To enhance efficiency in photovoltaic systems using optimization methods in battery management system

Abstract

Battery management systems (BMS) have undergone significant advancements over the years, transitioning from basic control strategies to sophisticated optimization algorithms. Historically, established BMS approaches were limited in their ability to handle energy efficiently, often leading to suboptimal power utilization and higher total harmonic distortion (THD). Integration of in a BMS application in modern days. advanced algorithms, such as flying squirrel search optimization (FSSO), have been optimized using grey wolf optimizer (GWO), and artificial neural network (ANN) significantly enhanced the performance of the system. These algorithms are efficient in terms of optimizing the duty cycle, whereby there is even a healthy energy flow between renewable load requirements and power requirements. These algorithms are analyzed, and their effectiveness in comparing their effectiveness is different operational scenarios. GWO algorithm is indicated as having the best reduction of THD and consequently improving the quality of power in a minimized way reliability. The ANN shows great expertise in voltage. regulating, low level of THD. Meanwhile, the FSSO algorithm shows significant potential in improving energy efficiency and reducing operational costs, despite its higher THD levels in current phases. GWO has a minimal current THD of 4.65% in Phase 1, exceeding ANN and FSSO. ANN has a voltage THD of 10.61%. FSSO has greater current THD values over 15% and good voltage control, highlighting decisions.