Fast Modeling of Battery Thermal Dynamics Based on Spatio-temporal Adaptation

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

12 Scopus Citations
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Original languageEnglish
Pages (from-to)337-344
Journal / PublicationIEEE Transactions on Industrial Informatics
Issue number1
Online published8 Apr 2021
Publication statusPublished - Jan 2022


The thermal effect has a significant impact on the performance and durability of lithium-ion batteries. This paper proposes a systematic approach for fast modeling of the distributed battery thermal process. In the proposed method, a well-recognized time/space (T/S) separation is adopted to decompose the spatio-temporal thermal dynamics. Under the T/S separation, an incremental-learning-based regulator is first employed for the recursive update of spatial basis functions (SBFs), which can represent the most recent spatial complexity. Then, a corresponding temporal model with incremental adaptive characteristics is developed to capture the temporal non-linearity. Under such a fully adaptive spatio-temporal modeling pattern, the desired temperature distribution can be well reconstructed and predicted with higher efficiency and flexibility. According to the Rademacher complexity, the generalization bound of the proposed model is derived to ensure stable modeling performance. Experimental studies indicate that the proposed method can achieve satisfactory modeling performance while its computational efficiency is outstanding compared to peer methods.

Research Area(s)

  • Adaptation models, Batteries, Battery thermal process, Computational modeling, Data models, incremental adaptation, Mathematical model, Modeling, Numerical models, temperature estimation, time/space separation