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

Title data

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. Vol. 11 (2023) Issue 12 . - 2300796.
ISSN 2194-4296
DOI: https://doi.org/10.1002/ente.202300796

Official URL: Volltext

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Kalman filter; lithium-ion batteries; multi-objective optimization; state estimation
Institutions of the University: Faculties > Faculty of Engineering Science > Chair Electrical Energy Systems > Chair Electrical Energy Systems - Univ.-Prof. Dr. Michael Danzer
Faculties > Faculty of Engineering Science > Chair Systems Engineering for Electrical Energy Storage
Research Institutions > Central research institutes > Bayerisches Zentrum für Batterietechnik - BayBatt
Faculties
Faculties > Faculty of Engineering Science
Faculties > Faculty of Engineering Science > Chair Electrical Energy Systems
Research Institutions
Research Institutions > Central research institutes
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 16 Dec 2023 22:01
Last Modified: 18 Dec 2023 06:21
URI: https://eref.uni-bayreuth.de/id/eprint/88071