Accuracy versus simplicity in online battery model identification

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dc.contributor.author Fotouhi, Abbas
dc.contributor.author Auger, Daniel J.
dc.contributor.author Propp, Karsten
dc.contributor.author Longo, Stefano
dc.date.accessioned 2016-11-02T15:38:57Z
dc.date.available 2016-11-02T15:38:57Z
dc.date.issued 2016-09-22
dc.identifier.citation Fotouhi A, Auger DJ, Propp K, Longo S, Accuracy versus simplicity in online battery model identification, IEEE Transactions on Systems Man and Cybernetics: Systems, Volume 48, Issue 2, 2016, pp.195-206 en_UK
dc.identifier.issn 2168-2216
dc.identifier.uri http//dx.doi.org/10.1109/TSMC.2016.2599281
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/10923
dc.description.abstract This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery’s ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack. en_UK
dc.language.iso en en_UK
dc.publisher IEEE en_UK
dc.rights Attribution 3.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Battery en_UK
dc.subject electric vehicle (EV) en_UK
dc.subject equivalent circuit modelling en_UK
dc.subject identification en_UK
dc.subject optimization en_UK
dc.title Accuracy versus simplicity in online battery model identification en_UK
dc.type Article en_UK


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