Optimization of Ni-MH Battery Fast Charging in Electric Vehicles Using Dynamic Data Mining and ANFIS

Authors:
Guifang Guo;Peng Xu;Zhifeng Bai;Shiqiong Zhou;Gang Xu;Binggang Cao
Affiliations:
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China 710049;School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China 710049;School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China 710049;School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China 710049;School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China 710049;School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China 710049
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

Fast and efficient charging of Ni-MH battery is a problem which is difficult and often expensive to solve using conventional techniques. This study proposes a method that the integrated data mining algorithm and the Adaptive Network Fuzzy Inference Systems (ANFIS) for discovering the fast charging more efficiently and presenting it more concisely. Because the battery charging is a highly dynamic process, dynamic data mining technique is used for extracting of control rules for effective and fast battery process. The ideal fast charging current has been obtained. The result indicates that the integrated method of adaptive charging current has effectively improved charging efficiency and avoided overcharge and overheating.