Recurrent Neural Network-Based Modeling and Simulation of Lead-Acid Batteries Charge-Discharge

被引：45

作者：

Capizzi, Giacomo ^{[1
]}

Bonanno, Francesco ^{[1
]}

Tina, Giuseppe M. ^{[1
]}

机构：

[1] Univ Catania, Dept Elect Elect & Syst Engn, I-95125 Catania, Italy

来源：

IEEE TRANSACTIONS ON ENERGY CONVERSION | 2011年 / 26卷 / 02期

关键词：

Lead-acid batteries; mathematical modeling; recurrent neural networks (RNNs); state-of-charge (SOC) observer; STATE-OF-CHARGE; ENERGY-STORAGE; SYSTEM; PATTERN; DESIGN;

D O I：

10.1109/TEC.2010.2095015

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

080707 [能源环境工程]; 082001 [油气井工程];

摘要：

This paper presents the main experiences and results obtained about the problem of the lead-acid battery modeling and simulation. A nonlinear mathematical model is presented as well as results of neuroprocessing of the charge-discharge experimental and simulated data. Recurrent neural networks were used to provide a state-of-charge observer and model parameter estimation and tuning. The simulation results are compared with those obtained by extensive lab tests performed on different batteries used for electric vehicle and photovoltaic application.

引用

页码：435 / 443

页数：9

共 29 条

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Battery choice and management for new-generation electric vehicles [J].