Improving a Fuel Cell System’s Thermal Management by Optimizing Thermal Control with the Particle Swarm Optimization Algorithm and an Artificial Neural Network
Abstract
:1. Introduction
2. Experiment
3. Model Development
3.1. Model Structure
3.2. Dynamic Mechanistic Model
3.2.1. Stack Voltage Model
3.2.2. Water Flow in the Cathode and Anode
- (1)
- Gas behaves as an ideal gas and conforms to the ideal gas law.
- (2)
- The gas temperature inside the flow channel equals the stack temperature.
- (3)
- The temperature, pressure, and humidity inside the flow channel and at the exit are equal.
- (4)
- When the relative humidity of the gas is above 100%, the vapor condenses to liquid form but does not leave the stack. The liquid water evaporates into the gas or accumulates in the flow channel.
- (5)
- The flow channel volume is constant.
3.2.3. Membrane Hydration
- (1)
- The water mass flow is distributed uniformly across the membrane.
- (2)
- The membrane water content is the average of the cathode water content and anode water content , , and can be expressed as
3.2.4. Stack Heat Exchange
3.2.5. Radiator
3.2.6. Piping
3.3. Control Model
4. Results and Discussion
4.1. Model Validation
4.2. Parametric Effect Analysis of Temperature
4.3. Training Results
4.4. Control Model Effectiveness
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value |
---|---|
Stack cell number | 140 |
Material of bipolar plate | Graphite |
Active area | 406 cm2 |
Thickness of PEM | 0.0018 cm |
Parameter | Value |
---|---|
Load current | 467 A-487 A-507 A |
Coolant flow rate | 90 L/Min |
Relative humidity | 95% |
Cathode inlet pressure | 40 kPa |
Anode inlet pressure | 50 kPa |
Ambient temperature | 20 °C |
Target temperature | 65 °C |
Maximum temperature | 68 °C |
Parameter | Symbol | Parameter | Symbol |
---|---|---|---|
Operating temperature | Cell number | ||
O2 inlet partial pressure | Water’s molar mass | ||
H2 inlet partial pressure | Cathode flow channel volume | ||
Load current | Vapor’s gas constant | ||
Cathode inlet pressure | PEM thickness |
Parameter | Symbol | Step | Interval |
---|---|---|---|
Load current | 20 A | 0~550 A | |
Coolant flow rate | 10 L/Min | 80~130 SLPM |
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Deng, B.; Zhang, X.; Yin, C.; Luo, Y.; Tang, H. Improving a Fuel Cell System’s Thermal Management by Optimizing Thermal Control with the Particle Swarm Optimization Algorithm and an Artificial Neural Network. Appl. Sci. 2023, 13, 12895. https://doi.org/10.3390/app132312895
Deng B, Zhang X, Yin C, Luo Y, Tang H. Improving a Fuel Cell System’s Thermal Management by Optimizing Thermal Control with the Particle Swarm Optimization Algorithm and an Artificial Neural Network. Applied Sciences. 2023; 13(23):12895. https://doi.org/10.3390/app132312895
Chicago/Turabian StyleDeng, Bo, Xuefeng Zhang, Cong Yin, Yuqin Luo, and Hao Tang. 2023. "Improving a Fuel Cell System’s Thermal Management by Optimizing Thermal Control with the Particle Swarm Optimization Algorithm and an Artificial Neural Network" Applied Sciences 13, no. 23: 12895. https://doi.org/10.3390/app132312895
APA StyleDeng, B., Zhang, X., Yin, C., Luo, Y., & Tang, H. (2023). Improving a Fuel Cell System’s Thermal Management by Optimizing Thermal Control with the Particle Swarm Optimization Algorithm and an Artificial Neural Network. Applied Sciences, 13(23), 12895. https://doi.org/10.3390/app132312895