Eswaraiah Bongani, Balakrishna Kethineni
Vignan's Foundation for Science Technology and Research, Vadlamudi, India.
Sci Rep. 2024 Sep 16;14(1):21627. doi: 10.1038/s41598-024-72861-7.
As of now, all over the world is focusing on the Electric Vehicle (EV) technology because its features are low environmental pollution, less maitainence cost required, high robustness, and good dynamic response. Also, the EVs work continuously until the input fuel is provided to the fuel stack. Here, a Proton Exchange Membrane Fuel Cell (PEMFC) is used as an input source to the electric vehicle system because of its merits fast startup, and quick response. However, the PEMFC gives nonlinear voltage versus current characteristics. As a result, the extraction of maximum power from the fuel stack is very difficult. The main aim of this work is to study different Maximum Power Point Tracking Techniques (MPPT) for the DC-DC converter-fed PEMFC system. The studied MPPT controllers are Adjusted Step Value of Perturb & Observe (ASV with P&O), Adaptive Step Size with Incremental Conductance (ASS with IC), Radial Basis Functional Network (RBFN), Incremental Step-Fuzzy Logic Controller (IS with FLC), Continuous Step Variation based Particle Swarm Optimization (CSV with PSO), and Adaptive Step Value-Cuckoo Search Algorithm (ASV with CSA). The selected MPPT controllers' comprehensive study has been in terms of maximum power extraction, tracking speed of the MPP, settling time of the fuel stack output voltage, oscillations across the MPP, and design complexity. From the comprehensive performance results of the hybrid MPPT controllers, the ASV with CSA technique gives superior performance when equated to the other MPPT controllers.
截至目前，全世界都在关注电动汽车（EV）技术，因为其特点是环境污染低、所需维护成本少、鲁棒性高且动态响应良好。此外，电动汽车在向燃料电池堆提供输入燃料之前会持续工作。在此，质子交换膜燃料电池（PEMFC）被用作电动汽车系统的输入源，因为它具有启动快和响应迅速的优点。然而，PEMFC呈现出非线性的电压与电流特性。因此，从燃料电池堆中提取最大功率非常困难。这项工作的主要目的是研究用于DC-DC变换器馈电的PEMFC系统的不同最大功率点跟踪技术（MPPT）。所研究的MPPT控制器有扰动观察法的调整步长值（ASV with P&O）、增量电导法的自适应步长（ASS with IC）、径向基函数网络（RBFN）、增量步长模糊逻辑控制器（IS with FLC）、基于连续步长变化的粒子群优化（CSV with PSO）以及自适应步长值布谷鸟搜索算法（ASV with CSA）。对所选MPPT控制器的综合研究涉及最大功率提取、MPP的跟踪速度、燃料电池堆输出电压的稳定时间、MPP上的振荡以及设计复杂度。从混合MPPT控制器的综合性能结果来看，与其他MPPT控制器相比，ASV with CSA技术具有更优的性能。
