Evolutionary design of spatio-temporal leaning model for thermal distribution in lithium-ion batteries

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

23 Scopus Citations
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Original languageEnglish
Pages (from-to)2838-2848
Journal / PublicationIEEE Transactions on Industrial Informatics
Issue number5
Online published21 Aug 2018
Publication statusPublished - May 2019


The temperature monitoring is indispensable to the optimal and safe operation of the lithium-ion battery. In this paper, a spatiotemporal learning model designed by evolutionary algorithm is proposed to predict the thermal distribution. To formulate the multi-characteristic spatial dynamics, the chicken swarm optimization based fusion of different dimensionality-reduction methods is proposed for learning spatial basis functions. Through integration with the time/space separation based approach and equivalent circuit model based thermal model, the reduced-order model is derived. The related parameters of the reduced-order model are identified by integrating chicken swarm optimization with time/space separation based approach. A Bayesian regularized neural network based compensation model is developed to compensate for the model errors caused by the spatio-temporal coupled dynamics. Based on the Rademacher complexity, the generalization bound of the proposed model is analyzed. Simulations and comparisons demonstrate the superiority of the proposed model.

Research Area(s)

  • Batteries, chicken swarm optimization, compensation model, Computational modeling, Evolutionary computation, Integrated circuit modeling, Reduced order systems, reduced-order model, spatiotemporal modeling, Spatiotemporal phenomena, Thermal distribution, time/space separation based approach