Accurate battery parameter estimation with improved continuous time system identification methods

Bing Xia Xin Zhao 1 Raymond de Callafon 1 Hugues Garnier 2 Truong Nguyen Chris Mi
1 UCSD - Department of Mechanical and Aerospace Engineering [San Diego]
2 CRAN - Centre de Recherche en Automatique de Nancy
Abstract : The modeling of Lithium-ion batteries usually utilizes discrete-time system identification methods to estimate parameters of discrete models. However, in real applications, there is a fundamental limitation of the discrete-time methods in dealing with sensitivity when the system is stiff and the storage resolutions are limited. To overcome this problem, this paper adopts direct continuous-time system identification methods to estimate the parameters of equivalent circuit models for Lithium-ion batteries. Compared with discrete- time system identification methods, the continuous-time system identification methods provide more accurate estimates to both fast and slow dynamics in battery systems and are less sensitive to disturbances. A case of a second order equivalent circuit model is studied which shows that the continuous-time estimates are more robust to high sampling rates, measurement noises and rounding errors. In addition, the estimation by the conventional continuous-time least squares method is further improved in the case of noisy output measurement by introducing the instrumental variable method. Simulation and experiment results validate the analysis and demonstrate the advantages of the continuous-time system identification methods in battery applications.
Keywords : battery modeling continuous-time identification
Document type :
Conference papers
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https://hal-auf.archives-ouvertes.fr/hal-01346590
Contributor : Hugues Garnier <>
Submitted on : Tuesday, July 19, 2016 - 11:54:58 AM
Last modification on : Wednesday, August 7, 2019 - 12:18:06 PM

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Bing Xia, Xin Zhao, Raymond de Callafon, Hugues Garnier, Truong Nguyen, et al.. Accurate battery parameter estimation with improved continuous time system identification methods. IEEE Energy Conversion Congress and Exposition, ECCE 2016, IEEE, Sep 2016, Milwaukee, United States. ⟨hal-01346590⟩

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