期刊
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
卷 8, 期 2, 页码 1634-1641出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TTE.2021.3124894
关键词
Heating systems; Batteries; Modeling; Computational modeling; Thermal conductivity; Temperature sensors; Eigenvalues and eigenfunctions; Batteries; distributed parameter systems (DPSs); error compensation; modeling; thermal variables' measurement
资金
- General Research Fund (GRF) Project from the Research Grants Council (RGC) of Hong Kong [CityU: 11210719]
- Strategic Research Grant (SRG) Project from the City University of Hong Kong [7005680]
This article proposes a physics/data hybrid modeling framework for the thermal process of lithium-ion batteries. A space decomposition strategy is designed to generate a more effective model, and a reduced-order model and data-based learning are used to capture the battery's thermal dynamics. Simulation studies and experiments demonstrate the effectiveness of the proposed methodology.
The thermal behavior has sustainable impacts on the safety performance and cycle life of lithium-ion batteries. This article proposes a physics/data hybrid modeling framework for the thermal process of the pouch cell. Since the thermal effects of the tab area have different dynamic characteristics from other parts of the battery, a space decomposition strategy is designed to generate a more effective multiphysics-based model. To capture the battery thermal dynamics in real time, a reduced-order model is derived using the eigenfunction-based spectral expansion. All the unknown nonlinearities and other disturbances are further compensated through data-based learning. Simulation studies and experiments demonstrate the effectiveness of the proposed modeling methodology.
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