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The process of employing optimization techniques to identify the optimum unknown variables appropriate for the creation of a precision fuel-cell performance forecasting model is known as parameter identification of a PEM fuel cell (PEMFC). The manufacturer's datasheet may not always provide these parameters, thus it is necessary to ascertain them to precisely estimate and forecast the fuel cell's performance. six unknown parameters of a PEMFC are computed using six optimization techniques: The Rime-Ice algorithm (RIME), the Grey Wolf Optimizer (GWO), the Moth Flam Optimizer (MFO), the Tunicate Swarm Algorithm (TSA), the Sine Cosine Algorithm (SCA), and the Osprey Optimization Algorithm (OOA). These six parameters serve as choice variables during optimization, and the sum square error (SSE) between the estimated and measured cell voltages is the fitness function that needs to be minimized. Ned Stack PS6, a real-world PEM fuel cell model, is used to verify the functionality of all comparator algorithms, including the suggested RIME method. The RIME algorithm yielded an SSE of 1.945417827, which was followed by MFO, GWO, TSA, SCA, and OOA. In addition, it was concluded that RIME's convergence speed was quicker than that of the other methods examined. The comparative analysis is conducted using the same dataset and the same computation burden for each of the several optimization techniques to provide a fair performance evaluation. The performance of the suggested RIME against the alternative optimization algorithms is further examined using statistical analysis. The results demonstrate a good degree of agreement between the experimental data and the estimated voltage-current curves produced by the suggested RIME.

The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model’s inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 × 10−04 ±0.0014, 1.3237 × 10−04±0.0043, and 6.6016 × 10−06±0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively.