Title :
Hybrid neural networks architectures for SOC and voltage prediction of new generation batteries storage
Author :
Capizzi, G. ; Bonanno, F. ; Napoli, C.
Author_Institution :
Dept. of Electr., Electron. & Inf. Eng., Univ. of Catania, Catania, Italy
Abstract :
This paper presents some experiences and results obtained about the problem of the SOC and voltage prediction and simulation of new generation batteries. A complex pipelined recurrent neural network (PRNN) was designed for modeling of new generation batteries storage in order to predict the SOC and the terminal voltage. The simulation results are compared with experimental data obtained on commercial batteries.
Keywords :
battery chargers; electric charge; electric potential; power engineering computing; recurrent neural nets; secondary cells; complex pipelined recurrent neural network; hybrid neural network architecture; new generation battery storage; state of charge observer; voltage prediction; Batteries; Biological neural networks; Ions; Lithium; Pipeline processing; Recurrent neural networks; System-on-a-chip; Lithium ions batteries; hybrid neural network architectures; pipelined recurrent neural network (PRNN); recurrent neural networks (RNNs); state-of-charge (SOC) observer;
Conference_Titel :
Clean Electrical Power (ICCEP), 2011 International Conference on
Conference_Location :
Ischia
Print_ISBN :
978-1-4244-8929-9
Electronic_ISBN :
978-1-4244-8928-2
DOI :
10.1109/ICCEP.2011.6036301
