F. Maletić, J. Deur, I. Erceg

A Multi-timescale Kalman Filter-Based Estimator of Li-Ion Battery Parameters Including Adaptive Coupling of State-of-Charge and Capacity Estimation

IEEE Transactions on Control Systems Technology, Vol. 31, No. 2, pp. 692-706, 2022.
The paper deals with coupled, state and parameter estimation for lithium-ion batteries described by an equivalent circuit model including polarization dynamics. Since the model parameters depend on the battery state-of-charge and temperature operating point, as well as on the battery state-of-health, all states and parameters need to be estimated simultaneously for an accurate overall estimation during the battery lifetime. The proposed estimation algorithm is structured in two timescales: (i) slow-scale, Sigma-point Kalman filter-based estimation of battery capacity and (ii) fast-scale, Dual Extended Kalman filter-based estimation of state-of-charge and model parameters. A particular emphasis is on adaptive parameterization of state-of-charge and capacity estimators, which provides robust coupling between two timescales and ensures favorable convergence as well as robust capacity tracking in conditions of state-of-charge and model parameters estimation errors. In support of estimation accuracy analysis, an algebraic observability analysis of impedance parameters is conducted. Also, by introducing an observability index calculated in each simulation timestep, a comparison of degrees of observability of different impedance parameter subsets is allowed for. The proposed estimation algorithm is verified both by simulation and experimentally for an electric scooter Li-NMC battery pack.