State of Charge Estimation of Lithium-ion Batteries Based on Latent Variable Strong Tracking Filtering
A state of charge(SOC)estimation method for lithium batteries based on latent variable strong tracking filtering algorithm(HV-STF)is proposed.This method sets the basic function terms of the nonlinear part as latent variables and combines the initial and latent variables of the system,thereby elevating the initial system model to a high-dimensional linear state model.In addition,the observation model is also transformed into a high-dimensional linear state observation model through equivalent rewriting.Through this approach,the latent variable strong tracking filtering algorithm can more accurately estimate the SOC value of lithium batteries.After experimental verification,this method has shown high accuracy in estimating the state of charge(SOC)of lithium batteries.Compared with Extended Kalman Filter(EKF),this method can more accurately estimate the SOC of lithium batteries,which is of great significance for improving the operational safety and performance of lithium batteries.
latent variablesstate of chargelithium batteriesKalman filtering