One of the main concerns regarding energy storage systems during their normal operation is the possibility to perform an accurate state-of-charge estimation. This cannot be done by simple ampere-hour counting, unless drift correction means are put in place to avoid accumulation of measurement errors over time. In this paper, a state-of-charge estimation algorithm is widely analysed and tested on a nickel manganese cobalt oxide (NMC) lithium cell. The procedure consists of the utilisation of an equivalent electrical network battery model and the implementation of a Luenberger technique for a runtime correction, from the measure of battery’s voltage and current. Although application of Luenberger-style estimation is not new in literature for application to batteries, new expressions of battery model parameters and more detailed simulations are shown, to imply much higher estimation accuracy than in the past. After setting the model parameters, different test cycles have been considered, to evaluate the robustness of the proposed technique.

Luenberger Observer for Lithium Battery State-of-Charge Estimation

Barsali S.;Ceraolo M.;Li J.;Lutzemberger G.;Scarpelli C.
2020-01-01

Abstract

One of the main concerns regarding energy storage systems during their normal operation is the possibility to perform an accurate state-of-charge estimation. This cannot be done by simple ampere-hour counting, unless drift correction means are put in place to avoid accumulation of measurement errors over time. In this paper, a state-of-charge estimation algorithm is widely analysed and tested on a nickel manganese cobalt oxide (NMC) lithium cell. The procedure consists of the utilisation of an equivalent electrical network battery model and the implementation of a Luenberger technique for a runtime correction, from the measure of battery’s voltage and current. Although application of Luenberger-style estimation is not new in literature for application to batteries, new expressions of battery model parameters and more detailed simulations are shown, to imply much higher estimation accuracy than in the past. After setting the model parameters, different test cycles have been considered, to evaluate the robustness of the proposed technique.
2020
978-3-030-37160-9
978-3-030-37161-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1043842
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