This paper presents the application of an active energy management strategy to a hybrid system consisting of a proton exchange membrane fuel cell (PEMFC), battery, and supercapacitor. The purpose of energy management is to control the battery and supercapacitor states of charge (SOCs) as well as minimizing hydrogen consumption. Energy management should be applied to hybrid systems created in this way to increase efficiency and control working conditions. In this study, optimization of an existing model in the literature with different meta-heuristic methods was further examined and results similar to those in the literature were obtained. Ant lion optimizer (ALO), moth-flame optimization (MFO), dragonfly algorithm (DA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), particle swarm optimization (PSO), and whale optimization algorithm (WOA) meta-heuristic algorithms were applied to control the flow of power between sources. The optimization methods were compared in terms of hydrogen consumption and calculation time. Simulation studies were conducted in Matlab/Simulink R2020b (academic license). The contribution of the study is that the optimization methods of ant lion algorithm, moth-flame algorithm, and sine cosine algorithm were applied to this system for the first time. It was concluded that the most effective method in terms of hydrogen consumption and computational burden was the sine cosine algorithm. In addition, the sine cosine algorithm provided better results than similar meta-heuristic algorithms in the literature in terms of hydrogen consumption. At the same time, meta-heuristic optimization algorithms and equivalent consumption minimization strategy (ECMS) and classical proportional integral (PI) control strategy were compared as a benchmark study as done in the literature, and it was concluded that meta-heuristic algorithms were more effective in terms of hydrogen consumption and computational time.
Classical solution of Navier-Stokes equations with nonslip boundary condition leads to inaccurate predictions of flow characteristics of rarefied gases confined in micro/nanochannels. Therefore, molecular interaction based simulations are often used to properly express velocity and temperature slips at high Knudsen numbers (Kn) seen at dilute gases or narrow channels. In this study, an event-driven molecular dynamics (EDMD) simulation is proposed to estimate properties of hard-sphere gas flows. Considering molecules as hard-spheres, trajectories of the molecules, collision partners, corresponding interaction times, and postcollision velocities are computed deterministically using discrete interaction potentials. On the other hand, boundary interactions are handled stochastically. Added to that, in order to create a pressure gradient along the channel, an implicit treatment for flow boundaries is adapted for EDMD simulations. Shear-Driven (Couette) and Pressure-Driven flows for various channel configurations are simulated to demonstrate the validity of suggested treatment. Results agree well with DSMC method and solution of linearized Boltzmann equation. At low Kn, EDMD produces similar velocity profiles with Navier-Stokes (N-S) equations and slip boundary conditions, but as Kn increases, N-S slip models overestimate slip velocities.
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