Paper Info
Title
Predicting the Aging Rate of Capacity in Ni/H Battery Using Artificial Neural Network
Abstract
Back-propagation artificial neural network was developed to study the relationship between the aging rates of capacity in Ni/H battery and alloying elements of cathode materials. Leave-one out method was used to train the ANN model. Test results showed that the prediction performance of the ANN model is satisfactory: the scatter dots distribute along the 0__45°diagonal line in the scatter diagram, the values of statistical criteria are 0.1195(MSE), 20.54%(MSRE), and 1.9144(VOF) respectively. Moreover, the ANN model was used to analyse the quantitative effects of chemical elements of cathode materials on the aging rate of capacity, results showed that the aging rate decreases with the increase of Ni content, increase with the increase of Co, Al and Si content, and change little with the change of La and Nd content.
Year
DOI
Venue
2009
10.1109/ISCID.2009.246
ISCID (2)
Keywords
Field
DocType
nd content,ni/h battery,rate decrease,cathode material,cathode materials,h battery,nickel,hydrogen,back-propagation artificial neural network,ann model,lanthanum,chemical elements,backpropagation,neodymium,backpropagation artificial neural network,co,aging rate,aluminium,silicon,statistical criteria,power engineering computing,si,nd,leave-one out method,artificial neural network,secondary cells,scatter diagram,scatter dot,cobalt,ni-h,si content,alloying elements,al,ni content,ageing,neural nets,artificial neural networks,back propagation,predictive models,aging,materials
Hydrogen,Thermodynamics,Aluminium,Pattern recognition,Computer science,Artificial intelligence,Battery (electricity),Backpropagation,Cathode,Artificial neural network
Conference
Volume
ISBN
Citations 
2
978-0-7695-3865-5
1
PageRank 
References 
Authors
0.41
2
6
Name
Order
Citations
PageRank
Wei You1114.20
Zhen Qiao210.41
Xiaoxia Li310.75
Fan Feng410.41
Weiei Huo510.41
Haibo Wang664.24