This study proposes a novel metaheuristic-based technique for optimum parameters' estimation of the proton exchange membrane fuel cells (PEMFCs).To provide better results with more reliability and accuracy, a Fractional Order-based design of the Whale Optimizer Algorithm (FO-WOA) is designed.A validation test showed that the proposed method provides a good trade-off between accuracy and convergence speed. Performing the algorithm for multiple independent runs also shows that the proposed method delivers reliable results toward some other comparative metaheuristic algorithms. This algorithm is then used for the minimization of the sum of square deviation between the experimental voltage-current polarization and the optimal achieved results by the model based on FO-WOA. The method is validated by considering two practical case studies, which are the Nexa PEMFC and 250 W PEM system, and its achievements are put in comparison with some approaches to indicate the higher effectiveness of the proposed method toward the others. The experimental results on the Nexa PEMFC indicate that the proposed FO-WOA-based method with 12 sum square errors (SSE) provided the minimum error toward the other. Also, the experiments on the 250 W PEM Stack indicated that for 250 W PEM Stack with 3/5 bar and 80°C, the proposed method with 0.01 SSE provides the fittest profile with the experimental data, and finally, the experiments on the same stack with 3/5 bar and 80°C showed the higher accuracy of the proposed method with the least SSE value (0.16) toward the others.

Summary Improving the electricity output of a propulsion configuration of an unmanned aerial vehicle (UAV) can be effective in achieving the targets of controlling fuel consumption and high flight endurance. Recently, fuel cell‐powered UAVs have been developed to achieve these goals. In addition, the use of an integrated fuel cell‐based electric propulsion system can further improve power generation rate. On the other hand, due to the limited reports on fuel cell‐powered UAVs (especially solid oxide fuel cell [SOFC]), it is necessary to conduct more and detailed studies. In this regard, the present article provides a conceptual analysis of a SOFC/thermionic generator (TIG)/thermoelectric generator (TEG) integrated propulsion configuration to produce electric energy of a small UAV. SOFC stack converts chemical energy into electric current and thermal energy. The thermal energy of the stack's exhaust is converted into electric power under two processes in downstream generators. The objective of this work is to calculate the needful power of an UAV at desired mission requirements. The present study presents a new propulsion configuration. In addition, the proposed system's design and modeling as well as provided results are in such a way that the propulsion system can be generalized for any desired size of UAV. The finding revealed that the introduced propulsion configuration could produce nearly 553.7 W of electricity. Moreover, the efficiency of electricity production was close to 49.3%. It was also found that the drone requires nearly 1.3 kW of electric power to perform the intended mission. According to the conceptual evaluation of the proposed propulsion configuration, five scenarios are also suggested to provide the necessary power to the UAV.

With the rapid development of Internet of Vehicles, computing-intensive and delay-sensitive applications are widely used. Faced with the shortcomings of less computing resources and limited power supply of vehicle mobile terminals, mobile edge technology came into being. Firstly, a multi-terminal single-edge vehicle network model is established to alleviate the terminal pressure by accessing Multi-Access Edge Computing ( MEC ) servers. Aiming at the heterogeneous computing network, this paper constructs a joint optimization problem of task offloading and resource allocation under the constraints of delay and energy consumption. Aiming at the problem that the resources in the offloading decision process exceed the load, the modified chromosome and the improved elite selection strategy are used to optimize the genetic algorithm. The dynamic parameter adjustment strategy is used to optimize the particle swarm optimization algorithm to prevent premature convergence. A two-stage joint optimization algorithm is proposed to solve the problem. It can be seen from the results that the optimized algorithm can find the appropriate optimal solution and effectively reduce the cost compared with GAVECOS and Partial algorithm.