Designing a control system based on SOC estimation of BMS for PV-Solar system

One of the major challenges forbattery energy stowage system is to design a supervisory controller whichcan yield high energy concentration, reducedself-discharge rate and prolong the battery lifetime. A regulatory PV-Battery Management System (BMS) based State of Charge (SOC) estimation is presented in this paper that optimally addresses the issues. The proposed control algorithm estimates SOC by Backpropagation Neural Network (BPNN) scheme and utilizesthe Maximum Power PointTracking(MPPT)schemeof the solar panels to take decision for charging, discharging or islanding mode of the Lead-Acid battery bank. A case study(SOC estimation) is demonstrated as well to depictthe efficiency(Error 0.082%) of the proposed modelusing real time data. The numerical simulation structured through real-time information concedes that the projected control mechanismis robust and accomplishes several objectives of integratedPV-BMS for instance avoiding overcharging and deep discharging manner under different solar radiations