PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation
Abstract
:1. Introduction
1.1. Fuel Cell Operation Principles
1.2. PEM Fuel Cell System Control
- Reactant Flow Subsystem
- Temperature Subsystem
- Water Management Subsystem
- Power Management Subsystem
2. Related Works
3. Materials and Methods
- Apply a feature selection algorithm to determine the variables needed to model and control the fuel cell voltage;
- Define the system inputs from the subset formed by the feature selection algorithm and try different regression algorithms to predict the output variable;
- Develop the inverse model of the fuel cell, turning the system inputs into outputs. The output of the regression model will become a system input.
- Integrate the inverse model with a PID neuro control to track the errors and tune the control signal to achieve the reference value of the system output. The reason why these two types of control are integrated is to modify the control signal by not only considering the error between the output variable and the reference value but also considering the state of the other variables in the transient state.
3.1. Experimental Setup
3.2. Feature Selection Algorithms and Data-Driven Models for Fuel Cells
- Filter methods measure the relevance of the variables by their correlations with the output variable;
- Wrapper methods create a subset of the original dataset using a training algorithm;
- Filter methods are much faster than wrapper and embedded methods;
- Wrapper methods can fall into overfitting;
- Embedded and wrapper methods capture feature dependencies while filters methods do not.
4. Results and Discussion
4.1. Fuel Cell Feature Selection
4.1.1. Filter Methods
4.1.2. Wrapper Methods
4.1.3. Embedded Methods
4.1.4. Principal Component Analysis (PCA)
- The air inlet and outlet pressure;
- The hydrogen inlet and outlet pressure; and
- The air inlet flow rate and hydrogen inlet flow rate.
4.2. Data-Driven Control-Oriented Models for PEM Fuel Cells
4.2.1. Fuel Cell Modeling Using Machine Learning Regression Algorithms
- RID: 0.840495 (0.075010)
- BYR: 0.840494 (0.075011)
- DTR: 0.815885 (0.131130)
- GBR: 0.860727 (0.124138)
- RFR: 0.830844 (0.120877)
4.2.2. Fuel Cell Modeling Based on Neural Networks
4.3. Hybrid Control Scheme
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Variable | Description | Unit |
---|---|---|
Time | Time aging | H |
Vout | Stack output voltage | V |
I | Current | A |
J | Current density | A/cm2 |
Tin, Tout H2 | Inlet and outlet H2 temperature | °C |
Tin, Tout Air | Inlet and outlet air temperature | °C |
Pin, Pout H2 | Inlet and outlet H2 pressure | mBar |
Fin, Fout H2 | Inlet and outlet H2 flow | L/min |
Fin, Fout Air | Inlet and outlet air flow | L/min |
Fwat | Flow rate of cooling water | L/min |
HrAIR | Inlet Hygrometry (Air) | % |
Parameter | Range |
---|---|
Air flow | 0 to 100 L/min |
H2 flow | 0 to 30 L/min |
Gas pressure | 0 to 2 bars |
Temperature | 20 to 80 °C |
Cell current | 0 to 300 A |
Variables | Type |
---|---|
Current | State variable |
Hydrogen inlet temp. | State variable |
Air inlet temp. | State variable |
Air inlet pressure | Input system |
Air outlet pressure | State variable |
Hydrogen inlet pressure | Input system |
Hydrogen outlet pressure | State variable |
Fold | Score |
---|---|
1 | 0.955929840882997 |
2 | 0.953074662444409 |
3 | 0.959505398134269 |
4 | 0.957813889951952 |
5 | 0.958048357252375 |
6 | 0.958116362163286 |
7 | 0.959355574634461 |
8 | 0.960026345242201 |
9 | 0.966767102061810 |
10 | 0.971974506261939 |
Ave. | 0.960061203902970 |
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Morán-Durán, A.; Martínez-Sibaja, A.; Rodríguez-Jarquin, J.P.; Posada-Gómez, R.; González, O.S. PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation. Processes 2019, 7, 434. https://doi.org/10.3390/pr7070434
Morán-Durán A, Martínez-Sibaja A, Rodríguez-Jarquin JP, Posada-Gómez R, González OS. PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation. Processes. 2019; 7(7):434. https://doi.org/10.3390/pr7070434
Chicago/Turabian StyleMorán-Durán, Andrés, Albino Martínez-Sibaja, José Pastor Rodríguez-Jarquin, Rubén Posada-Gómez, and Oscar Sandoval González. 2019. "PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation" Processes 7, no. 7: 434. https://doi.org/10.3390/pr7070434