We propose a method that enables the design of parameter specific optimal experiments to complement existing parametrization approaches. Previous studies on optimal experiments focus on fisher information based experiment design [1][2], which employ local sensitivities to characterize experiment information content. This has the distinct weakness of dependency on the initial parameter vector. We propose a method utilizing a global sensitivity scheme to fully map the available parameter space and thus account for model non-linearity and parameter interdependence. Furthermore, common optimal experiment design approaches for physics-based battery models consider a limited experiment space, choosing from a set a pre-defined experiments [2]. We circumvent this issue by algorithmically generating virtual experiments and finding the optimal one within this space. We validate our approach using both model and experiments. The experimental validation is based on the comparison with conventional battery characterization tools such as electrochemical impedance spectroscopy (EIS), galvanostatic intermittent titration technique (GITT) or scanning electron microscopy (SEM).With the accelerating growth in battery electric vehicles (BEV), efficient utilization of rapidly evolving system designs and chemistries shifts into focus. Recent developments towards nickel-rich cathode materials aiming for higher energy densities can only be an intermediate step towards the next generation of batteries. These fast paced improvements pose an immense challenge for the conventional approach to battery management systems (BMS), mainly in their reliance on empirical and experimentally validated control oriented models. Trends are pointing towards physics based (PM) as well as data-driven models (DM) for BMS, whose accuracy and resolution enables aging sensitive control. PM rely on long term cycling studies and detailed electrochemical characterization procedures for accurate parametrization.We propose a method for model parametrization utilizing global sensitivity based optimal experiment design. The method improves on conventional techniques in four ways: firstly, it significantly reduces computational and experimental effort, as we show by studying the impact of test length on identifiability. Secondly, it improves model accuracy through reductions in parameter error. Thirdly, it improves model fidelity by specifically designing experiments to excite desired parameters and finally, it enables full-cell parametrization with small experimental effort. We propose several use-cases for this method: beginning of life (BOL) parametrization, aging-specific experiments and model reparametrization and finally virtual twin [3] parametrization using a DM surrogate. We investigate which qualitatively dependent parameters can be untangled and therefore accurately determined from full cell measurements. We benchmark our method with half-cell experiments of nickel-rich electrodes harvested at beginning of life from commercial cells.[1] A. Pozzi, G. Ciaramella, S. Volkwein, and D. M. Raimondo, “Optimal design of experiments for a lithium-ion cell: Parameters identification of an isothermal single particle model with electrolyte dynamics,” en, Industrial & Engineering Chemistry Research, vol. 58, no. 3, pp. 1286–1299, 2019[2] S. Park, D. Kato, Z. Gima, R. Klein, and S. Moura, “Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model,”, Journal of The Electrochemical Society, vol. 165, no. 7, A1309–A1323, 2018[3] B. Wu, W. D. Widanage, S. Yang, and X. Liu, “Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems,”, Energy and AI, vol. 1, 2020 Figure 1
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Jul 3, 2024
Doney Abraham + 4
