Shenzhen Science and Technology Innovation Commission (Grant No. KQJSCX20180330170047681）; Shenzhen Engineering Laboratory for Autonomous Driving Technology(Y7D004);Shenzhen Key Laboratory of Electric Vehicle Powertrain Platform and Safety Technology
固体氧化物燃料电池(Solid-Oxide Fuel Cell，SOFC)因其能量转换效率高而备受关注，但其相 关技术非常复杂，技术成熟度比质子交换膜燃料电池、直接甲醇燃料电池等其他类型的燃料电池低。 SOFC 的微观结构是影响其性能的因素之一，为加速 SOFC 的商业化应用，需要对其复杂微观结构进 行有效优化。同时，SOFC 性能测试实验耗时长、费用高，而高可靠性的 SOFC 计算机模型可用来缩 短 SOFC 微观结构优化时间和降低研发成本。该研究根据阳极支撑 SOFC 结构变化对应的性能实验 数据，开发了一种基于人工神经网络的、根据结构特性来预测其性能的 SOFC 计算机模型。实验过程 利用部分数据对该人工神经网络进行训练，并利用另一部分数据对其进行验证。结果显示，所开发的 SOFC 模型能够准确地根据微观结构的变化呈现其性能变化，适合用于 SOFC 微观结构的优化。
Solid oxide fuel cells (SOFCs) have gained lots of attentions owing to their high energy conversion efficiency, however, because of the complex technology, their application is not mature as compared with other types of fuel cells such as proton-exchange membrane fuel cells and direct methanol fuel cells. The micro-structure is one of important factors on the SOFC performance, therefore, in order to expedite the commercialization of SOFCs, it is crucial to develop an effective method to optimize the complicated microstructure of SOFCs. The experiment of the SOFC performance test is time-consuming and cost-ineffective, thus it is necessary to develop an SOFC simulation model with high reliability to save the time and cost of the micro-structure optimization. This research proposes an artificial neural network (ANN)-based SOFC simulation model according to the experimental data of an anode-supported SOFC performance, in which the polarization characteristics of SOFCs are estimated from their structural characteristics. After training the ANN based on a part of the experimental data, the rest part of data are used to evaluate the effectiveness of the proposed SOFC model. Results show that the proposed SOFC simulation model accurately presents the polarization characteristics of SOFCs according to the structural characteristics, and this indicates that the model is suitable for the micro-structure optimization for SOFCs.
宋昌熙,郑春花,车硕源.基于人工神经网络的固体氧化物燃料电池性能 预测模型开发 [J].集成技术,2020,9(5):27-33
SONG Changhee, ZHENG Chunhua, CHA Suk Won. Development of a Simulation Model for Polarization Characteristics of Solid Oxide Fuel Cells Based on an Artificial Neural Network[J]. Journal of Integration Technology,2020,9(5):27-33