A joint moving horizon strategy for state-of-charge estimation of lithium-ion batteries under combined measurement uncertainty

Measurement uncertainty is a common problem for state of charge (SOC) estimation of lithium-ion batteries in real applications. In this paper, to mitigate the negative effect of unseen measurement uncertainty, a joint moving horizon estimation (joint-MHE) approach is proposed. First, the equivalent circuit model (ECM) is constructed for battery modeling. Then, on the basis of ECM, the augmented state space model is formulated, in which the bias current is treated as an additional state and the measurement noises are summarized in covariance matrices. Finally, by integrating joint-MHE strategy with augmented model, the SOC estimation under measurement uncertainty condition is implemented. The effectiveness of proposed method is conducted under three uncertainty issues, including current bias, combined current uncertainty, and combined current and voltage uncertainty, and compared to the conventional MHE and the joint-extended Kalman filter (EKF) thoroughly. The results demonstrate that the joint strategy is an effective way to suppress the uncertainties in measurements. Furthermore, although two joint methods both can reduce the negative effect of unseen measurement uncertainty, the joint-MHE could provide better convergence speed and SOC estimation accuracy, and is much less sensitive to different uncertainty sources. Under the combined measurement uncertainty, the RMSE by joint-EKF is 5.32% during the whole applied DST range, while that by joint-MHE is only 1.46%. It thus indicates that the joint-MHE is a potential promising approach to tackle the measurement uncertainty problem, which would greatly assist in improving the feasibility of ECM-based SOC estimation approach in commercial BMS.

Automated summary

The study proposes a joint moving horizon estimation approach to tackle measurement uncertainty in lithium-ion battery SOC estimation. Results demonstrate that the joint-MHE method effectively suppresses uncertainties in measurements, improving SOC estimation accuracy in the presence of various uncertainty sources.