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Experimental Investigation of State and Parameter Estimation within Reconfigurable Battery Systems

  • The battery system is one of the most-important, but also -critical components in the electric power-train. The battery’s system states and parameters are commonly tracked by the battery monitoring system. However, in reality, the accuracy of the state and parameter estimation may suffer from insufficient excitation of the system. Since the current states and parameters serve as the basis for many battery management system functions, this might lead to incorrect operation and severe damage. Reconfigurable battery systems allow enhancing the system’s excitation by applying a switching operation. In this contribution, the state and parameter estimation of a reconfigurable battery module were simulated and tested experimentally. Thereby, a low-exciting and a high-exciting drive cycle were compared. Furthermore, the switching patterns were applied to enhance the excitation and, hence, improve the estimation of an extended Kalman filter. The cells were switched via a pulse-width modulation signal, and the influence of frequency and dutyThe battery system is one of the most-important, but also -critical components in the electric power-train. The battery’s system states and parameters are commonly tracked by the battery monitoring system. However, in reality, the accuracy of the state and parameter estimation may suffer from insufficient excitation of the system. Since the current states and parameters serve as the basis for many battery management system functions, this might lead to incorrect operation and severe damage. Reconfigurable battery systems allow enhancing the system’s excitation by applying a switching operation. In this contribution, the state and parameter estimation of a reconfigurable battery module were simulated and tested experimentally. Thereby, a low-exciting and a high-exciting drive cycle were compared. Furthermore, the switching patterns were applied to enhance the excitation and, hence, improve the estimation of an extended Kalman filter. The cells were switched via a pulse-width modulation signal, and the influence of frequency and duty cycle variation on the estimation accuracy were investigated. Compared to the low-excitation input, a significant improvement in the estimation of up to 46% for the state of charge and 78% for the internal resistance were achieved. Hereby, low frequencies and duty cycles proved to be particularly advantageous. Switching, however, has only a limited influence on an already highly excited system and may lead to additional aging due to higher heat generation.show moreshow less

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Metadaten
Author:Michael TheilerORCiD, Dominik SchneiderORCiD, Christian EndischORCiD
Language:English
Document Type:Article
Year of first Publication:2023
published in (English):Batteries
Publisher:MDPI
Place of publication:Basel
ISSN:2313-0105
Volume:9
Issue:3
Pages:18
Article Number:145
Review:peer-review
Open Access:ja
Version:published
Tag:Kalman filter; battery model; joint estimation; parameter estimation; reconfigurable battery systems; state estimation
URN:urn:nbn:de:bvb:573-31110
Related Identifier:https://doi.org/10.3390/batteries9030145
Faculties / Institutes / Organizations:Fakultät Elektro- und Informationstechnik
Institut für Innovative Mobilität (IIMo)
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2023/02/22