Polynomial Fitting-Based Performance Evaluation of a Battery-Powered Fuel-less Generator Prototype Using MATLAB
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This study extends earlier work on a 12 V battery-powered, fuel-less generator by applying polynomial regression in MATLAB to model and predict its performance under varying load conditions. Quadratic and cubic fits were developed for key metrics—efficiency, output voltage, motor current and battery runtime—using experimentally acquired data (10 W – 180 W). The best-fit model for efficiency vs. load was quadratic, achieving an R² = 0.982, while a cubic fit captured the non-linear behavior of runtime vs. load with R² = 0.996. The fitted equations enable rapid estimation of generator behavior for untested loads, highlight the optimal operating window (≈ 24 W – 60 W), and quantify performance degradation at heavier loads. The approach provides a low-cost analytical layer that can guide design tweaks or closed-loop control strategies in future iterations of the prototype.
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