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Scientific paper - Original scientific paper
Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack
Energies, 13 (2020), 3; 540-556. https://doi.org/10.3390/en13030540
Maletić, Filip; Hrgetić, Mario; Deur, Joško

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

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Maletić, F., Hrgetić, M. & Deur, J. (2020). Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack. Energies, 13. (3), 540-556. doi: 10.3390/en13030540

Maletić, Filip, et al. "Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack." Energies, vol. 13, no. 3, 2020, pp. 540-556. https://doi.org/10.3390/en13030540

Maletić, Filip, Mario Hrgetić and Joško Deur. "Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack." Energies 13, no. 3 (2020): 540-556. https://doi.org/10.3390/en13030540

Maletić, F., Hrgetić, M. and Deur, J. (2020) 'Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack', Energies, 13(3), pp. 540-556. doi: 10.3390/en13030540

Maletić F, Hrgetić M, Deur J. Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack. Energies [Internet]. 2020 January 22 [cited 2024 November 29];13(3):540-556. doi: 10.3390/en13030540

F. Maletić, M. Hrgetić and J. Deur, "Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack", Energies, vol. 13, no. 3, pp. 540-556, January 2020. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:235:353571. [Accessed: 29 November 2024]

Metadata
Title (english)Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack
AuthorFilip Maletić
AuthorMario Hrgetić
AuthorJoško Deur
Author's institutionUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
(Department of Robotics and Production System Automation)
(Chair of Engineering Automation)
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract (english)
Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper first describes the experimental setup that consists of commercially available electric scooter equipped with telemetry measurement equipment. Next, dual extended Kalman filter-based (DEKF) estimator of battery SoC, internal resistances, and parameters of open-circuit voltage (OCV) vs. SoC characteristic is presented under the assumption of fixed polarization time constant vs. SoC characteristic. The DEKF is upgraded with an adaptation mechanism to capture the battery OCV hysteresis without explicitly modelling it. Parameterization of an explicit hysteresis model and its inclusion in the DEKF is also considered. Finally, a slow time scale, sigma-point Kalman filter-based capacity estimator is designed and inter-coupled with the DEKF. A convergence detection algorithm is proposed to ensure that the two estimators are coupled automatically only after the capacity estimate has converged. The overall estimator performance is experimentally validated for real electric scooter driving cycles.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleEnergies
Numberingvol. 13, no. 3, pp. 540-556
p-ISSN1996-1073
DOIhttps://doi.org/10.3390/en13030540
URN:NBNurn:nbn:hr:235:353571
Publication2020-01-22
Document URLhttps://www.mdpi.com/1996-1073/13/3/540
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-07-05 11:54:13