Research on state-of-charge estimation of lithium battery based on An improved adaptive unscented Kalman filtering

Abstract

The state-of-charge (SOC) of lithium-ion batteries is one of the important parameters for the operation and maintenance of the battery management system (BMS). Fast and accurate estimation Enables security control and monitoring on-time of lithium-ion batteries. It also helps to extend the service life of the battery and improve the safety of its use. The conventional unscented Kalman filter (UKF) algorithms have the risk of negatively determining the covariance matrix when estimating the SOC of lithium-ion batteries. To address the issue of poor estimation accuracy of the algorithm, this paper proposes improvement measures. In this article, lithium iron phosphate (Li-FePO 4 ) batteries are used as the object of study and modeling the second-order RC equivalent-circuit-model (ECM). The parameters of the equivalent model are identified by the recursive least squares method FFRLS. Combined with the adaptive adjustment strategy, the AUKF algorithm is formed to realize the SOC estimation of lithium-ion battery. And the simulation model is established by Matlab/Simulink to verify its accuracy. The test results indicate that the improved AUKF algorithm can better estimate the state-of-charge of batteries. The fluctuation limit of SOC estimation error is kept at 3%. It shows that the prediction method has high accuracy, good anti-interference ability and error correction ability.