Space-decomposition-based Spectral Modeling for Distributed Battery Thermal Dynamics

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)1634-1641
Journal / PublicationIEEE Transactions on Transportation Electrification
Issue number2
Online published2 Nov 2021
Publication statusPublished - Jun 2022


Thermal behavior has sustainable impacts on the safety performance and cycle life of lithium-ion batteries. This paper 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 multi-physics-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.

Research Area(s)

  • Batteries, Computational modeling, distributed parameter systems (DPSs), Eigenvalues and eigenfunctions, error compensation, Heating systems, modeling, Temperature sensors, Thermal conductivity, thermal variables measurement