Online state of charge estimation of lithium-ion batteries: A moving horizon estimation approach

- A battery state-space model based MHE approach is proposed to SOC estimation.
- The convergence to true SOC of MHE is much faster than that of EKF.
- MHE is less sensitive to the poor initial SOC guess than EKF.
- MHE is a potential promising accurate and reliable approach for SOC estimation.

Online state of charge (SOC) estimation of lithium-ion batteries (LIBs) relies not only on accurate battery model but also on effective state estimation method. In this study, a nonlinear battery state-space model based moving horizon estimation (MHE) approach is proposed to estimate SOC within the full range. The relationship between SOC and circuit parameters in battery model is captured by polynomial functions. The essential arrival cost in the MHE problem formulation is approximated by the filtering scheme and its covariance matrix is updated by extended Kalman filter (EKF) method. Hybrid pulse power characterization test is first used to guide battery model construction and tuning parameters determination in MHE. The constant current discharge test and dynamic stress test are then used to validate the applicability of the MHE and investigate the performance comparisons between MHE and EKF. The results demonstrate that compared to the EKF, the MHE is less sensitive to the poor initial SOC guesses and has faster convergence to the true SOC. The results thus validate that the MHE provides a potential promising approach to perform accurate, reliable and robust SOC estimation of LIBs.