Evolutionary Design of Spatio–Temporal Learning Model for Thermal Distribution in Lithium-Ion Batteries

Abstract

Temperature monitoring is indispensable to the optimal and safe operation of a lithium-ion battery. In this paper, a spatio–temporal learning model designed by evolutionary algorithm is proposed to predict the thermal distribution. To formulate the multicharacteristic 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.