Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

Abstract

Nowadays, given the great deal of fossil fuel consumption and associated environmental pollution, solid oxide fuel cells (SOFCs) have shown their great merits in terms of high energy conversion efficiency and low emissions as a stationary power source. To ensure power quality and efficiency, both the output voltage and fuel utilization of an SOFC should be tightly controlled. However, these two control objectives usually conflict with each other, making the controller design of an SOFC quite challenging and sophisticated. To this end, a multi-objective genetic algorithm (MOGA) was employed to tune the proportional–integral–derivative (PID) controller parameters through the following steps: (1) Identifying the SOFC system through a least squares method; (2) designing the control based on a relative gain array (RGA) analysis; and (3) applying the MOGA to a simulation to search for a set of optimal solutions. By comparing the control performance of the Pareto solutions, satisfactory control parameters were determined. The simulation results demonstrated that the proposed method could reduce the impact of disturbances and regulate output voltage and fuel utilization simultaneously (with strong robustness).

Design and Experimental Validation of the Temperature Control of a PEMFC Stack by Applying Multiobjective Optimization

“…In these works, various design parameters are optimized by using economic, environmental and energy efficiency indicators. Special mention deserves [34], which, methodologically, is similar to our research. In this work, multiobjective optimization is applied to tune the controller parameters of a SOFC stack.…”

Performance Evaluation of Balancing Bicopter using P, PI, and PID Controller

“…It can stabilize the roll angle under an impulse disturbance faster than P and PID controller tuned by Ziegler-Nichols. To maximize the response, especially improving the overshoot performances, more advanced tuning methods, such as involving genetic algorithm [20], particle swarm optimization [21], iterative-tuning method [22] or another optimization approach may be necessary. It is also possible to create a more robust controller after utilizing artificial intelligence in the control parameter tuning process [23].…”

Online characterization of operational parameters in an SOFC system with anode‐exhaust gas recirculation by oxygen sensors

“…The reduction of greenhouse gases in order to limit and counteract climate change poses currently one of the major challenges. One way to do so is to replace con-ventional energy converters with a high carbon footprint and switch to innovative technologies with higher efficiency and therefore lower or even zero carbon footprint [1,2]. A promising technology in this regard represent fuel cells [2][3][4].…”

“…One way to do so is to replace con-ventional energy converters with a high carbon footprint and switch to innovative technologies with higher efficiency and therefore lower or even zero carbon footprint [1,2]. A promising technology in this regard represent fuel cells [2][3][4]. A fuel cell converts chemical energy of Fuel Cells.…”

“…An SOFC consists of an ionconduction ceramic electrolyte and nonprecious metals as catalyst material of the electrode, leading to a simplified construction of the cell due to the absence of a complex electrolyte management and lower costs [5]. Further advantages are fuel flexibility and fuel pre-processing with reduced complexity [2,[5][6][7]. At system level, an SOFC system with anode-exhaust gas recirculation (AEGR) has the highest potential of electrical efficiency [8].…”