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Electromobility platform solar-assisted EVs and rail cars experience abrupt irradiance swings and frequent partial shading that depress PV energy yield and destabilize conventional MPPT schemes. We propose a hybrid MPPT that couples a rule-based fuzzy-logic controller with a lightweight artificial neural network (FL-ANN) to enhance tracking speed, accuracy, and robustness. A DC–DC boost converter enacts duty-cycle commands; in parallel, technology-specific ANNs (HIT, a-Si/c-Si tandem, a-Si, and CIGS) estimate MPP voltage and power. Each ANN is trained on more than five thousand field/logged samples per technology collected between Jan 2024 and Mar 2025. MATLAB/Simulink studies under Standard Test Conditions and dynamic scenarios irradiance steps, 25–40  \(^{\circ }\) C temperature sweeps, mobility-inspired partial shading, and sensor noise show consistent gains over standalone fuzzy logic: average tracking efficiency rises 98.71% at STC and 99.12% at 40  \(^{\circ }\) C; settling times shorten by 12–25%, and output ripple decreases across all tests. The FL-ANN-assisted controller has medium implementation complexity on low-cost microcontrollers, requires modest memory, and generalizes across four PV technologies without retuning. These attributes make it an implementation-ready solution aimed at maximizing energy capture in mobility-oriented PV systems.

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Design of an MPPT Algorithm Based on Neural Networks and Fuzzy Logic for Electromobility Applications

  • 会议论文

  • 发表日期：

    2026年

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  • Wilson Rivera,
  • Diana Fernandez,
  • Michael García,
  • José Angulo,
  • Jan Amaru Töfflinger,
  • Carlos Paragua-Macuri

摘要

Electromobility platform solar-assisted EVs and rail cars experience abrupt irradiance swings and frequent partial shading that depress PV energy yield and destabilize conventional MPPT schemes. We propose a hybrid MPPT that couples a rule-based fuzzy-logic controller with a lightweight artificial neural network (FL-ANN) to enhance tracking speed, accuracy, and robustness. A DC–DC boost converter enacts duty-cycle commands; in parallel, technology-specific ANNs (HIT, a-Si/c-Si tandem, a-Si, and CIGS) estimate MPP voltage and power. Each ANN is trained on more than five thousand field/logged samples per technology collected between Jan 2024 and Mar 2025. MATLAB/Simulink studies under Standard Test Conditions and dynamic scenarios irradiance steps, 25–40  \(^{\circ }\) C temperature sweeps, mobility-inspired partial shading, and sensor noise show consistent gains over standalone fuzzy logic: average tracking efficiency rises 98.71% at STC and 99.12% at 40  \(^{\circ }\) C; settling times shorten by 12–25%, and output ripple decreases across all tests. The FL-ANN-assisted controller has medium implementation complexity on low-cost microcontrollers, requires modest memory, and generalizes across four PV technologies without retuning. These attributes make it an implementation-ready solution aimed at maximizing energy capture in mobility-oriented PV systems.