title: Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms.
abstract: A hybrid evolutionary modeling algorithm (HEMA) is proposed to build the discharge lifetime models with multiple impact factors for battery systems as well as make predictions. The main idea of the HEMA is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model, while a GA is employed to optimize its parameters. The experimental results on lithium-ion batteries show that the HEMA works effectively, automatically and quickly in modeling the discharge lifetime of battery systems. The algorithm has some advantages compared with most existing modeling methods and can be applied widely to solving the automatic modeling problems in many fields.
authors.affiliation: State Key Laboratory of Software Engineering, Wuhan University, People's Republic of China. jxyu@whu.edu.cn
authors.forename: H
authors.initials: H
authors.lastname: Cao
authors.forename: J
authors.initials: J
authors.lastname: Yu
authors.forename: L
authors.initials: L
authors.lastname: Kang
authors.forename: H
authors.initials: H
authors.lastname: Yang
authors.forename: X
authors.initials: X
authors.lastname: Ai
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D011211:Electric Power Supplies; D008094:Lithium / Q000737:chemistry; D008962:Models, Theoretical; D016571:Neural Networks, Computer
chemical_list: D008094:Lithium