Aljaidi Mohammad, Jangir Pradeep, Agrawal Sunilkumar P, Pandya Sundaram B, Parmar Anil, Anbarkhan Samar Hussni, Abualigah Laith
Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, 13110, Jordan.
Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602 105, India.
Sci Rep. 2025 Jan 11;15(1):1662. doi: 10.1038/s41598-024-83538-6.
Electrochemical energy conversion technologies include proton exchange membrane fuel cells (PEMFCs) where proton interchange is an alternative to diesel distributed generation, and PEMFCs are considered as a promising backup power source and a tool to regulate power consumption. Some of the major benefits of these PEMFCs especially in power system applications include low emission of carbon, fast load following capability, no noise and high start-up reliability. It is challenging to find the best PEMFC parameters because the model is complex and the problem is nonlinear; not all optimization algorithms can solve this problem. This paper presents a new approach that applies QUasi-Affine TRansformation Evolution algorithm with a new adaptation of Evolution Matrix and Selection operation (QUATRE-EMS) to determine optimal values of uncertain parameters in PEMFC stack references. The objective function of the optimization problem is defined as the sum of squared errors of the actual and predicted voltage data. The effectiveness of the proposed QUATRE-EMS algorithm is also checked through statistical analysis and the QUATRE-EMS variant is compared with other variants of DE optimization algorithms which are recently proposed in the state-of-the-art literature such as LSHADE, MadDE, CS-DE, LPalmDE, EDEV, jSO, SHADE, ISDE, and JADE. Results show that the QUATRE-EMS algorithm reduces SSE significantly, with an average SSE of 0.078492, which is 15% less than the best performing existing algorithms. QUATRE-EMS also achieved the lowest average values of absolute error, relative error and mean bias error among different PEMFC stack references, with accuracy improved by up to 20%. It was also computationally more efficient, cutting runtime in half compared to other methods. The results of these findings confirm the effectiveness and practicability of the QUATRE-EMS algorithm for improving the accuracy of BCS500W, NedStackPS6, SR12, H12, HORIZON, and Standard 250W PEMFC stack references.
电化学能量转换技术包括质子交换膜燃料电池（PEMFC），其中质子交换是柴油分布式发电的一种替代方式，并且PEMFC被视为一种有前景的备用电源以及调节电力消耗的工具。这些PEMFC的一些主要优点，特别是在电力系统应用中，包括低碳排放、快速的负载跟随能力、无噪音以及高启动可靠性。由于模型复杂且问题是非线性的，找到最佳的PEMFC参数具有挑战性；并非所有优化算法都能解决这个问题。本文提出了一种新方法，该方法应用具有进化矩阵和选择操作新适配的拟仿射变换进化算法（QUATRE - EMS）来确定PEMFC堆栈参考中不确定参数的最优值。优化问题的目标函数被定义为实际电压数据与预测电压数据的平方误差之和。还通过统计分析检查了所提出的QUATRE - EMS算法的有效性，并将QUATRE - EMS变体与近期在最前沿文献中提出的其他差分进化（DE）优化算法变体进行了比较，如LSHADE、MadDE、CS - DE、LPalmDE、EDEV、jSO、SHADE、ISDE和JADE。结果表明，QUATRE - EMS算法显著降低了均方误差（SSE），平均SSE为0.078492，比性能最佳的现有算法低15%。在不同的PEMFC堆栈参考中，QUATRE - EMS还实现了绝对误差、相对误差和平均偏差误差的最低平均值，精度提高了20%。它在计算上也更高效，与其他方法相比，运行时间减少了一半。这些研究结果证实了QUATRE - EMS算法在提高BCS500W、NedStackPS6、SR12、H12、HORIZON和Standard 250W PEMFC堆栈参考精度方面的有效性和实用性。
