Joint State of Charge and State of Health Estimation of Lithium-ion Battery Using Improved Adaptive Dual Extended Kalman Filter Based on Piecewise Forgetting Factor Recursive Least Squares

Abstract

This work aims to improve the accuracy of state of charge estimation for lithium-ion battery, as well as to accurately estimate state of health. This study presents a piecewise forgetting factor recursive least squares method based on integral separation with a second-order resistor-capacitor model and uses a novel adaptive filter based on error covariance correction on the conventional dual extended Kalman filter. The experiments show that the error of SOC estimation is less than 0.61% and the error of SOH is less than 0.09% under different complex conditions, the proposed method can effectively improve the estimation accuracy and robustness.