A Nonlinear Fuel Cell Model Based on Adaptive Neuro-Fuzzy Inference System

Qi Li; Wei Rong Chen; Zhi Xiang Liu; Shu Kui Liu; Wei Min Tian

doi:10.4028/www.scientific.net/AMM.321-324.1357

Paper Titles

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Design on a Micro-Grid System without Inverse-Power-Flow Based on Distributed Generation
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Equipment Uncertainty Assessment due to Voltage Sags Considering the Uncertainties of Invalidation Event and its Severity
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Parameter Matching Research of On-Board Synergic Electric Power Supply System Based on Secondary Development of ADVISOR
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A Nonlinear Fuel Cell Model Based on Adaptive Neuro-Fuzzy Inference System
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Application of Modified Artificial Fish Swarm Algorithm in Power System Reactive Power Optimization
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Investigation and Analysis on the Performance of the Oscillating Buoy Wave Power Device under the South China Sea
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Modeling of Temperature Characteristic of Hybrid Supercapacitor with Aqueous Electrolyte for Electric City Bus’ Power System
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Damping Improvement of Power System Oscillations by Using Optimal Coordinated Design between PSS and SVC-Based Stabilizer
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324A Nonlinear Fuel Cell Model Based on Adaptive...

A Nonlinear Fuel Cell Model Based on Adaptive Neuro-Fuzzy Inference System

Abstract:

A nonlinear model of proton exchange membrane fuel cell (PEMFC) based on an adaptive neuro-fuzzy inference system (ANFIS) is proposed to study different operational conditions effect on the dynamic response of Ballard 1.2kW Nexa power module. A hybrid learning algorithm combining back propagation (BP) and least squares estimate (LSE) is adopted to identify the parameters of input and output membership functions for the improvement of training efficiency in the ANFIS. The comparisons with the experimental data demonstrate that the obtained ANFIS model can efficiently approximate the dynamic output response of Nexa power module and is capable of predicting dynamic performance in terms of stack output voltage with a high accuracy.