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<p>This paper presents an adaptive battery charge control strategy that integrates fuzzy logic-based decision-making with Flexible Power Point Tracking (FPPT) for solar photovoltaic systems. Unlike conventional Maximum Power Point Tracking (MPPT) methods that prioritize maximum power extraction, the proposed approach dynamically adjusts the operating point based on battery State of Charge (SOC), available photovoltaic power, and load demand to prevent battery degradation from aggressive charging. The fuzzy logic controller employs fuzzification, inference, and defuzzification modules with triangular membership functions to generate adaptive charging commands. A comprehensive simulation study validates the controller under multiple load transition scenarios in a DC microgrid environment. Comparative analysis reveals that while MPPT achieves 2.3% higher charging efficiency, the proposed FPPT method reduces current fluctuations by 77.8% (from 8621 to 1923A peak-to-peak), voltage oscillations by 74.9% (from 61.57 to 15.46V), and achieves RMS voltage ripple reduction of 68.3%, thereby extending battery lifespan through gentler charging profiles. The trade-off analysis demonstrates that sacrificing marginal efficiency gains for enhanced battery health management provides net economic savings of approximately INR 3,000 per year per kW system in standalone photovoltaic applications where battery replacement costs exceed energy losses.</p>

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Battery charge controlling using fuzzy logic based flexible power point tracking

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月10日

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  • Jigneshkumar P. Desai,
  • Krutik Patel

摘要

This paper presents an adaptive battery charge control strategy that integrates fuzzy logic-based decision-making with Flexible Power Point Tracking (FPPT) for solar photovoltaic systems. Unlike conventional Maximum Power Point Tracking (MPPT) methods that prioritize maximum power extraction, the proposed approach dynamically adjusts the operating point based on battery State of Charge (SOC), available photovoltaic power, and load demand to prevent battery degradation from aggressive charging. The fuzzy logic controller employs fuzzification, inference, and defuzzification modules with triangular membership functions to generate adaptive charging commands. A comprehensive simulation study validates the controller under multiple load transition scenarios in a DC microgrid environment. Comparative analysis reveals that while MPPT achieves 2.3% higher charging efficiency, the proposed FPPT method reduces current fluctuations by 77.8% (from 8621 to 1923A peak-to-peak), voltage oscillations by 74.9% (from 61.57 to 15.46V), and achieves RMS voltage ripple reduction of 68.3%, thereby extending battery lifespan through gentler charging profiles. The trade-off analysis demonstrates that sacrificing marginal efficiency gains for enhanced battery health management provides net economic savings of approximately INR 3,000 per year per kW system in standalone photovoltaic applications where battery replacement costs exceed energy losses.