Online reduced complexity parameter estimation technique for equivalent circuit model of lithium-ion battery

Journal article

Saleem, K., Mehran, K. and Ali, Z. (2020). Online reduced complexity parameter estimation technique for equivalent circuit model of lithium-ion battery. Electric Power Systems Research. 185, p. 106356. https://doi.org/10.1016/j.epsr.2020.106356
AuthorsSaleem, K., Mehran, K. and Ali, Z.
Abstract

For control-oriented battery management applications in electric vehicles, Equivalent Circuit Model (ECM) of battery packs offer acceptable modelling accuracy and simple mathematical equations for including the cell parameters. However, in real-time applications, circuit parameters continuously changes by varying operating conditions and state of the battery and thus, require an online parameter estimator. The estimator must update the battery parameters with less computational complexity suitable for real-time processing. This paper presents a novel Online Reduced Complexity (ORC) technique for the online parameter estimation of the ECM. The proposed technique provides significantly less complexity (hence estimation time) compared to the existing technique, but without compromising the accuracy. We use Trust Region Optimization (TRO) based Least Square (LS) method as an updating algorithm in the proposed technique and validate our results experimentally using Nissan Leaf (pouch) cells and with the help of standard vehicular testing cycles, i.e. the Dynamic Driving Cycle (DDC), and the New European Driving Cycle (NEDC).

KeywordsEquivalent circuit modelLithium-ion battery, Battery management systemParameter estimationDriving cyclesNissan leaf pouch cell
Year2020
JournalElectric Power Systems Research
Journal citation185, p. 106356
PublisherElsevier
ISSN1873-2046
Digital Object Identifier (DOI)https://doi.org/10.1016/j.epsr.2020.106356
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0378779620301620
Publication dates
Print27 Mar 2020
Publication process dates
Accepted03 May 2020
Deposited21 Feb 2024
Accepted author manuscript
License
File Access Level
Open
Additional information

© 2020 Elsevier B.V. All rights reserved.

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