Estimation of Best Linear Approximation from Varying Operating Conditions for the Identification of a Li-ion Battery Model*. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Relan, Rishi; Tiels, Koen; Timmermans, Jean-Marc; Schoukens, Johan
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 4739
• Page End: 4744
• Publication Date: 2017-07
• Subjects: System identification -- Multiple experiments -- Nonlinear models -- Li-ion battery -- Best linear approximation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.867
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The short term dynamic response of the battery varies with varying operating conditions. Hence, even before proceeding towards the modelling step, it is important to fully characterise and understand the dynamic behaviour of the battery at varying operating conditions. In this paper, a data-driven methodology for characterising the battery's short term electrical response at varying operating conditions e.g. at different levels of SoC and different temperature levels is discussed. Furthermore, a novel way to estimate the best linear approximation from the data acquired at these operating conditions with varying levels of noise and nonlinear distortions is proposed.