Recurrent Neural Network-Based Modeling and Simulation of Lead-Acid Batteries Charge–Discharge
- 17 January 2011
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Energy Conversion
- Vol. 26 (2), 435-443
- https://doi.org/10.1109/tec.2010.2095015
Abstract
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.Keywords
This publication has 24 references indexed in Scilit:
- Adaptive online state-of-charge determination based on neuro-controller and neural networkEnergy Conversion and Management, 2010
- Ni–MH batteries state-of-charge prediction based on immune evolutionary networkEnergy Conversion and Management, 2009
- On-Line Detection Of State-Of-Charge In Lead Acid Battery Using Radial Basis Function Neural NetworkAsian Journal of Control, 2008
- A Design of a Grey-Predicted Li-Ion Battery Charge SystemIEEE Transactions on Industrial Electronics, 2008
- Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String SystemsIEEE Transactions on Industrial Electronics, 2008
- Static and Dynamic Neural NetworksPublished by Wiley ,2003
- Recurrent Neural Networks for PredictionPublished by Wiley ,2001
- Fuzzy-controlled Li-ion battery charge system with active state-of-charge controllerIEEE Transactions on Industrial Electronics, 2001
- Extension of battery life via charge equalization controlIEEE Transactions on Industrial Electronics, 1993
- A mathematical model for lead-acid batteriesIEEE Transactions on Energy Conversion, 1992