Hybrid Metaheuristic Optimization for Accurate PEM Fuel Cell Parameter Estimation: Combining Grey Wolf Optimizer and Harris Hawks Optimization

Abstract

Abstract Accurate parameter identification is essential for predictive proton-exchange membrane fuel-cell (PEMFC) modelling, yet it remains challenging because commonly used semi-empirical formulations are strongly nonlinear and exhibit pronounced parameter coupling. This paper focuses on the optimisation engine rather than proposing a new PEMFC model. We introduce a sequential hybrid metaheuristic that combines the leader-guided exploration of the Grey Wolf Optimizer (GWO) with the adaptive exploitation mechanisms of Harris Hawks Optimization (HHO). The resulting method, denoted GWO--HHO, estimates the key parameters of a standard single-cell/stack model by minimising the sum of squared errors (SSE) between measured and simulated stack voltages. A unified identification protocol is applied to three public PEMFC benchmarks (Horizon 500~W, BCS 500~W, and Nedstack PS6), as well as to a suite of standard unimodal and multimodal test functions used to stress the optimiser itself. On the fuel-cell datasets, the proposed hybrid achieves SSE values of $0.0110$, $0.0116$, and $2.0655$, respectively, and exhibits smooth convergence with low variance across repeated runs. Compared with several recent optimisers under the same computational budget, GWO--HHO delivers competitive accuracy and robust behaviour across datasets. The method therefore provides a practical optimisation tool for reliable PEMFC parameter estimation using established physics-based models.

Key findings

• ▸The proposed hybrid method achieves SSE values of 0.0110, 0.0116, and 2.0655 on the Horizon 500~W, BCS 500~W, and Nedstack PS6 benchmarks respectively
• ▸The method exhibits smooth convergence with low variance across repeated runs
• ▸The hybrid approach outperforms individual optimization methods in terms of accuracy and robustness

Keywords

Identifiers

DOI

10.1088/2631-8695/ae4a6e

Journal

Engineering Research Express

Year

2026