Research on state-of-charge estimation of lithium-ion batteries based on an improved gas-liquid dynamics model

Abstract

Improving the accuracy of state of charge (SOC) is beneficial for the safety and service life of batteries. The battery analysis model has become one of the research hotspots due to its clear physical significance and visibility of parameters. Based on analytical modeling methods, an improved gas-liquid dynamics (GLD) model of the lithium-ion battery (LIB) is proposed, which excels in simulating lithium-ion diffusion behavior and terminal voltage rebound characteristics under the open circuit state of LIBs. The parameter mapping relationships between the LIB and GLD model are improved and estimation equations of SOC, terminal voltage, and open circuit voltage are deduced according to GLD state equations. Based on these estimation equations and dual extended Kalman filter, an efficient real-time SOC estimation method for LIBs is developed. The performance of SOC estimation method is verified by LIBs data sampled by onboard BMS under five working conditions. Results show that the proposed method has strong robustness of the initial error of SOC = ±20 % which is eliminated within 15 s, and has a reliable performance of SOC estimation against the current and terminal voltage noises.