Battery Voltage Estimation Using NARX Recurrent Neural Network Model

Adrian Chmielewski , Jakub Możaryn , Piotr Piórkowski , Krzysztof Jakub Bogdziński


This work presents a prediction of battery terminal voltage in subsequent charging/discharging cycles. To estimate chosen signals the NARX (Auto-Regressive with eXogenous input) model based on Recurrent Neural Net-work has been employed. A training and testing data were gathered at the la-boratory test stand with the Lithium Iron Phosphate (LiFePO4) battery in dif-ferent working conditions. Test stand research was conducted for 40 charg-ing/discharging cycles. Furthermore, the paper presents the results of the identification of double RC model parameters for a specified state of charge level. As a result, the analysis of the proposed methodology has been dis-cussed.
Author Adrian Chmielewski (FACME / IV)
Adrian Chmielewski,,
- Institute of Vehicles
, Jakub Możaryn (FM / IACR)
Jakub Możaryn,,
- The Institute of Automatic Control and Robotics
, Piotr Piórkowski (FACME / ICME)
Piotr Piórkowski,,
- The Institute of Construction Machinery Engineering
, Krzysztof Jakub Bogdziński (FACME / IV)
Krzysztof Jakub Bogdziński,,
- Institute of Vehicles
Publication size in sheets0.65
Book Szewczyk Roman, Zieliński Cezary, Kaliczyńska Małgorzata (eds.): Automation 2019: Progress in Automation, Robotics and Measurement Techniques, Advances in Intelligent Systems and Computing, vol. 920, 2020, Cham, Switzerland, Springer, ISBN 978-3-030-13272-9, [978-3-030-13273-6], 727 p., DOI:10.1007/978-3-030-13273-6
Keywords in EnglishArtificial Neural Network, LiFePO4 battery, experimental research, Recur-rent Neural Networks.
Languageen angielski
LicensePublisher website (books and chapters only); author's final; Uznanie Autorstwa (CC-BY); after publication
Score (nominal)20
Score sourcepublisherList
ScoreMinisterial score = 20.0, 07-07-2020, ChapterFromConference
Publication indicators Scopus Citations = 1
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