Thirumalai, P. and Ravi, R. and Kalaiselvi, N. (2007) Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells. Electrochimica Acta, 53. pp. 1877-1882. ISSN 0013-4686

PDF Restricted to Registered users only Download (602Kb) | Request a copy

Abstract

CoO anode, as an alternate to the carbonaceous anodes of lithium-ion cells has been prepared and investigated for electrochemical charge–discharge characteristics for about 50 cycles. Artificial neural networks (ANNs), which are useful in estimating battery performance, has been deployed for the first time to forecast and to verify the charge–discharge behavior of lithium-ion cells containing CoO anode for a total of 50 cycles. In this novel approach, ANN that has one input layer with one neuron corresponding to one input variable, viz., cycles [charge–discharge cycles] and a hidden layer consisting of three neurons to produce their outputs to the output layer through a sigmoid function has been selected for the present investigation. The output layer consists of two neurons, representing the charge and discharge capacity, whose activation function is also the sigmoid transfer function. In this ever first attempt to exploit ANN as an effective theoretical tool to understand the charge–discharge characteristics of lithium-ion cells, an excellent agreement between the calculated and observed capacity values was found with CoO anodes with the best fit values corresponding to an error factor of <1%, which is the highlight of the present study.

Item Type:	Article
Uncontrolled Keywords:	Artificial neural network; Back propagation; Lithium-ion cells; CoO anodes; Charge–discharge cycle
Subjects:	Lithium batteries
Divisions:	UNSPECIFIED
Depositing User:	ttbdar CECRI
Date Deposited:	26 Mar 2012 17:40
Last Modified:	26 Mar 2012 17:40
URI:	http://cecri.csircentral.net/id/eprint/635

Actions (login required)

View Item