A novel optimization approach using the Improved Parrot Optimizer (IPO) significantly enhances the accuracy of parameter estimation for proton exchange membrane fuel cells (PEMFCs). This renewable energy technology is gaining traction due to its high efficiency and potential to reduce emissions from traditional power sources.
Proton exchange membrane fuel cells have emerged as pivotal players in the shift toward sustainable energy, particularly valued for their ability to convert chemical energy from hydrogen and oxygen directly to electricity with minimal environmental impact. Yet, achieving optimal performance from these systems has been challenged by the difficulties of estimating various parameters necessary for accurate modeling and performance predictions.
Addressing this issue, the research introduces the IPO, which enhances traditional optimization methods by integrating two significant improvements: Opposition-Based Learning and Local Escaping Operator. These techniques refine the search process, enabling the algorithm to explore potential solutions more effectively and avoid local optima, which often traps conventional models.
Through empirical testing, the IPO demonstrated its capacity to achieve substantially improved results across three different PEMFC stacks—the NedStackPS6, BCS Stack, and Ballard Mark V. The corresponding sum of squared errors (SQE) was reduced to values of 2.065816 V, 0.012457 V, and 0.814325 V respectively, highlighting effective optimization of the PEMFC models. Remarkably, the IPO showed not just high accuracy but also efficiency gains, marked by improvements of 12.87% and reductions of 88.37% compared to previous methods.
The study explored the performance of the IPO through rigorous testing with benchmark functions and examined its application within real-world PEMFC systems, confirming its high reliability and adaptability. Findings underline the influence of optimal parameter estimation on the overall effectiveness of PEMFC applications, showcasing how enhanced models can facilitate broader adoption of this clean energy technology.
With continuous growth and investment directed toward renewable energy technologies, COP's successful implementation could significantly impact energy management strategies, providing new avenues for optimizing energy production and consumption. The research outcomes suggest possibilities for leveraging the IPO's methodologies beyond current PEMFC applications to other technologies, such as photovoltaic systems and microgrid energy management.
Feedback from sensitivity analysis reinforces the importance of accurate parameter estimation as it demonstrated how even minor deviations from optimal values could lead to substantial variations in performance. This aspect is particularly relevant for industries reliant on highly efficient energy conversion technologies, underscoring the necessity for precise operational guidelines.
Conclusively, the research presents not only methodological advances within the sphere of PEMFC modeling but also fosters optimism for future enhancements of renewable energy systems. The IPO sets the groundwork for enhancing energy-efficient technologies, paving the way for integrating reliable systems capable of meeting increasingly stringent energy demands and environmental standards.
By advancing the field of parameter optimization, innovative approaches like the IPO hold promise for optimizing existing energy systems and the future of sustainable energy technologies.