Fuel cell systems have attracted significant attention in the field of residential energy due to their high efficiency and environmentally friendly characteristics. However, the inherent coupling of its thermoelectric output limits the flexibility of the system to meet diverse residential energy needs. This study proposes a combined heat and power system based on a proton exchange membrane fuel cell integrated with an organic Rankine cycle and heat pump, and builds a multi-level optimization design and intelligent control framework. Through this framework, current density and split ratio were identified as two key operational parameters affecting heat and power output. To enhance the precision and adaptability of system control, a neural network evaluation metric based on sensitivity weighting was introduced to optimize the hyperparameters of the Back Propagation neural network controller. This approach significantly improved the accuracy of the control model and system performance. Based on the optimized neural network controller, an intelligent control strategy oriented towards heat demand was realized, effectively meeting users’ dynamic needs. Results show that under typical demand conditions, the system achieved significant performance improvement: maximum thermal efficiency of 47.48 %, maximum electrical efficiency of 36.73 %, maximum hydrogen consumption rate of 1.3 g/s, and minimum levelized cost of energy of 0.4183 $/kW·h−1. This research provides valuable theoretical guidance for the optimization design and operations management of fuel cell-based combined heat and power systems.

中文翻译：

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基于燃料电池的热电联产系统多层次优化设计与智能控制框架


燃料电池系统因其高效和环保的特性而在住宅能源领域引起了极大的关注。然而，其热电输出的固有耦合限制了系统满足各种住宅能源需求的灵活性。本研究提出了一种基于质子交换膜燃料电池的热电联产系统，集成了有机朗肯循环和热泵，并构建了多层次优化设计和智能控制框架。通过这个框架，电流密度和分光比被确定为影响热量和功率输出的两个关键操作参数。为了提高系统控制的精度和适应性，引入了基于灵敏度加权的神经网络评估指标来优化反向传播神经网络控制器的超参数。这种方法显著提高了控制模型的准确性和系统性能。基于优化的神经网络控制器，实现了面向热量需求的智能控制策略，有效满足了用户的动态需求。结果表明，在典型需求条件下，该系统实现了显著的性能改进：最大热效率为 47.48 %，最大电效率为 36.73 %，最大耗氢率为 1.3 g/s，最低平准化能源成本为 0.4183 $/kW·h−1。本研究为燃料电池热电联产系统的优化设计和运营管理提供了有价值的理论指导。