Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle

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


The following work presents the model-assisted research on Valve-Regulated Lead-Acid (VRLA) Absorbent Glass Mat (AGM) battery in pulse operation cycle. The experimental research was conducted for a constant value of State of Charge (SOC) of the battery, for values ranging from 0.2 to 0.8. Based on the conducted test stand research, the parameters of the battery were identified, which were later used to model the battery using the equivalent circuit based on dual polarization (DP) model with double Resistive-Capacitive (RC) loop. Simulations were performed for the identified parameters of the battery which are described by the general form of the polynomial. The second part contains the research on utilization of Nonlinear AutoRegressive eXogenous (NARX) recurrent neural network to predict SOC and a terminal voltage of the battery. Obtained validation results with the use of the identified parameters of the double RC loop and NARX model were discussed in the following work. The article also features the advantages and disadvantages of NARX model and DP model utilization for the use of in Battery Managements Systems (BMS) and micro-installations based on renewable energy sources. Furthermore, the advantages of the addition of more RC loops to describe the dynamic states of batteries in pulse states were discussed in the article.
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
Journal seriesEnergies, ISSN 1996-1073
Issue year2018
Publication size in sheets1.35
Keywords in EnglishNARX neural network; identification; pulse cycle; VRLA battery
ASJC Classification1700 General Computer Science
Languageen angielski
LicenseJournal (articles only); published final; Uznanie Autorstwa (CC-BY); after publication
Score (nominal)25
Score sourcejournalList
ScoreMinisterial score = 25.0, 08-07-2020, ArticleFromJournal
Publication indicators WoS Citations = 0; Scopus Citations = 1; GS Citations = 1.0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.156; WoS Impact Factor: 2018 = 2.707 (2) - 2018=2.99 (5)
Citation count*1 (2020-07-08)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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