Reliable Thermal-Physical Modeling of Lithium-Ion Batteries: Consistency between High-Frequency Impedance and Ion Transport

Gabriele Sordi, Claudio Rabissi () and Andrea Casalegno
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Gabriele Sordi: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Claudio Rabissi: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Andrea Casalegno: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy

Energies, 2023, vol. 16, issue 12, 1-17

Abstract: Among lithium-ion battery diagnostic tests, electrochemical impedance spectroscopy, being highly informative on the physics of battery operation within limited testing times, deserves a prominent role in the identification of model parameters and the interpretation of battery state. Nevertheless, a reliable physical simulation and interpretation of battery impedance spectra is still to be addressed, due to its intrinsic complexity. An improved methodology for the calibration of a state-of-the-art physical model is hereby presented, focusing on high-energy batteries, which themselves require a careful focus on the high-frequency resistance of the impedance response. In this work, the common assumption of the infinite conductivity of the current collectors is questioned, presenting an improved methodology for simulating the pure resistance of the cell. This enables us to assign the proper contribution value to current collectors’ resistance and, in turn, not to underestimate electrolyte conductivity, thereby preserving the physical relation between electrolyte conductivity and diffusivity and avoiding physical inconsistencies between impedance spectra and charge–discharge curves. The methodology is applied to the calibration of the model on a commercial sample, demonstrating the reliability and physical consistency of the solution with a set of discharge curves, EIS, and a dynamic driving cycle under a wide range of operating conditions.

Keywords: lithium-ion battery; EIS; parameter identification; modeling; electrolyte conductivity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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