The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model
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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“One common way is through the use of models simulating the behaviours of cells [8,9], followed by various optimization algorithms and observers, such as the Kalman filter family [10] and particle filter [11] to identify the parameters and SOH states [4].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [9]
Citations
Cites
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.032 · Cited by 1,927
Fast Model Predictive Control Using Online Optimization
Wang, Y., & Boyd, S. (2009) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2009.2017934 · Cited by 1,411
Methods for state-of-charge determination and their applications
Piller, S., Perrin, M., & Jossen, A. (2001) · Journal of Power Sources · doi:10.1016/s0378-7753(01)00560-2 · Cited by 1,207
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831
Rezvanizaniani, S. M., Liu, Z., Chen, Y., & Lee, J. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.01.085 · Cited by 714
Sun, F., Hu, X., Zou, Y., & Li, S. (2011) · Energy · doi:10.1016/j.energy.2011.03.059 · Cited by 614
Self-citationSigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.06.004 · Cited by 547
Control oriented 1D electrochemical model of lithium ion battery
Smith, K. A., Rahn, C. D., & Wang, C.-Y. (2007) · Energy Conversion and Management · doi:10.1016/j.enconman.2007.03.015 · Cited by 449
Kim, I.-S. (2009) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2009.2034966 · Cited by 438
Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries
Andre, D., Appel, C., Soczka-Guth, T., & Sauer, D. U. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.001 · Cited by 428
Research works printed but not linked: 2
- rawlings, 2009, Model Predictive Control Theory and Design
- newman, 2008, Fortran programs for the simulation of electrochemical systems, University of California at Berkeley CA USA
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Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,394
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
“One common way is through the use of models simulating the behaviours of cells [8,9], followed by various optimization algorithms and observers, such as the Kalman filter family [10] and particle filter [11] to identify the parameters and SOH states [4].”
MethodState estimation for advanced battery management: Key challenges and future trends
Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857
Self-citationChemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.06.104 · Cited by 818
Hu, X., Zheng, Y., Howey, D. A., Perez, H. E., Foley, A. M., & PECHT, M. G. (2019) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2019.100806 · Cited by 459
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Metadata
- Authors
- Hu, X., Cao, D., & Egardt, B.
- Affiliations
- Chongqing University (CN)
- Cranfield University (GB)
- Chalmers University of Technology (SE)
- Venue
- IEEE/ASME Transactions on Mechatronics
- Published
- 2018-02
- Volume
- 23
- Issue
- 1
- Pages
- 167-178
- ISSN
- 1083-4435 (ISSN-L)
- 1941-014X
- 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
- 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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