Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator

The modeling and state-of-charge estimation of the batteries and ultracapacitors are crucial to the battery/ultracapacitor hybrid energy storage system. In recent years, the model based state estimators are welcomed widely, since they can adjust the gain according to the error between the model predictions and measurements timely. In most of the existing algorithms, the model parameters are either configured by theoretical values or identified off-line without adaption. But in fact, the model parameters always change continuously with loading wave or self-aging, and the lack of adaption will reduce the estimation accuracy significantly. To overcome this drawback, a novel co-estimator is proposed to estimate the model parameters and state-of-charge simultaneously. The extended Kalman filter is employed for parameter updating. To reduce the convergence time, the recursive least square algorithm and the off-line identification method are used to provide initial values with small deviation. The unscented Kalman filter is employed for the state-of-charge estimation. Because the unscented Kalman filter takes not only the measurement uncertainties but also the process uncertainties into account, it is robust to the noise. Experiments are executed to explore the robustness, stability and precision of the proposed method. (C) 2017 Elsevier Ltd. All rights reserved.