Accurate Lithium-ion battery parameter estimation with continuous-time system identification methods. (1st October 2016)

• Record Type: Journal Article
• Authors: Xia, Bing; Zhao, Xin; de Callafon, Raymond; Garnier, Hugues; Nguyen, Truong; Mi, Chris
• Is Part Of: Applied energy. Volume 179(2016)
• Page Start: 426
• Page End: 436
• Publication Date: 2016-10-01
• Subjects: Lithium-ion battery -- Equivalent circuit model -- Battery management system -- Continuous-time system identification -- Instrumental variable; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.07.005
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Continuous-time system identification is applied in Lithium-ion battery modeling. Continuous-time and discrete-time identification methods are compared in detail. The instrumental variable method is employed to further improve the estimation. Simulations and experiments validate the advantages of continuous-time methods. 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 2 nd -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.