Hybrid Intelligent Model for Fault Detection of a Lithium Iron Phosphate Power Cell Used in Electric Vehicles

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• 关键词：Power cell ; Fault detection ; Battery ; Clustering ; Artificial neural networks ; Polynomial regression ; LS ; SVR
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9648
• 期：1
• 页码：751-762
• 全文大小：817 KB
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• 作者单位：Héctor Quintián (17)
  José-Luis Casteleiro-Roca (18)
  Francisco Javier Perez-Castelo (18)
  José Luis Calvo-Rolle (18)
  Emilio Corchado (17)
  
  17. Departamento de Informática y Automática, University of Salamanca, Plaza de la Merced s/n, 37008, Salamanca, Salamanca, Spain
  18. Departamento de Ingeniería Industrial, University of A Coruña, Avda. 19 de febrero s/n, 15495, Ferrol, A Coruña, Spain
  
• 丛书名：Hybrid Artificial Intelligent Systems
• ISBN：978-3-319-32034-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Currently, the electrical mobility and the intermittent power generation facilities problem are two of the main purposes of batteries. Batteries, in general terms, have a complex behavior. Due to the usual electrochemical nature of batteries, several tests are made to check their performance, and it is very useful to know a priori how they are working in each case. By checking the battery temperatures for a specific voltage and current value, this work describes a hybrid intelligent model aimed at making fault detection of a LFP (Lithium Iron Phosphate - LiFePO4) power cell type, used in Electric Vehicles. A large set of operating points is obtained from a real system to create the dataset for the operation range of the power cell. Clusters of the different behavior zones have been obtained to accomplish the solution. Some simple regression methods have been applied for each cluster. Polynomial Regression, Artificial Neural Networks and Support Vector Regression were the combined techniques to develop the hybrid intelligent model proposed. The novel hybrid model allows to be achieved good results in all the operating range, detecting all the faults tested.