Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data

Pohjoranta, Antti; Halinen, Matias; Pennanen, Jari; Kiviaho, Jari

doi:10.1016/j.jpowsour.2014.11.126

Cited by 44 publications

(13 citation statements)

References 22 publications

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“…To solve the problem of maintaining the output voltage and fuel utilization of SOFCs, many advanced control strategies have been proposed, such as model predictive control (MPC) [11][12][13], fuzzy proportional-integral-derivative (PID) control [14], fuzzy logic control [15], and neural network control [16]. All of these control strategies have been shown to obtain excellent control performance in numerous simulation studies: However, due to their computational complexity, these control strategies are difficult to implement in practical applications.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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).

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…A thermal management model was proposed under different working conditions to keep the output performance of PEMFC stable [26]. There is a large volume of research concerning the cooling control strategy [27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Research on Control Algorithm of Proton Exchange Membrane Fuel Cell Cooling System

Lin

Yang

et al. 2019

Energies

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The proton exchange membrane fuel cell (PEMFC) is taken to be the ultimate technical direction of vehicle power. Cooling system is a key component which directly affects the fuel cell performance, reliability and durability. For the purpose of keeping accurate temperature control under dynamic loads and achieving rapid warm-up control during cold-start, a 35 kW PEMFC’s cooling system dynamic model is established and validated by experiments firstly. According to the simulation results, the model can well be fitted to the actual system. Then an integrate separate PID (Proportional-Integral-Derivative) algorithm and cooling fan prestart strategy is proposed. The result shows that it can effectively reduce the temperature overshoot under dynamic loads. In view of the thermostat mechanical characteristics tend to cause large temperature fluctuation during warm-up process, a thermostat control strategy is proposed to reduce the temperature fluctuation from 7.5 °C to 0.4 °C.

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“…In recent years, numerous studies on the control of SOFC systems have been published. Generally, these studies mainly attempted to address three control targets of the SOFC: (1) maintaining a rapid flow of power to address the power demand [1,2,[6][7][8][16][17][18][19][20]; (2) keeping the crucial components, such as the stack, burner, and exchanger temperature below the safety operation limits [5,8,16,[21][22][23][24][25]; and (3) ensuring high levels of fuel utilization and system efficiency [5,8,21,23,26,27]. For the optimal operation of the SOFC system, an integral control scheme with rapidness, efficiency, easy application, and robustness must be designed to handle the control targets [28].…”

Section: Introductionmentioning

confidence: 99%

Control System Based on Anode Offgas Recycle for Solid Oxide Fuel Cell System

Zhan

Yang

2018

Mathematical Problems in Engineering

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The conflicting operation objectives between rapid load following and the fuel depletion avoidance as well as the strong interactions between the thermal and electrical parameters make the SOFC system difficult to control. This study focuses on the design of the decoupling control for the thermal and electrical characteristics of the SOFC system through anode offgas recycling (AOR). The decoupling control system can independently manipulate the thermal and electrical parameters, which interact with one another in most cases, such as stack temperatures, burner temperature, system current, and system power. Under the decoupling control scheme, the AOR is taken as a manipulation variable. The burner controller maintains the burner temperature without being affected by abrupt power change. The stack temperature controller properly coordinates with the burner temperature controller to independently modulate the stack thermal parameters. For the electrical problems, the decoupling control scheme shows its superiority over the conventional controller in alleviating rapid load following and fuel depletion avoidance. System-level simulation under a power-changing case is performed to validate the control freedom between the thermal and electrical characteristics as well as the stability, efficiency, and robustness of the novel system control scheme.

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Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data

Cited by 44 publications

References 22 publications

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

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

Research on Control Algorithm of Proton Exchange Membrane Fuel Cell Cooling System

Control System Based on Anode Offgas Recycle for Solid Oxide Fuel Cell System

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