Dynamic data mining technique for rules extraction in a process of battery charging

被引:24
作者
Aliev, R. A. [1 ]
Aliev, R. R. [1 ]
Guirimov, B. [1 ]
Uyar, K. [1 ]
机构
[1] Azerbaijan State Oil Acad, Dept Comp Aided Control Syst, Baku, Azerbaijan
关键词
data mining; control rules; soft computing; fuzzy recurrent neural network; genetic algorithm; intelligent control; battery charging;
D O I
10.1016/j.asoc.2007.02.015
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
Battery charging controllers design and application is a growing industry direction. Fast and efficient charging of battery packs is a problem which is difficult and often expensive to solve using conventional techniques. The majority of existing works on intelligent charging systems are based on expert knowledge and heuristics. Not all features of the desired charging behavior can be attained by the hard- wired logic implemented by expert generated rules. Because the battery charging is a highly dynamic process and the chemical technology a battery uses varies significantly for different battery types, data mining technique can be of real importance for extracting the charging rules from the large databases, especially when the charging logic is to be continuously changed during the life of the battery dependent on the type and characteristics of the battery and utilization conditions. In this paper we use soft computing-based data mining technique for extraction of control rules for effective and fast battery charging process. The obtained rules were used for NiCd battery charging. The comparative performance evaluation was done among the existing charging control methods and the proposed system, which demonstrated a significant increase of performance (minimum charging time and minimum overheating) using the soft computing-based approach. (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:1252 / 1258
页数:7
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