Related Experiment Video
Updated: Jun 7, 2025
11:18
A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells
Published on: December 11, 2019
6.6K
A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter
Pradeep Jangir1,2,3,4,5, Absalom E Ezugwu6, Kashif Saleem7
1University Centre for Research and Development, Chandigarh University, Gharuan, 140413, Mohali, India.
Scientific Reports
|November 20, 2024
Summary
This study introduces a novel mathematical model for optimizing Proton Exchange Membrane Fuel Cells (PEMFCs) using an advanced Artificial Rabbits Optimization algorithm (MNEARO). MNEARO demonstrates superior performance in accuracy and efficiency for PEMFC parameter estimation.
Area of Science:
- Electrochemistry
- Computational Modeling
- Optimization Algorithms
Background:
- Accurate mathematical models are crucial for the simulation, control, and optimization of Proton Exchange Membrane Fuel Cells (PEMFCs).
- Existing models often require precise parameter estimation, which can be challenging.
- Empirical and semi-empirical equations combined with optimization techniques offer a viable approach for parameter estimation.
Purpose of the Study:
- To develop and validate a novel mathematical model for PEMFCs using advanced optimization techniques.
- To estimate unknown model parameters by minimizing the sum of squares error (SSE) between measured and estimated current and voltage values.
- To evaluate the performance of a new optimization algorithm, MNEARO, against existing methods for PEMFC modeling.
Main Methods:
- Development of a mathematical model for PEMFCs utilizing empirical/semi-empirical equations.
- Application of an advanced Artificial Rabbits Optimization algorithm (MNEARO) for parameter estimation and SSE minimization.
- Comparative analysis of MNEARO with other optimization algorithms (ARO, TLBO, DE, SSA) using SSE, Absolute Error (AE), and Mean Bias Error (MBE).
Main Results:
- The MNEARO algorithm demonstrated superior performance in both computational cost and solution quality compared to other optimization techniques.
- Experiments on six commercially available PEMFCs confirmed the effectiveness of the developed model and the MNEARO algorithm.
- Benchmark problem tests further validated the superiority of MNEARO over other meta-heuristic approaches.
Conclusions:
- The proposed MNEARO algorithm offers an efficient and accurate method for mathematical modeling and optimization of PEMFCs.
- The developed mathematical model, optimized by MNEARO, provides reliable parameter estimation for various commercial PEMFCs.
- MNEARO represents a significant advancement in meta-heuristic optimization for fuel cell technology.
