Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms

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@Article{cao:2001:CC,

• author = "Hongqing Cao and Jingxian Yu and Lishan Kang and Hanxi Yang and Xinping Ai",
• title = "Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms",
• journal = "Computers \& Chemistry",
• year = "2001",
• volume = "25",
• number = "3",
• pages = "251--259",
• month = may,
• keywords = "genetic algorithms, genetic programming, Discharge lifetime of battery systems, Lithium-ion battery, Hybrid evolutionary modelling",
• ISSN = "0097-8485",
• DOI = "doi:10.1016/S0097-8485(00)00099-1",
• 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 optimise 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 modelling the discharge lifetime of battery systems. The algorithm has some advantages compared with most existing modelling methods and can be applied widely to solving the automatic modelling problems in many fields.",
• notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/627320/description#description",

}

Genetic Programming entries for Hong-Qing Cao Jingxian Yu Li-Shan Kang Hanxi Yang Xinping Ai

Citations