ISOMAP-based spatiotemporal modeling for lithium-ion battery thermal process

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

26 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)569-577
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume14
Issue number2
Online published23 Aug 2017
Publication statusPublished - Feb 2018

Abstract

The real-time monitoring of temperature distribution in lithium-ion batteries (LIBs) is crucial for their safety and optimal operation in electrical vehicles. An accurate and effective thermal model is needed for online temperature monitoring since limited sensors are available in vehicle application. In this paper, a data-based spatiotemporal modeling method is researched for online estimation of temperature distribution of LIBs. First, Isometric Mapping (ISOMAP) method is used for time/space separation and model reduction. Then, the low-dimensional representation can be obtained in terms of ISOMAP based mapping functions. The unknown temporal dynamics in the low-dimensional space can be approximated using neural network model with parameters trained using extreme learning machine (ELM) algorithm. Finally, the spatiotemporal model of the thermal process can be reconstructed by integrating the neural network model and the mapping functions. The generalization bound of the proposed spatiotemporal model can be analyzed using Rademacher complexity. Simulation results showed that the proposed modeling method can model the LIB thermal process very well.

Research Area(s)

  • ISOMAP, Lithium-ion batteries (LIBs), Neural learning, Spatiotemporal modeling, Thermal process