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

Yuxiao Qin, Guodong Zhao, Qingsong Hua, Li Sun and Soumyadeep Nag
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Yuxiao Qin: Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
Guodong Zhao: School of Information Engineering, Ningxia University, Yinchuan 750021, China
Qingsong Hua: College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875, China
Li Sun: Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
Soumyadeep Nag: Department of Electrical & Computer Engineering, Baylor University, Waco, TX 76798, USA

Sustainability, 2019, vol. 11, issue 12, 1-20

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).

Keywords: Solid oxide fuel cells (SOFCs); PID control; genetic algorithm (GA) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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