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Kalman Filter Tuning for State Estimation of Lithium-Ion Batteries by Multi-Objective Optimization via Hyperspace Exploration

Titelangaben

Mößle, Patrick ; Tietze, Tobias ; Danzer, Michael A.:
Kalman Filter Tuning for State Estimation of Lithium-Ion Batteries by Multi-Objective Optimization via Hyperspace Exploration.
In: Energy Technology. Bd. 11 (2023) Heft 12 . - 2300796.
ISSN 2194-4296
DOI: https://doi.org/10.1002/ente.202300796

Volltext

Link zum Volltext (externe URL): Volltext

Abstract

For the estimation of the state of charge of lithium-ion batteries Kalman filters are the state of the art. To ensure precise and reliable estimations these filters use covariance matrices, which need to be tuned correctly by the developer. This process is time-consuming and depends largely on the experience and skill of the developer. Hence, filter tuning is not reproducible and not optimal with regard to goals as accuracy and convergence speed. Herein a multiobjective optimization framework called hyperspace exploration is used for the first time to automate the filter tuning procedure for an extended Kalman filter and two versions of adaptive extended Kalman filters. Four key performance indicators, including the maximum error in the estimation of the state of charge and the according root mean square error, are used to describe, validate, and compare the filter performance. This automated process enables optimal usage of the degrees of freedom in filter tuning and no longer requires manual tuning while the whole hyperspace, including different use cases and validation scenarios, is considered in the optimization. Furthermore, the proposed approach yields a novel method for the evaluation of filter parameters and their influence on the estimation behavior.

Weitere Angaben

Publikationsform: Artikel in einer Zeitschrift
Begutachteter Beitrag: Ja
Keywords: Kalman filter; lithium-ion batteries; multi-objective optimization; state estimation
Institutionen der Universität: Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Elektrische Energiesysteme > Lehrstuhl Elektrische Energiesysteme - Univ.-Prof. Dr. Michael Danzer
Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Systemtechnik elektrischer Energiespeicher
Forschungseinrichtungen > Zentrale wissenschaftliche Einrichtungen > Bayerisches Zentrum für Batterietechnik - BayBatt
Fakultäten
Fakultäten > Fakultät für Ingenieurwissenschaften
Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Elektrische Energiesysteme
Forschungseinrichtungen
Forschungseinrichtungen > Zentrale wissenschaftliche Einrichtungen
Titel an der UBT entstanden: Ja
Themengebiete aus DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Eingestellt am: 16 Dec 2023 22:01
Letzte Änderung: 18 Dec 2023 06:21
URI: https://eref.uni-bayreuth.de/id/eprint/88071