Implementation of Adaptive Neuro Fuzzy Controller for Fuel Cell Based Electric Vehicles

Abstract

The global concern for clean energy generation paved the way for technological inventions and provided scope for researchers. More prominently, integration of heterogeneous renewable sources, storage systems, and electric vehicles became the pioneer solutions. In this article, a soft computing based ANFIS method has been proposed to execute the rapid speed response in electric vehicle. Here, Brushless DC motor was used as a propulsion system to drive the vehicle. Electric Vehicle is basically a time variant system, whose operating parameters and road conditions vary continuously. To address these uncertainties, a novel control strategy is proposed. The fuel cell battery is used as the auxiliary power supply for the electric vehicle. To demonstrate the performance of the controllers, a case study has been considered with parameter uncertainties for an ECE-15 test cycle. To evaluate the proficiency of the proposed soft computing control method, the speed response results are evaluated and compared with existing methods like conventional PI and fuzzy based tuned PID controllers. In addition, the performance of proposed technique is benchmarked with other controllers reported in the literature.

Keywords

• Electric Vehicle
• BLDC Motor
• Speed control
• Fuzzy PID Controller

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