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.

Hybrid neural networks architectures for SOC and voltage prediction of new generation batteries storage / G, Capizzi; Bonanno, F.; Napoli, C.. - (2011), pp. 341-344. ( 3rd International Conference on Clean Electrical Power: Renewable Energy Resources Impact, ICCEP 2011 Ischia; Italy ) [10.1109/ICCEP.2011.6036301].

Hybrid neural networks architectures for SOC and voltage prediction of new generation batteries storage

C. NAPOLI
2011

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.
2011
3rd International Conference on Clean Electrical Power: Renewable Energy Resources Impact, ICCEP 2011
Neural Networks; Modelling; Simulations
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Hybrid neural networks architectures for SOC and voltage prediction of new generation batteries storage / G, Capizzi; Bonanno, F.; Napoli, C.. - (2011), pp. 341-344. ( 3rd International Conference on Clean Electrical Power: Renewable Energy Resources Impact, ICCEP 2011 Ischia; Italy ) [10.1109/ICCEP.2011.6036301].
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