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.
Some Remarks on the Application of RNN and PRNN for the Charge-Discharge Simulation of Advanced Lithium-Ions Battery Energy Storage / Capizzi, Giacomo; Bonanno, F.; Napoli, Christian. - (2012), pp. 941-945. (Intervento presentato al convegno 21st International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2012 tenutosi a Sorrento; Italy) [10.1109/SPEEDAM.2012.6264500].
Some Remarks on the Application of RNN and PRNN for the Charge-Discharge Simulation of Advanced Lithium-Ions Battery Energy Storage
NAPOLI, CHRISTIAN
2012
Abstract
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.File | Dimensione | Formato | |
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Bonanno_Some-Remarks_2012.pdf
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VE_2012_11573-1328661.pdf
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