A Novel State-of-Health Estimation for Lithium-Ion Battery via Unscented Kalman Filter and Improved Unscented Particle Filter
Feng Zhu, Jingqi Fu
Abstract
This paper aims to improve the rapidity and accuracy of the State of Health (SOH) assessment for lithium-ion battery. By integrating the Unscented Kalman Filter (UKF) and Improved Unscented Particle Filter (IUPF) algorithm, SOH can be effectively evaluated. The UKF algorithm is used to estimate the state of charge (SOC), the IUPF algorithm is employed to identify the ohmic internal resistance. The novelty of the proposed strategy relies on the 4-dimensional IUPF filter that is split into a 3-dimensional UKF filter and a 1-dimensional IUPF filter. Experimental results demonstrate that more accuracy and a faster rate of SOH estimation can be achieved via the UKFIUPF algorithm compared to the IUPF approach.
Topics & Concepts
Kalman filterUnscented transformExtended Kalman filterControl theory (sociology)Particle filterState of healthState of chargeComputer scienceEnsemble Kalman filterBattery (electricity)EngineeringArtificial intelligencePhysicsPower (physics)Control (management)Quantum mechanicsAdvanced Battery Technologies ResearchFault Detection and Control SystemsReliability and Maintenance Optimization