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首页> 外文会议>11th fuel cell science, engineering, and technology conference 2013 >DYNAMIC MODELING OF A REFORMED METHANOL FUEL CELL SYSTEM USING EMPIRICAL DATA AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM MODELS
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DYNAMIC MODELING OF A REFORMED METHANOL FUEL CELL SYSTEM USING EMPIRICAL DATA AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM MODELS

机译：利用经验数据和自适应神经模糊推理系统模型对重整甲醇燃料电池系统进行动态建模

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In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy® is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous hydrogen, which is difficult and energy consuming to store and transport. The models include thermal equilibrium models of the individual components of the system. Models of the heating and cooling of the gas flows between components are also modeled and Adaptive Neuro-Fuzzy Inference System models of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other's output. The models take this into account using an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified and fine-tuned through a series of experiments and are found to have mean absolute errors between 0.4% and 6.4% but typically below 3%. After a comparison between the performance of the combined model and the experimental setup, the model is deemed to be valid for control design and optimization purposes.
机译：在这项工作中，基于理论和经验方法，开发了Serenergy®生产的H3-350重整甲醇燃料电池（RMFC）独立电池充电器的动态MATLAB Simulink模型。 RMFC系统的优点是它们使用液态甲醇代替气态氢作为燃料，这很难储存和运输，而且消耗能源。这些模型包括系统各个组件的热平衡模型。还对组件之间的气体流动的加热和冷却模型进行了建模，并实施了重整过程的自适应神经模糊推理系统模型。制作了用于燃料电池的鼓风机和为重整器提供过程热的燃烧器的冷却流模型。两个鼓风机有一个共同的排气口，这意味着两个鼓风机会影响彼此的输出。模型使用经验方法将其考虑在内。还使用经验方法开发了用于空气冷却效果的翅片效率模型。还基于适用于H3-350模块的测量性能的标准模型来实现燃料电池模型。通过一系列实验对模型的所有各个部分进行了验证和微调，发现其平均绝对误差在0.4％至6.4％之间，但通常低于3％。在组合模型的性能与实验设置之间进行比较之后，该模型被认为对控制设计和优化目的有效。

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