Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context⁎X. Xie acknowledges support from the International Max Planck Research School for Advanced Methods in Process and Systems Engineering, MPI Magdeburg. D.M. Raimondo has been (partially) supported by the Italian Ministry for Research in the framework of the 2017 Program for Research Projects of National Interest (PRIN), Grant no. 2017YKXYXJ. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Pozzi, A.; Xie, X.; Raimondo, D.M.; Schenkendorf, R.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 7248
• Page End: 7255
• Publication Date: 2020
• Subjects: parameter identification -- global parameter sensitivities -- uncertainty quantification -- design of experiments -- lithium-ion batteries; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.558
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Battery management systems may rely on mathematical models to provide higher performance than standard charging protocols. Electrochemical models allow us to capture the phenomena occurring inside a lithium-ion cell and therefore, could be the best model choice. However, to be of practical value, they require reliable model parameters. Uncertainty quantification and optimal experimental design concepts are essential tools for identifying systems and estimating parameters precisely. Approximation errors in uncertainty quantification result in sub-optimal experimental designs and consequently, less-informative data, and higher parameter unreliability. In this work, we propose a highly efficient design of experiment method based on global parameter sensitivities. This novel concept is applied to the single-particle model with electrolyte and thermal dynamics (SPMeT), a well-known electrochemical model for lithium-ion cells. The proposed method avoids the simplifying assumption of output-parameter linearization (i.e., local parameter sensitivities) used in conventional Fisher information matrix-based experimental design strategies. Thus, the optimized current input profile results in experimental data of higher information content and in turn, in more precise parameter estimates.