Articles | Volume 15
Adv. Radio Sci., 15, 93–97, 2017
https://doi.org/10.5194/ars-15-93-2017
Adv. Radio Sci., 15, 93–97, 2017
https://doi.org/10.5194/ars-15-93-2017

  21 Sep 2017

21 Sep 2017

State of charge classification for lithium-ion batteries using impedance based features

Marian Patrik Felder and Jürgen Götze Marian Patrik Felder and Jürgen Götze
  • Information Processing Lab, TU Dortmund University, 44221 Dortmund, Germany

Abstract. Currently, the electrification of the drive train of passenger cars takes place, and the task of obtaining precise knowledge about the condition of the on board batteries gains importance. Due to a flat open circuit voltage (OCV) to state of charge (SoC) characteristic of lithium ion batteries, methods employed in applications with other cell chemistries cannot be adapted. Exploiting the higher significance of the impedance for state estimation for that chemistry, new impedance based features are proposed by this work. To evaluate the suitability of these features, simulations have been conducted using a simplified on-board power supply net as excitation source. The simulation outcome has been investigated regarding the cross correlation factor rxy and in a polynomial regression scenario. The results of the simulations show a best case error below 1 % SoC, which is 3 percentage points lower than using terminal voltage and impedance. When increasing the measurement uncertainty, the difference remains around 2 percent points.

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Short summary
Currently, the electrification of the drive train of passenger cars takes place, and the task of obtaining precise knowledge about the condition of the on board batteries gains importance. Due to internal characteristics, several existing methods cannot be used. This work describes an impedance based approach using the Taylor Fourier transformation. The parameters extracted by the method can be used as features in machine learning algorithms.