Cited by 205Battery Modeling and ManagementControl Systems and IdentificationTarget Tracking and Data Fusion in Sensor Networks

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Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model

Hu, Xiaosong, Cao, Dongpu, Egardt, Bo·2018·IEEE/ASME Transactions on Mechatronics

DOI 10.1109/tmech.2017.2675920

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Abstract

Efficient battery condition monitoring is of particular importance in large-scale, high-performance, and safety-critical mechatronic systems, e.g., electrified vehicles and smart grid. This paper pursues a detailed assessment of optimization-driven moving horizon estimation (MHE) framework by means of a reduced electrochemical model. For state-of-charge estimation, the standard MHE and two variants in the framework are examined by a comprehensive consideration of accuracy, computational intensity, effect of horizon size, and fault tolerance. A comparison with common extended Kalman filtering and unscented Kalman filtering is also carried out. Then, the feasibility and performance are demonstrated for accessing internal battery states unavailable in equivalent circuit models, such as solid-phase surface concentration and electrolyte concentration. Ultimately, a multiscale MHE-type scheme is created for State-of-Health estimation. This study is the first known systematic investigation of MHE-type estimators applied to battery management.

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Authors: Hu, X., Cao, D., & Egardt, B.
Affiliations: Chongqing University (CN)
Cranfield University (GB)
Chalmers University of Technology (SE)
ORCID: Cao, Dongpu
Egardt, Bo
Venue: IEEE/ASME Transactions on Mechatronics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2018-02
Volume: 23
Issue: 1
Pages: 167-178
ISSN: 1083-4435 (ISSN-L)
1941-014X
DOI: 10.1109/tmech.2017.2675920
OpenAlex: W2594530559
Cited by: 205
References: 38
Citations by year: 2026: 8 · 2025: 10 · 2024: 24 · 2023: 31 · 2022: 20 · 2021: 34 · 2020: 23 · 2019: 26 · 2018: 26
FWCI: 13.52
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE/ASME Transactions on Mechatronics (publishedVersion)
Chalmers Research (Chalmers University of Technology) (submittedVersion)
Funders: British Mass Spectrometry Society
Else Kröner-Fresenius-Stiftung
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
Keywords: battery state monitoring, moving horizon estimation, state of charge estimation, state of health estimation, electrolyte concentration, multiscale estimation, fault tolerance, horizon size, electrified vehicles, smart grid, extended Kalman filter, unscented Kalman filter, computational efficiency

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Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model

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