Monitoring and enhanced dynamic modeling of battery by genetic algorithm using LabVIEW applied in photovoltaic system

The dynamic modeling of batteries used in photovoltaic systems (PVS), exceptionally in stand-alone or microgrid systems, is considered as an important issue and a major problem for the monitoring and simulation and even for fault detection applications. In this work, we propose as first step an enhancement of CIEMAT model by the improvement of 21 charge and discharge parameters; also we propose an enhancement of 4 parameters for the estimation approximately of the gassing and the saturation levels by a new static method using genetic algorithm. Basically, this method relies on static measurements by charging of the battery with different constant currents to give a more accurate estimation of this area. Furthermore, a real-time interface system using LabVIEW using the improved model was proposed in this work to provide online estimation and measurements of all battery data and characteristics in PVS. The proposed robust and low-cost method of simulation and monitoring can be applied for the study of battery fault detection in PVS.

[1]  Ziyad M. Salameh,et al.  A mathematical model for lead-acid batteries , 1992 .

[2]  Jimi Tjong,et al.  Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries Part I: Parameterization Model Development for Healthy Batteries , 2014, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[3]  E. B. Gueguim Kana,et al.  Modeling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm. , 2012 .

[4]  Fardin Ahmadizar,et al.  Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm , 2015, Eng. Appl. Artif. Intell..

[5]  Jun Zheng,et al.  Multi-objective optimization of lithium-ion battery model using genetic algorithm approach , 2014 .

[6]  Jimi Tjong,et al.  Reduced-Order Electrochemical Model Parameters Identification and State of Charge Estimation for Healthy and Aged Li-Ion Batteries—Part II: Aged Battery Model and State of Charge Estimation , 2014, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[7]  S. Blaifi,et al.  An improved dynamic battery model suitable for photovoltaic applications , 2015, 2015 International Conference on Renewable Energy Research and Applications (ICRERA).

[8]  Dirk Uwe Sauer,et al.  Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination , 2013 .

[9]  Rudolf Scitovski,et al.  Solving the parameter identification problem of mathematical models using genetic algorithms , 2004, Appl. Math. Comput..

[10]  Rafael Illanes,et al.  Dynamic simulation and modelling of stand-alone PV systems by using state equations and numerical integration methods , 2014 .

[11]  Bernard Multon,et al.  Energy Modeling of a Lead-Acid Battery within Hybrid Wind/Photovoltaic Systems , 2003 .

[12]  Michael E. Webber,et al.  A dynamic model-based estimate of the value of a vanadium redox flow battery for frequency regulation in Texas , 2014 .

[13]  F. Chenlo,et al.  Lead/acid batteries for photovoltaic applications. Test results and modeling , 1994 .

[14]  A.L.S. Chan.  Generation of typical meteorological years using genetic algorithm for different energy systems , 2016 .

[15]  Bruce J. Tatarchuk,et al.  A simplified equivalent circuit model for simulation of Pb–acid batteries at load for energy storage application , 2011 .

[16]  Jun Xu,et al.  Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications , 2013 .

[17]  S. Barsali,et al.  Dynamical Models of Lead-Acid Batteries: Implementation Issues , 2002, IEEE Power Engineering Review.

[18]  Chee Burm Shin,et al.  Electrochemical model of a lithium-ion battery implemented into an automotive battery management system , 2015, Comput. Chem. Eng..

[19]  Ilhami Colak,et al.  An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications , 2016 .

[20]  Rui Liu,et al.  Numerical and analytical modeling of lithium ion battery thermal behaviors with different cooling designs , 2013 .

[21]  E. Lorenzo,et al.  A general battery model for PV system simulation , 1993 .

[22]  Teuku Meurah Indra Mahlia,et al.  Characterization of PV panel and global optimization of its model parameters using genetic algorithm , 2013 .

[23]  Mukesh Singh,et al.  Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications , 2014, IEEE Transactions on Energy Conversion.

[24]  F. J. Muñoz,et al.  Online Monitoring System for Stand-Alone Photovoltaic Applications—Analysis of System Performance From Monitored Data , 2012 .

[25]  A. Sellami,et al.  Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction , 2010 .

[26]  Daniel Guasch,et al.  Dynamic battery model for photovoltaic applications , 2003 .

[27]  Chen Shi,et al.  An approach of pig weight estimation using binocular stereo system based on LabVIEW , 2016, Comput. Electron. Agric..

[28]  Jianqiu Li,et al.  A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications , 2015 .

[29]  Dragoljub Kosanovic,et al.  Operational planning of combined heat and power plants through genetic algorithms for mixed 0-1 nonlinear programming , 2015, Comput. Oper. Res..

[30]  M. Ceraolo,et al.  New dynamical models of lead-acid batteries , 2000 .

[31]  Alireza Maheri,et al.  An accurate method for the PV model identification based on a genetic algorithm and the interior-point method , 2014 .