TR2019-065
Parameter Identification of the Nonlinear Double-Capacitor Model for Lithium-Ion Batteries: From the Wiener Perspective
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- "Parameter Identification of the Nonlinear Double-Capacitor Model for Lithium-Ion Batteries: From the Wiener Perspective", American Control Conference (ACC), DOI: 10.23919/ACC.2019.8814957, July 2019, pp. 897-902.BibTeX TR2019-065 PDF
- @inproceedings{Tian2019jul,
- author = {Tian, Ning and Fang, Huazhen and Wang, Yebin},
- title = {Parameter Identification of the Nonlinear Double-Capacitor Model for Lithium-Ion Batteries: From the Wiener Perspective},
- booktitle = {American Control Conference (ACC)},
- year = 2019,
- pages = {897--902},
- month = jul,
- doi = {10.23919/ACC.2019.8814957},
- url = {https://www.merl.com/publications/TR2019-065}
- }
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- "Parameter Identification of the Nonlinear Double-Capacitor Model for Lithium-Ion Batteries: From the Wiener Perspective", American Control Conference (ACC), DOI: 10.23919/ACC.2019.8814957, July 2019, pp. 897-902.
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Abstract:
Battery parameter identification is emerging as an important topic due to the increasing use of battery energy storage. This paper studies parameter identification for the nonlinear double-capacitor (NDC) model for Lithium-ion batteries, which is a new equivalent circuit model developed in the authors’ previous work [1]. It is noticed that the NDC model has a structure similar to the Wiener system. From the Wiener perspective, this work builds a parameter identification approach for this model upon the well-known maximum a posteriori (MAP) estimation. The purpose of using MAP is to overcome the nonconvexity and local minima that can cause unphysical parameter estimates. The proposed approach is the first one that we aware of exploits MAP for Wiener system identification. It also demonstrates significant effectiveness for accurate identification of the NDC model, validated through simulations and experiments.
Related News & Events
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NEWS MERL researchers presented 8 papers at American Control Conference Date: July 10, 2019 - July 12, 2019
Where: Philadelphia
MERL Contacts: Ankush Chakrabarty; Stefano Di Cairano; Devesh K. Jha; Yebin Wang; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference, MERL presented 8 papers on subjects including model predictive control applications, estimation and motion planning for vehicles, modular control architectures, and adaptation and learning.
Related Publication
- @article{Tian2020apr,
- author = {Tian, Ning and Fang, Huazhen and Chen, Jian and Wang, Yebin},
- title = {Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2020,
- pages = {1--15},
- month = apr,
- doi = {10.1109/TCST.2020.2976036},
- url = {https://www.merl.com/publications/TR2020-035}
- }