Title
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.
Year
DOI
Venue
2019
10.1109/TII.2018.2866468
IEEE Transactions on Industrial Informatics
Keywords
Field
DocType
Spatiotemporal phenomena,Batteries,Computational modeling,Reduced order systems,Integrated circuit modeling,Evolutionary computation
Evolutionary algorithm,Swarm behaviour,Computer science,Rademacher complexity,Evolutionary computation,Algorithm,Control engineering,Basis function,Battery (electricity),Thermal distribution,Equivalent circuit
Journal
Volume
Issue
ISSN
15
5
1551-3203
Citations 
PageRank 
References 
1
0.35
0
Authors
3
Name
Order
Citations
PageRank
Xianbing Meng11224.60
Hanxiong Li22519157.48
Haidong Yang36212.34