Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

Ana-Irina Stroe, Jinhao Meng, Daniel-Ioan Stroe, Maciej Jozef Swierczynski, Remus Teodorescu, Søren Knudsen Kær

Research output: Contribution to journalJournal articleResearchpeer-review

4 Citations (Scopus)
264 Downloads (Pure)

Abstract

State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.
Original languageEnglish
Article number795
JournalEnergies
Volume11
Issue number4
Pages (from-to)1-19
Number of pages19
ISSN1996-1073
DOIs
Publication statusPublished - Mar 2018

Fingerprint

Parametric Uncertainty
Battery
Oxides
Lithium
Charge
Error analysis
Estimation Error
Extended Kalman filters
Electric potential
Sensitivity analysis
Voltage
Lithium-ion Battery
Influence
Uncertainty
Estimation Algorithms
Initialization
Kalman Filter
Sensitivity Analysis
Model
Model-based

Keywords

  • Lithium-ion batteries
  • Lithium titanate oxide (LTO) batteries
  • Hybrid pulse power characterization test
  • Model parametrization
  • Equivalent electrical circuit
  • State of charge
  • Extended Kalman Filter

Cite this

@article{4852b8c1aefc4914bae95972a5c2d0eb,
title = "Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries",
abstract = "State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16{\%} and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.",
keywords = "Lithium-ion batteries, Lithium titanate oxide (LTO) batteries, Hybrid pulse power characterization test, Model parametrization, Equivalent electrical circuit, State of charge, Extended Kalman Filter",
author = "Ana-Irina Stroe and Jinhao Meng and Daniel-Ioan Stroe and Swierczynski, {Maciej Jozef} and Remus Teodorescu and K{\ae}r, {S{\o}ren Knudsen}",
year = "2018",
month = "3",
doi = "10.3390/en11040795",
language = "English",
volume = "11",
pages = "1--19",
journal = "Energies",
issn = "1996-1073",
publisher = "M D P I AG",
number = "4",

}

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries. / Stroe, Ana-Irina; Meng, Jinhao; Stroe, Daniel-Ioan; Swierczynski, Maciej Jozef; Teodorescu, Remus; Kær, Søren Knudsen.

In: Energies, Vol. 11, No. 4, 795, 03.2018, p. 1-19.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

AU - Stroe, Ana-Irina

AU - Meng, Jinhao

AU - Stroe, Daniel-Ioan

AU - Swierczynski, Maciej Jozef

AU - Teodorescu, Remus

AU - Kær, Søren Knudsen

PY - 2018/3

Y1 - 2018/3

N2 - State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

AB - State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

KW - Lithium-ion batteries

KW - Lithium titanate oxide (LTO) batteries

KW - Hybrid pulse power characterization test

KW - Model parametrization

KW - Equivalent electrical circuit

KW - State of charge

KW - Extended Kalman Filter

UR - http://www.scopus.com/inward/record.url?scp=85045388412&partnerID=8YFLogxK

U2 - 10.3390/en11040795

DO - 10.3390/en11040795

M3 - Journal article

VL - 11

SP - 1

EP - 19

JO - Energies

JF - Energies

SN - 1996-1073

IS - 4

M1 - 795

ER -