Constrained Bayesian dual-filtering for state of charge estimation of lithium-ion batteries

Abstract

The state-of-charge estimation of lithium-ion batteries plays a key role in energy storage systems such as battery monitoring, fault detection, power and energy optimization control. However, it is technically challenging, in particular, for the simultaneous estimation of battery internal impedance and state-of-charge, which are two key state variables affecting battery performance. This paper reveals that the commonly used state-of-charge estimation schemes based on Bayesian filters are fundamentally flawed in taking state constraints into account. Constrained Bayesian dual filtering framework for parameter estimation and state-of-charge estimation are designed in this paper to improve the estimation accuracy and robustness. After a state-of-charge and open-circuit-voltage mapping is accurately identified, a dual-filtering framework is introduced to simultaneously estimate the state-of-charge and model parameters which gives rise to the dynamics. The inequality constraints of state variables in Bayesian dual-filtering framework are also taken into account. The state-of-charge and model parameter estimation results of the constrained dual-filtering are regarded as the mean of constraints. Extensive comparative experiments are conducted to validate that the proposed method is superior over existing methods in providing improved accuracy and robustness.