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초록

Parameter identification (PI) is a cost-effective approach for estimating the parameters of an electrochemical model for lithium-ion batteries (LIBs). However, it requires identifiability analysis (IA) of model parameters because identifiable parameters vary with reference data and electrochemical models. Therefore, we propose a PI and IA (PIIA) framework for a robust PI that can adapt to discharge data. The IA results show that the best subset with 15 parameters is determined by the Fisher information matrix and the sample-averaged RDE criterion under various operating conditions. The identification process based on a genetic algorithm determines the optimal parameters. The identified-parameter model predicts voltage curves with uncertainty bounds, considering the confidence intervals of identified parameters. Further, we demonstrate that the proposed PIIA framework robustly identifies the parameters of the electrochemical model from experimental data.

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