<![CDATA[Valve Regulated Lead-Acid (VRLA) batteries are widely used in energy storage systems due to their reliability and low cost; however, their energy utilization is strongly affected by discharge patterns and recovery behavior. Recent studies have shown that dynamic battery switching can improve extractable energy compared to static configurations, yet the switching time is commonly treated as a fixed parameter, despite experimental evidence indicating that the optimal switching interval depends on battery capacity and operating conditions. This paper proposes a fuzzy-based framework for adaptive switching time determination in VRLA battery systems, where switching duration is treated as an explicit control variable inferred from discharge–recovery characteristics. Key indicators, including voltage drop rate, voltage recovery magnitude, and relative internal resistance, are incorporated as inputs to a Mamdani-type fuzzy inference system, while the switching time is defined as the fuzzy output. The proposed approach enables adaptive adjustment of switching duration without relying on detailed electrochemical models. Simulation-based analysis is conducted to qualitatively evaluate the behavior of the proposed method in comparison with fixed switching strategies. The results demonstrate that fuzzy-based adaptive switching produces smoother switching time evolution and more stable voltage trends, indicating improved utilization of discharge–recovery dynamics. This study establishes a conceptual foundation for adaptive switching time control and provides a basis for future experimental validation and real-time implementation in intelligent battery management systems.]]>
