Efficiency, reliability and lifetime of polymer electrolyte membrane fuel cells (PEMFCs) are significantly limited by inadequate water management. High-performance water active control algorithms cannot be implemented due to the absence of adequate online sensors that can measure the internal liquid water saturation. A promising technique that can be applied in this context is the state observer. However, fuel cell models present strong nonlinearities, model uncertainty, unmatched unknown parameters and sensor noise, which are major difficulties in observer design. The algorithm proposed in this work is based on a time-varying adaptive observer, that offers an estimation of the liquid water state and behaviour in the cathode catalyst layer of a PEMFC, coupled with a low-power peaking-free observer with dynamic dead-zone filtering that is used as a high-performance soft sensor. The algorithm is shown to provide an accurate estimation of the liquid water saturation and the liquid water transport parameters even in the presence of sensor noise and model inaccuracies. The results are validated through numerical simulations and in a real experimental prototype.

水管理不足会严重限制聚合物电解质膜燃料电池（PEMFC）的效率，可靠性和寿命。由于缺少足够的在线传感器来测量内部液态水饱和度，因此无法实现高性能的水主动控制算法。在这种情况下可以应用的一种有前途的技术是状态观察器。但是，燃料电池模型存在强烈的非线性，模型不确定性，未知参数不匹配和传感器噪声等问题，这是观察者设计中的主要困难。这项工作中提出的算法基于时变自适应观测器，该观测器可以估算PEMFC阴极催化剂层中的液态水状态和行为，结合具有动态死区滤波功能的低功率无峰值观察器，该观察器用作高性能软传感器。该算法显示即使在存在传感器噪声和模型不准确的情况下，也可以提供对液态水饱和度和液态水传输参数的准确估计。通过数值模拟和真实的实验原型验证了结果。