Temperature Control Strategy for Hydrogen Fuel Cell Based on IPSO-Fuzzy-PID
Abstract
:1. Introduction
2. PEMFC Hydrothermal Management System
2.1. System Structure
2.2. Modeling of the Output Voltage of the Stack
2.3. Heat Production Modeling
2.4. Thermal Modeling
3. Improved Particle Swarm Algorithm
3.1. Standard Particle Swarm Algorithm
3.2. Initializing Particle Populations Based on Logistic Chaotic Mapping
3.3. Dynamic Regulation of Learning Factors
3.4. Adaptive Adjustment of the Inertia Weighting Factor
3.5. Flow of the Improved PSO Algorithm
4. Design of PEMFC Temperature Controller Based on IPSO-Fuzzy-PID
4.1. Fuzzy-PID Control
4.2. Optimization of Fuzzy-PID Controller Parameters Based on IPSO
5. Analysis of Experiment Results
5.1. Experimental System
5.2. Analysis of the Polarization Curve of the Stack
5.3. Analysis of the IPSO Algorithm
5.4. Analysis of IPSO-Fuzzy-PID Experimental Results
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Ahmad, S.; Ullah, A.; Samreen, A.; Qasim, M.; Nawaz, K.; Ahmad, W.; Alnaser, A.; Kanan, A.M.; Egilmez, M. Hydrogen production, storage, transportation and utilization for energy sector: A current status review. J. Energy Storage 2024, 101, 113733. [Google Scholar] [CrossRef]
- Zhou, M.; Hu, T. Analysis of carbon emission status under the carbon neutral target in China for Earth’s atmospheric balance. In IOP Conference Series: Earth and Environmental Science; IOP Publishing Ltd.: Bristol, UK, 2021; Volume 804, p. 042082. [Google Scholar]
- Lebrouhi, B.E.; Djoupo, J.J.; Lamrani, B.; Benabdelaziz, K.; Kousksou, T. Global hydrogen development—A technological and geopolitical overview. Int. J. Hydrogen Energy 2022, 47, 7016–7048. [Google Scholar] [CrossRef]
- Wang, Y.; Wang, Y.; Xu, J.; Chai, T. Observer-Based Discrete Adaptive Neural Network Control for Automotive PEMFC Air-Feed Subsystem. IEEE Trans. Veh. Technol. 2021, 70, 3149–3163. [Google Scholar] [CrossRef]
- Xu, J.; Zhang, C.; Fan, R.; Bao, H.; Wang, Y.; Huang, S.; Chin, C.S.; Li, C. Modelling and control of vehicle integrated thermal management system of PEM fuel cell vehicle. Energy 2020, 199, 117495. [Google Scholar] [CrossRef]
- Şefkat, G.; Özel, M.A. Experimental and numerical study of energy and thermal management system for a hydrogen fuel cell-battery hybrid electric vehicle. Energy 2020, 238, 121794. [Google Scholar] [CrossRef]
- Xing, L.; Chang, H.; Zhu, R.; Wang, T.; Zou, Q.; Xiang, W.; Tu, Z. Thermal analysis and management of proton exchange membrane fuel cell stacks for automotive vehicle. Int. J. Hydrogen Energy 2021, 46, 32665–32675. [Google Scholar] [CrossRef]
- Zhu, D.; Ait-Amirat, Y.; Djerdir, A. Active thermal management between proton exchange membrane fuel cell and metal hydride hydrogen storage tank considering long-term operation. Energy Convers. Manag. 2019, 202, 112187. [Google Scholar] [CrossRef]
- 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, 23. [Google Scholar] [CrossRef]
- Shi, Y.; Sun, Z.; Wang, Y.; Li, M. Thermal Management of Proton-Exchange Membrane Fuel Cells with Enhanced Fuzzy Control via an Improved Whale Optimization Algorithm. Energy Technol. 2024, 12, 2400055. [Google Scholar] [CrossRef]
- Chen, X.; Xu, J.; Fang, Y.; Li, W.; Ding, Y.; Wan, Z.; Wang, X.; Tu, Z. Temperature and humidity management of PEM fuel cell power system using multi-input and multi-output fuzzy method. Appl. Therm. Eng. 2022, 203, 117865. [Google Scholar] [CrossRef]
- Jiang, J.; Wei, W.; Shao, W.; Liang, Y.; Qu, Y. Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm. IEEE Access 2021, 9, 56364–56375. [Google Scholar] [CrossRef]
- Liu, H.; Zhang, X.; Tu, L. A modified particle swarm optimization using adaptive strategy. Expert Syst. Appl. 2020, 152, 113353. [Google Scholar] [CrossRef]
- Yuan, Z.; Wang, W.; Wang, H.; Ashourian, M. Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm. Energy Rep. 2020, 6, 1572–1580. [Google Scholar] [CrossRef]
- Zhao, H.; Pan, S.; Ma, L.; Wu, Y.; Wu, Y. Control of Fuel Cell Temperature Based on Classified Replay Twin Delayed Bayesian Deep Deterministic Policy Gradient. Trans. China Electrotech. Soc. 2024, 13, 4240–4256. [Google Scholar]
- Souissi, A. Adaptive sliding mode control of a PEM fuel cell system based on the super twisting algorithm. Energy Rep. 2021, 7, 3390–3399. [Google Scholar] [CrossRef]
- Hu, P.; Cao, G.; Zhu, X.; Hu, M. Coolant circuit modeling and temperature fuzzy control of proton exchange membrane fuel cells. Int. J. Hydrogen Energy 2010, 35, 9110–9123. [Google Scholar] [CrossRef]
- Qian, J.; Zhang, J.; Yao, D.; Li, Y.; Wang, Q. A Particle Swarm Optimization Algorithm Based on Improved Inertia Weight. Comput. Digit. Eng. 2022, 8, 1667–1670. [Google Scholar]
- Xue, W. Particle Swarm Optimization Algorithm with Improved Inertia Weight. Mod. Inf. Technol. 2023, 20, 88–91. [Google Scholar]
- Li, S.; Ma, D.; Lu, J.; Xing, B.; Wang, K.; Gao, Y.; Han, B. In situ calibration of triaxial coils of a vector optically pumped magnetometers based on a particle swarm optimization algorithm. Measurement 2022, 202, 111878. [Google Scholar] [CrossRef]
- Shang, L.; Min, P.; Zhang, J. Research on MPPT of photovoltaic array based on improved PSO algorithm. Transducer Microsyst. Technol. 2024, 8, 35–39. [Google Scholar]
- Huang, Y. Secure sharing of privacy data in the Internet of Things under attribute Logistic chaos mapping. Mod. Electron. Tech. 2024, 13, 97–101. [Google Scholar]
- Nayeem, G.M.; Fan, M.; Akhter, Y. A time-varying adaptive inertia weight based modified PSO algorithm for UAV path planning. In Proceedings of the 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 5–7 January 2021. [Google Scholar]
- Hou, R.; Yang, J.; Yu, P.; Wang, J.; Kong, X. Temperature Control of Proton Exchange Membrane Fuel Cell Based on Fuzzy Active Disturbance Rejection. J. Shandong Ind. Technol. 2022, 6, 16–23. [Google Scholar]
ΔKp | E | |||||||
---|---|---|---|---|---|---|---|---|
NB | NM | NS | ZO | PS | PM | PB | ||
EC | NB | PB | PB | PM | PM | PM | PB | PB |
NM | PB | PB | PM | PM | PM | PB | PB | |
NS | PM | PS | PS | ZO | PS | PM | PM | |
ZO | PM | PS | PS | ZO | PS | PS | PM | |
PS | PM | PM | PS | PS | PS | PM | PM | |
PM | PB | PB | PM | PM | PM | PM | PB | |
PB | PB | PB | PM | PM | PM | PB | PB |
ΔKi | E | |||||||
---|---|---|---|---|---|---|---|---|
NB | NM | NS | ZO | PS | PM | PB | ||
EC | NB | NB | NB | NM | NM | NM | NB | NB |
NM | NB | NB | NM | NM | NM | NB | NB | |
NS | NM | NM | NS | NS | NS | NM | NM | |
ZO | NM | NM | NS | NS | NS | NM | NM | |
PS | NM | NM | NS | NS | NS | NM | NM | |
PM | NB | NB | NM | NM | NM | NB | NB | |
PB | NB | NB | NM | NM | NB | NB | NB |
ΔKd | E | |||||||
---|---|---|---|---|---|---|---|---|
NB | NM | NS | ZO | PS | PM | PB | ||
EC | NB | PB | PB | PM | PM | PM | PB | PB |
NM | PB | PM | PS | PS | PS | PM | PB | |
NS | PM | PS | ZO | ZO | ZO | PS | PS | |
ZO | PM | PS | ZO | ZO | ZO | PS | PM | |
PS | PM | PS | ZO | ZO | ZO | PS | PM | |
PM | PB | PM | PS | PS | PM | PM | PB | |
PB | PB | PB | PM | PM | PM | PB | PB |
Test Function | Control Strategy | Average Value | Minimum Value |
---|---|---|---|
F9 | IPSO | 34.9299 | 9.9496 |
PSO | 339.1051 | 298.8717 | |
GA | 97.9543 | 65.0149 | |
ALO | 59.9694 | 39.7983 | |
F10 | IPSO | 0.9476 | 2.207 × 10−13 |
PSO | 5.4087 | 0.1011 | |
GA | 2.3122 | 7.5679 × 10−7 | |
ALO | 3.3749 | 1.8997 |
Control Methods | Peak Time | Peak Value | Steady State Value | Adjustment Time | Overshoot |
---|---|---|---|---|---|
PID | 150.033 s | 346.9 K | 343.1 K | 691.372 s | 1.1075% |
Fuzzy-PID | 162.398 s | 345.9 K | 342.8 K | 553.030 s | 0.9043% |
IPSO-Fuzzy-PID | 190.053 s | 343.3 K | 342.9 K | 293.478 s | 0.1167% |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, Z.; Dong, H.; Ma, X. Temperature Control Strategy for Hydrogen Fuel Cell Based on IPSO-Fuzzy-PID. Electronics 2024, 13, 4949. https://doi.org/10.3390/electronics13244949
Liu Z, Dong H, Ma X. Temperature Control Strategy for Hydrogen Fuel Cell Based on IPSO-Fuzzy-PID. Electronics. 2024; 13(24):4949. https://doi.org/10.3390/electronics13244949
Chicago/Turabian StyleLiu, Zenghui, Haiying Dong, and Xiping Ma. 2024. "Temperature Control Strategy for Hydrogen Fuel Cell Based on IPSO-Fuzzy-PID" Electronics 13, no. 24: 4949. https://doi.org/10.3390/electronics13244949
APA StyleLiu, Z., Dong, H., & Ma, X. (2024). Temperature Control Strategy for Hydrogen Fuel Cell Based on IPSO-Fuzzy-PID. Electronics, 13(24), 4949. https://doi.org/10.3390/electronics13244949