An auto-regressive model for battery voltage prediction

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Abstract

Accurate modelling of the dynamic behaviour of Lithium-ion (Li-ion) batteries is important in a wide range of scenarios from the determination of appropriate battery-pack size, to battery balancing and state estimation in battery management systems. The prevailing methods used in voltage prediction are the equivalent electrical circuit (EEC) models. EEC models account for the change in the voltage by a series of resistor capacitor networks to mimic the internal resistance of a battery. Thus, given a change in current the EEC models create an appropriate change in the voltage. The downside is that the parameters of the model needs to be fully characterised, across the entire range of usage and life of the battery. This is both time consuming and expensive. In this paper, a linear auto-regressive (AR) process is proposed to account for the short-term dynamic behaviour of the battery cell, allowing for accurate prediction of the voltage given other measurable parameters such as current and temperature. After conducting a sensitivity analysis on the size of the sequence needed to train the AR model, it was found that less than a days worth of raw measurements data is enough to offer a better voltage prediction than a traditional EEC model (the root mean square errors of the two considered voltage estimation approaches were 0.00157 and 0.0133 V, respectively).

Original languageEnglish
Title of host publication2021 IEEE Applied Power Electronics Conference and Exposition (APEC)
Number of pages8
PublisherIEEE
Publication date2021
Pages2673-2680
ISBN (Print)978-1-7281-8950-5
ISBN (Electronic)978-1-7281-8949-9
DOIs
Publication statusPublished - 2021
Event36th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2021 - Virtual, Online, United States
Duration: 14 Jun 202117 Jun 2021

Conference

Conference36th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2021
Country/TerritoryUnited States
CityVirtual, Online
Period14/06/202117/06/2021
SponsorIEEE Industry Applications Society, IEEE Power Electronics Society, Power Sources Manufacturers Association
SeriesI E E E Applied Power Electronics Conference and Exposition. Conference Proceedings
ISSN1048-2334

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Lithium-ion battery
  • Voltage prediction
  • Auto-regressive process
  • Equivalent electrical circuits

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  • An auto-regressive model for battery voltage prediction

    Vilsen, S. B. & Stroe, D-I., 2021, 2021 IEEE Applied Power Electronics Conference and Exposition (APEC). IEEE, p. 2673-2680 8 p. (I E E E Applied Power Electronics Conference and Exposition. Conference Proceedings).

    Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

    Open Access
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    75 Downloads (Pure)

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