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arXiv · 2212.09452

Identification of optimal prediction error Th\'evenin models of Li-ion cells using the MOLI approach

Paulo Lopes dos Santos·T-P Azevedo Perdicoúlis·Paulo A. Salgado
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Abstract

This report presents System Identification algorithms to estimate the dynamical model of Li-Oin cells. First the dependence of open circuit voltage (OCV) on the state of charge (SOC) is studied. thN battery equivalent model when a resistor is added to the circuit is stated. The discharge data is divided into segments where the internal resistance is assumed constant, and therefore SOC is constant, thence is described an LTI identification algorithm to be used to estimate the cell model in each segment. A Randles circuit is introduced to the model to describe the diffusion process. This model includes the so called Warburg impedance which is as fractional system. This impedance is discussed and it is approximatted by a finite order linear time invariant state-space model. Also, after presenting the simplified Randles circuit, is stated an identification algorithm that estimates the parameters of this model. The Th\'evenin model is presented as an alternative to the Randles circuit. An algorithm to identify a Th\'evenin model of 1st and 2nd order is enunciated. The performances of the simplified randles model and of the two Th\'evenin models models described and its respective identification algorithms, are discussed and compared using an experimental set of data.

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Paulo Lopes dos Santos, T-P Azevedo Perdicoúlis, Paulo A. Salgado. 2022-12-19. Identification of optimal prediction error Th\'evenin models of Li-ion cells using the MOLI approach. https://arxiv.org/abs/2212.09452

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