Optimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE
The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with Back Propagation neural network adaptive Proporti...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266654682400096X |
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| Summary: | The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with Back Propagation neural network adaptive Proportion Integration Differentiation (BP-PID), and then a metaheuristic algorithm is used to optimize the parameters of RBF-BP-PID for further improvement in temperature control. First, an air-cooled PEMFC system model is established. To match the simulation data with the experimental data, Teaching Learning Based Optimization–Differential Evolution (TLBO-DE) is proposed to identify the unknown parameters, and the maximum relative error is <3.5 %. Second, RBF neural network is introduced to identify the stack temperature and provide the accurate ∂y(k)∂u(k) for BP-PID, which solves the problem of using sign function sgn(∂y(k)∂u(k)) to approximate the ∂y(k)∂u(k) in BP-PID. Regarding the temperature control of air-cooled PEMFC, several controllers are compared, including PID, Fuzzy-PID, BP-PID and RBF-BP-PID. The proposed RBF-BP-PID achieves the best control effect, which reduces the integrated time and absolute error (ITAE) by 3.4 % and 15.8 % based on BP-PID in the startup and steady phases, respectively. Since the ∂y(k)∂u(k) provided by RBF changes softly and continuously during the control process, the parameters self-tuning ability of RBF-BP-PID is better than BP-PID. Third, to improve the control effect of RBF-BP-PID further, TLBO-DE is adopted to optimize the parameters of RBF neural network and BP neural network. |
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| ISSN: | 2666-5468 |