Lithium ion battery state estimation based on incremental extended Kalman filter

As energy storage systems become increasingly prevalent, lithium-ion batteries have gained significant importance in modern power grids. Accurate State of Charge (SOC) estimation is crucial for enhancing battery system reliability and prolonging operational lifespan. This study focuses on a 280Ah lithium iron phosphate (LiFePO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf>) battery and develops a second-order RC equivalent circuit model. To address the variance uncertainty issue in the conventional Extended Kalman Filter (EKF), an improved Incremental Extended Kalman Filter (IEKF) is introduced. This method can deal with the uncertainty of noise variance in traditional extended Kalman filter by dynamically adjusting the covariance of system noise and observation noise. Experimental validation under diverse operating conditions confirms the algorithm’s superior estimation accuracy.