Preethiraj P M, J Belwin Edward
School of Electrical Engineering, Vellore institute of technology, Vellore, Tamil Nadu, India.
Sci Rep. 2024 Dec 30;14(1):31955. doi: 10.1038/s41598-024-83453-w.
The increasing concern about global warming and the depletion of fossil fuel reserves has led to a growing interest in alternative energy sources, particularly fuel cells (FCs). These green energy sources convert chemical energy into electrical energy, offering advantages such as quick initiation, high power density, and efficient operation at low temperatures. However, the performance of FCs is influenced by changes in operating temperature, and optimal efficiency is achieved by operating them at their maximum power point (MPP). This study uses Proton Exchange Membrane Fuel Cells (PEMFCs) to charge electric vehicles (EVs), amplifying the voltage generated by the FC using the Interleaved Boost-Cuk (IBC) converter. The optimal tracking of the maximum power output is achieved using the Improved Mayfly optimized (IMO) Cascaded Adaptive Neuro Fuzzy Inference System (Cascaded ANFIS). The study uses MATLAB to simulate the task in various settings and analyze the relevant performances, demonstrating enhanced efficiency and power tracking outputs. The proposed converter efficiency has improved to 94% with a minimal part count of 2 switched configurations. configuration. The applied control logic, in my opinion, Cascaded ANFIS is capable of operating the BLDC with an operational efficiency of 98.92%, including better output voltage generations of 350 V.
对全球变暖以及化石燃料储备枯竭的日益关注，引发了人们对替代能源，尤其是燃料电池（FC）的兴趣日益浓厚。这些绿色能源将化学能转化为电能，具有启动迅速、功率密度高以及在低温下高效运行等优点。然而，燃料电池的性能会受到运行温度变化的影响，通过在最大功率点（MPP）运行可实现最佳效率。本研究使用质子交换膜燃料电池（PEMFC）为电动汽车（EV）充电，利用交错式升压 - 库克（IBC）转换器放大燃料电池产生的电压。通过改进的蜉蝣优化（IMO）级联自适应神经模糊推理系统（级联ANFIS）实现最大功率输出的最优跟踪。该研究使用MATLAB在各种设置下模拟该任务并分析相关性能，展示了更高的效率和功率跟踪输出。所提出的转换器效率提高到了94%，开关配置最少为2种。我认为，所应用的控制逻辑——级联ANFIS能够以98.92%的运行效率操作无刷直流电机（BLDC），包括产生更好的350V输出电压。
