Applied comparative study of existing methods for state-of-charge estimation in lithium-ion batteries
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Abstract
Estimating the state of charge (SOC) of lithium-ion batteries is crucial for the operation of various electrical and electronic devices and equipment. This work presents the implementation of models based on a Bayesian approach using linearized Kalman filtering and particle filtering (PF) to estimate the SOC in lithium-ion batteries. state equation of the Bayesian models incorporates battery resistance as an artificial evolution parameter. Two models based on machine learning algorithms, random forest and K-nearest neighbors (KNN), are also implemented by fitting the parameters of an equivalent electric circuit model to electrochemical impedance spectroscopy measurements. A cylindrical LCO 26650 cell was employed in this study. The results show high performance in SOC estimation for the Bayesian filters, with PF exhibiting the best metrics, including an R2 adjustment factor of 0.9968.
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