Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India.
Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India.
Environ Sci Pollut Res Int. 2021 Jul;28(26):34511-34526. doi: 10.1007/s11356-021-13097-0. Epub 2021 Mar 2.
In recent years, proton exchange membrane fuel cells (PEMFCs) have been known to be a viable method for meeting the electrical energy needs, thereby enhancing the overall reliability of renewable energy systems. PEMFCs demonstrate various promising attributes like pollution-free, totally sustainable, non-self-discharging. These need hydrogen as fuel, and air for their operation, while the final product is pure water only. Thus, under varying operating conditions, the appropriate modeling and parameter optimization of PEMFCs have gained considerable importance in recent times. The evolutionary optimization approaches had been utilized in recent past for estimating PEMFCs parameters as exact modeling of the same does not exist in the literature. For the evaluation of PEMFCs performance criteria, a newly proposed algorithm is developed in this manuscript i.e. black widow optimization (BWO). Firstly, the performance of this proposed algorithm is checked by complex benchmark results. After that, this proposed algorithm is applied to extract the parameters of PEMFCs models under different operating temperatures. The parameter optimization results are obtained using BWO and are further compared with those obtained with five other algorithms, i.e., particle swarm optimization (PSO), multi-verse optimizer (MVO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), and grey wolf optimization (GWO). The complete error analysis is carried out for the two data sheets of the PEMFCs to establish the superiority of BWO. It has been observed that the developed proposed algorithm gives better results when compared to those obtained with rest of the algorithms considered in this work. After calculating the error, non-parametric test is performed which suggests that the BWO is better than the rest of the compared algorithms.
近年来，质子交换膜燃料电池（PEMFC）已被证明是满足电能需求的一种可行方法，从而提高了可再生能源系统的整体可靠性。PEMFC 具有无污染、完全可持续、不自放电等各种有前途的特性。它们需要氢气作为燃料，空气作为其运行的氧气，而最终产物只有纯水。因此，在不同的工作条件下，PEMFC 的适当建模和参数优化在最近得到了相当大的重视。进化优化方法在最近的过去被用于估计 PEMFC 参数，因为文献中不存在对其进行精确建模的方法。为了评估 PEMFC 的性能标准，本文提出了一种新的算法，即黑寡妇优化（BWO）。首先，通过复杂的基准结果检查该算法的性能。然后，将该算法应用于在不同工作温度下提取 PEMFC 模型的参数。使用 BWO 获得参数优化结果，并与其他五种算法（粒子群优化算法（PSO）、多宇宙优化算法（MVO）、正弦余弦算法（SCA）、鲸鱼优化算法（WOA）和灰狼优化算法（GWO））的结果进行比较。对 PEMFC 的两个数据表进行了完整的误差分析，以确定 BWO 的优势。结果表明，与本文中考虑的其他算法相比，开发的算法给出了更好的结果。在计算误差后，进行了非参数检验，表明 BWO 优于其他比较算法。
