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

Author

Bonanno, F. ; Capizzi, G. ; Napoli, C.

Author_Institution

Dept. of Electr., Electron. & Inf. Eng., Univ. of Catania, Catania, Italy

fYear

2012

fDate

20-22 June 2012

Firstpage

941

Lastpage

945

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.

Keywords

digital simulation; lithium; polymers; power engineering computing; recurrent neural nets; secondary cells; Li; PRNN simulation; SOC; advanced lithium-ions battery energy storage; charge-discharge simulation; commercial solid-polymer cell technology; pipelined recurrent neural network; soft computing technology; state of charge; voltage prediction; Batteries; Computer architecture; Microprocessors; Neurons; Pipeline processing; Recurrent neural networks; System-on-a-chip; Lithium-ions battery; Pipelined recurrent neural network; Recurrent neural network; State of charge;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2012 International Symposium on

Conference_Location

Sorrento

Print_ISBN

978-1-4673-1299-8

Type

conf

DOI

10.1109/SPEEDAM.2012.6264500

Filename

6264500