Journal
APPLIED ENERGY
Volume 238, Issue -, Pages 423-434Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2019.01.057
Keywords
Battery management system; Building energy storage system; State of charge estimation; Model switching
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Funding
- National Natural Science Foundation of China [61433005]
- Guangdong scientific and technological project [2017B010120002]
- Swedish Energy Agency [39786-1]
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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-infinity 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.
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