Adaptive Power Management in Fuel Cell Driven Electric Rickshaw Using Neural Network Controlled Interleaved DC-DC Converter

Efficient power management in hydrogen-powered light electric vehicles demands precise coordination between the fuel cell energy source, power conversion stage, and motor drive system. This paper investigates the performance of a neural network-based adaptive power management strategy for a Proton Exchange Membrane Fuel Cell (PEMFC) powered electric rickshaw, implemented through a six-phase interleaved DC-DC boost converter controlled by a Function Fitting Neural Network (fitnet) trained using the Levenberg-Marquardt optimisation algorithm. The neural network continuously maps real-time PEMFC terminal voltage and output current to an optimal pulse-width modulation duty cycle, enabling dynamic adaptation of the converter operating point to maximise fuel cell stack utilisation efficiency. The six-phase interleaved architecture is employed to achieve high voltage conversion ratio and substantially attenuate input current ripple, thereby protecting the fuel cell membrane electrode assembly from harmful high-frequency current stress. The converted energy is delivered to a Brushless DC (BLDC) motor through a three-phase voltage source inverter governed by a Hall effect sensor-based six-step commutation controller. Comprehensive simulation studies in MATLAB/Simulink demonstrate that the neural network power management strategy elevates PEMFC stack efficiency from 42.65% to 78.54% relative to a conventional fixed duty cycle approach, while simultaneously improving electromagnetic torque quality and reducing stator current harmonic distortion. The comparative analysis confirms the technical feasibility and performance advantages of the proposed adaptive power management framework for fuel cell electric rickshaw propulsion.