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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 language | English |
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Article number | 795 |
Journal | Energies |
Volume | 11 |
Issue number | 4 |
Pages (from-to) | 1-19 |
Number of pages | 19 |
ISSN | 1996-1073 |
DOIs | |
Publication status | Published - Mar 2018 |
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
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Dive into the research topics of 'Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries'. Together they form a unique fingerprint.Projects
- 1 Finished
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ALPBES: Advanced Lifetime Predictions of Battery Energy Storage
Kær, S. K., Andreasen, S. J., Teodorescu, R., Stroe, A., Barreras, J. V., Khan, M. R. & Swierczynski, M. J.
DSF The Danish Council for Strategic Research
01/02/2013 → 30/08/2017
Project: Research