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Combustion Characterization and Model Fuel Development for Micro-tubular Flame-assisted Fuel Cells
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Offline and online parameter estimation of nonlinear systems: Application to a solid oxide fuel cell system.
Yashan Xing1, Lucile Bernadet2, Marc Torrell2
1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, China; Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment, Kunming University of Science and Technology, Kunming, 650500, China.
ISA Transactions
|August 4, 2022
Summary
This study presents a new method to calibrate solid oxide fuel cell (SOFC) models using offline tuning and online parameter estimation. The approach accurately models SOFCs under varying conditions, improving performance prediction.
Area of Science:
- Electrochemistry
- Chemical Engineering
- Control Systems
Background:
- Mathematical models are crucial for understanding and optimizing Solid Oxide Fuel Cell (SOFC) performance.
- Accurate model calibration is essential, especially under dynamic operating conditions.
- Existing offline tuning strategies may not adequately capture the complex behavior of SOFCs across various operational states.
Purpose of the Study:
- To develop and validate an advanced offline tuning strategy for SOFC mathematical models.
- To introduce an online parameter estimation method for real-time SOFC performance tracking.
- To enhance the accuracy and adaptability of SOFC models under diverse operating conditions.
Main Methods:
- Implemented a hybrid optimization approach combining Particle Swarm Optimization (PSO) with gradient-based search for parameter tuning.
- Developed polynomial equations to represent sensitive parameters across different operating conditions.
- Introduced an adaptive optimal learning law for online minimization of cost functions using estimation error.
- Utilized low-pass filters and algebraic calculations to extract estimation error for online adaptation.
Main Results:
- Successfully calibrated the SOFC mathematical model under various operation conditions using the proposed offline strategy.
- Demonstrated the capability of the reconstructed model to accurately represent SOFC behavior across the entire operational range.
- Validated the effectiveness of the online adaptive estimation method in tracking slowly time-varying SOFC performance.
- Experimental verification on a practical SOFC test bench confirmed the efficacy of both offline and online methods.
Conclusions:
- The proposed offline tuning strategy effectively calibrates SOFC models for diverse operating conditions.
- The online adaptive estimation method accurately captures the dynamic performance of SOFCs.
- The combined approach offers a robust solution for improving SOFC model accuracy and real-time monitoring.
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