A multi-scale dual moving horizon estimation method for state of charge of lithium-ion battery

For the battery management system (BMS), accurate state of charge (SOC) estimation plays an important role in enhancing efficiency and ensuring safety of battery. Considering the tradeoff between accuracy of SOC and computational complexity, this paper proposes a multi-scale dual moving horizon estimation (MHE) algorithm, which incorporates a SOC estimator and a model parameters estimator using different time scales. Firstly, the state-space models of the SOC and parameters are built on basis of the equivalent circuit model and parametric sensitivity analysis, respectively. Secondly, the estimation problem of the model parameters is transformed into an optimization problem solved in a larger time scale used for the update of the SOC. Thirdly, the estimation problem of the SOC is solved in a smaller time scale used for the update of the model parameters. Finally, simulation results show that this method can effectively enhance accuracy and reduce computational burden compared with the methods such as MHE and dual EKF. The effect of the MHE window size on the SOC estimation is also discussed.