Extreme learning machine based spatiotemporal modeling of lithium-ion battery thermal dynamics

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

19 Scopus Citations
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Original languageEnglish
Pages (from-to)228-238
Journal / PublicationJournal of Power Sources
Publication statusPublished - 1 Mar 2015


Due to the overwhelming complexity of the electrochemical related behaviors and internal structure of lithium ion batteries, it is difficult to obtain an accurate mathematical expression of their thermal dynamics based on the physical principal. In this paper, a data based thermal model which is suitable for online temperature distribution estimation is proposed for lithium-ion batteries. Based on the physics based model, a simple but effective low order model is obtained using the Karhunen-Loeve decomposition method. The corresponding uncertain chemical related heat generation term in the low order model is approximated using extreme learning machine. All uncertain parameters in the low order model can be determined analytically in a linear way. Finally, the temperature distribution of the whole battery can be estimated in real time based on the identified low order model. Simulation results demonstrate the effectiveness of the proposed model. The simple training process of the model makes it superior for onboard application.

Research Area(s)

  • Extreme learning machine, Lithium-ion batteries, Spatiotemporal estimation, Thermal model