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

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.