Estimating the State of Charge of Lithium-Ion Batteries Based on the Transfer Function of the Voltage Response to the Current Pulse

Ivan Radaš (), Nicole Pilat, Daren Gnjatović, Viktor Šunde and Željko Ban
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Ivan Radaš: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
Nicole Pilat: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
Daren Gnjatović: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
Viktor Šunde: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
Željko Ban: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia

Energies, 2022, vol. 15, issue 18, 1-14

Abstract: There are several methods for estimating the SoC of lithium-ion batteries that use electrochemical battery models or artificial intelligence and intelligent algorithms. These methods have numerous advantages but are complex and computationally intensive. This paper presents a new method for estimating the SoC of lithium-ion batteries based on identifying the transfer function of the measured battery voltage response to the charging current pulse. It is assumed that the transfer function of the battery changes with the state of charge. In the learning phase, a reference table of known SoC s and associated transfer functions is created. The parameters of these transfer functions form the reference points in hyperspace. In the phase of determining the unknown SoC of the battery, the parameters of the measured transfer function form a point in hyperspace that is compared with the reference points of the transfer functions for known SoC s. The unknown SoC of the battery at the particular measurement time is obtained by finding the two reference points closest to the point of unknown SoC using the Euclidean distance and a linear interpolation based on this distance. The method is simple, computationally undemanding, insensitive to measurement noise, and has high accuracy in SoC estimation.

Keywords: lithium-ion batteries; estimating the SoC of battery; battery’s equivalent circuit model; transfer function of battery; Euclidean hyperspace of transfer function parameters (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: 2022
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