Accurately estimating state of charge (SoC) is very important to enable advanced management of lithium-ion batteries, however technical challenges mainly exist in the lack of a high-fidelity battery model whose parameters are sensitive to changes of the state and load condition. To address the problem, this paper explores and proposes a model switching estimation algorithm that online selects the most suitable model from its model library based on the relationship between load conditions for calibration and in practice. By leveraging a high-pass filter and the Coulomb counting, an event trigger procedure is developed to detect the estimation performance and then determine timely switching actions. This estimation algorithm is realized by adopting a gradient correction method for system identification and the unscented Kalman filter and H∞ observer for state estimation. Experimental results illustrate that the proposed algorithm is able to reproduce SoC trajectories under various operating profiles, with the root-mean-square errors bounded by 2.22%. The efficacy of this algorithm is further corroborated by comparing to single model-based estimators and two prevalent adaptive SoC estimators.
Bibliographical noteFunding Information:
This work of X. Tang, F. Gao, K. Yao, and W. Hu is supported in part by the National Natural Science Foundation of China project ( 61433005 ) and in part Guangdong scientific and technological project ( 2017B010120002 ). This work of C. Zou and T. Wik is supported by the Swedish Energy Agency under grant 39786-1 .
© 2019 Elsevier Ltd
- Battery management system
- Building energy storage system
- Model switching
- State of charge estimation