article · Scientific Reports
This research focuses on extracting seven unknown parameters of proton exchange membrane fuel cell stacks to better model their behavior. The technique uses a Kepler Optimisation Algorithm to minimise the sum of squared deviations between experimental measurements and calculated model data. The method was tested across four practical commercial fuel cell stacks under varying operating conditions to evaluate steady-state performance. When compared to several other recent optimisation algorithms, this method achieved superior accuracy in matching measured voltage outputs. Additionally, the approach upgraded Amphlett's model to capture the electrical dynamic transient responses of fuel cells alongside steady-state operation. The findings show that this computational optimisation process produces highly viable results across both operating regimes and holds potential for real-time applications.
Accurate computational models are vital for designing, monitoring, and controlling clean energy systems. Proton exchange membrane fuel cells often have unknown internal characteristics that complicate performance predictions. By precisely extracting these missing parameters, engineers can better simulate and predict how fuel cells respond during both stable operation and sudden changes in demand.
The method addresses modeling challenges for fuel cell developers, control engineers, and power system integrators. By accurately matching data from four practical fuel cell models, including Ballard Mark and NedStack units, the work demonstrates applied and tested computational modeling. Because the algorithm shows potential for real-time implementation, it could eventually inform embedded diagnostic or control software, though the abstract does not specify an explicit path to a commercial product.
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Abstract The current effort addresses a novel attempt to extract the seven ungiven parameters of PEMFCs stack. The sum of squared deviations (SSDs) among the measured and the relevant model-based calculated datasets is adopted to define the cost function. A Kepler Optimization Algorithm (KOA) is employed to decide the best values of these parameters within viable ranges. Initially, the KOA-based methodology is applied to assess the steady-state performance for four practical study cases under several operating conditions. The results of the KOA are appraised against four newly challenging algorithms and the other recently reported optimizers in the literature under fair comparisons, to prove its superiority. Particularly, the minimum values of the SSDs for Ballard Mark, BCS 0.5 kW, NedStack PS6, and Temasek 1 kW PEMFCs stacks are 0.810578 V 2 , 0.0116952 V 2 , 2.10847 V 2 , and 0.590467 V 2 , respectively. Furthermore, the performance measures are evaluated on various metrics. Lastly, a simplified trial to upgrade Amphlett’s model to include the PEMFCs’ electrical dynamic response is introduced. The KOA appears to be viable and may be extended in real-time conditions according to the presented scenarios (steady-state and transient conditions).
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DOI: 10.1038/s41598-023-46847-w
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