Some remarks on the application of RNN and PRNN for the charge-discharge simulation of advanced Lithium-ions battery energy storage

In this paper is reported a critical review, experiences and results about state of charge (SOC) and voltage prediction of Lithium-ions batteries obtained by recurrent neural network (RNN) and pipelined recurrent neural network (PRNN) based simulation. These soft computing technologies will be here...

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Bibliographic Details
Published in2012 International Symposium on Power Electronics, Electrical Drives, Automation and Motion pp. 941 - 945
Main Authors Bonanno, F., Capizzi, G., Napoli, C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2012
Subjects
Batteries
Computer architecture
Lithium-ions battery
Microprocessors
Neurons
Pipeline processing
Pipelined recurrent neural network
Recurrent neural network
Recurrent neural networks
State of charge
System-on-a-chip
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Summary:In this paper is reported a critical review, experiences and results about state of charge (SOC) and voltage prediction of Lithium-ions batteries obtained by recurrent neural network (RNN) and pipelined recurrent neural network (PRNN) based simulation. These soft computing technologies will be here presented, utilized and implemented to obtain the typical charge characteristics and the charge/discharge simulation procedure of a commercial solid-polymer technology based cell. Simulations are compared with experimental data manufacturers.
ISBN:9781467312998
1467312991
DOI:10.1109/SPEEDAM.2012.6264500