Title :
The Estimation of the Capacity of Lead-Acid Storage Battery Using Artificial Neural Networks
Author :
Chen, Chao-Rong ; Huang, Kuo Hhua ; Teng, Hsiang Chung
Author_Institution :
Nat. Taipei Univ. of Technol., Taipei
Abstract :
The capacity of lead-acid storage battery for communication system has been long estimated by constant current discharge method in the past. It spends a lot of time and labor and wastes more energy. This paper proposes a new method combining the measured data of battery discharge and the back-propagation neural network. After they are trained and learned, the back-propagation neural network can estimate the capacity of lead-acid storage battery after half hour discharge test. Therefore, the advantages of this paper are less discharge time of storage battery, less working hour and saving energy. The practical results show that the method has good performances.
Keywords :
backpropagation; lead acid batteries; neural nets; power engineering computing; artificial neural networks; back-propagation neural network; constant current discharge method; lead-acid storage battery capacity; Artificial neural networks; Batteries; Cybernetics; Dielectrics and electrical insulation; Electrodes; Gravity; Lead compounds; Neural networks; Power supplies; Voltage; Back-propagation Artificial neural network (BP ANN); Battery Capacity; Lead-Acid Storage Battery;
Conference_Titel :
Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
1-4244-0099-6
Electronic_ISBN :
1-4244-0100-3
DOI :
10.1109/ICSMC.2006.384942