Efficient Parametrization of Electric Circuit-Based Battery Models: A Novel Approach Using Adaptive Polynomial Fitting

Battery systems are increasingly recognized as crucial components within both current and future energy systems. Thereby, a proper modelling of their dynamic behavior under different load conditions is of high importance. In this work, we present a workflow using the Electric Equivalent Circuit model with one RC-element (ECM), and a novel approach for its parametrization. As a first step, pulse discharge measurements on a set of LiFePO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf>-battery cells are conducted with a uniform discharge current and equal time-intervals. Based on the recorded voltage and current profile, discrete sets of the EEC components and corresponding State of Charge values are calculated for every single cell. An adaptive polynomial fitting method is applied to this discrete dataset, thereby generating a numerically optimal ECM model. The result is a mathematical model which is able to simulate the transient behavior of a random sample of a certain cell model. In comparison to other common parametrization approaches, the presented method has the advantage of having a low time effort for the generation of a large dataset, guaranteed numerical success, and applicability for an arbitrary battery type.

Authors

Institutions

Publication Details

Published
2024-11-03
DOI
https://doi.org/10.1109/iecon55916.2024.10905638
Citations
1
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Efficient Parametrization of Electric Circuit-Based Battery Models: A Novel Approach Using Adaptive Polynomial Fitting

Stephan Ledinger, Bernd Eschelmüller, Daniel Stahleder, Petra Alexendra Reisz
1 citations
Advanced Battery Technologies Research
0.20
article

Efficient Parametrization of Electric Circuit-Based Battery Models: A Novel Approach Using Adaptive Polynomial Fitting

Stephan Ledinger, Bernd Eschelmüller, Daniel Stahleder, Petra Alexendra Reisz
article en
1 citations

Abstract

Battery systems are increasingly recognized as crucial components within both current and future energy systems. Thereby, a proper modelling of their dynamic behavior under different load conditions is of high importance. In this work, we present a workflow using the Electric Equivalent Circuit model with one RC-element (ECM), and a novel approach for its parametrization. As a first step, pulse discharge measurements on a set of LiFePO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf>-battery cells are conducted with a uniform discharge current and equal time-intervals. Based on the recorded voltage and current profile, discrete sets of the EEC components and corresponding State of Charge values are calculated for every single cell. An adaptive polynomial fitting method is applied to this discrete dataset, thereby generating a numerically optimal ECM model. The result is a mathematical model which is able to simulate the transient behavior of a random sample of a certain cell model. In comparison to other common parametrization approaches, the presented method has the advantage of having a low time effort for the generation of a large dataset, guaranteed numerical success, and applicability for an arbitrary battery type.

AIT Austrian Institute of Technology GmbH (AT)
Affordable and clean energy
Openalex Percentile: Top 45%
Advanced Battery Technologies Research
0.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.