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.