BRITE Pivot: Enabling Efficient Fuel Cell Control Using Data-Driven Modeling

该奖项的全部或部分资金来自《2021年美国救援计划法案》(Public Law 117-2)。该奖项将资助将燃料电池用作汽车动力来源的研究，使其成为减少交通部门碳排放的可行战略，从而促进科学进步，促进国家繁荣和福利，并增加对安全和环保能源的依赖。燃料电池提供了一种非常适合中型和重型车辆的电气化方法，因为它们可以加油，行驶里程长，但与基于石油的发动机相比，它们的运行效率要高得多，排放减少或可以忽略不计。汽车应用中典型的高度可变的功率需求使得对燃料电池电堆的动态进行适当控制具有挑战性。需要新的工具来确保可以了解燃料或空气流量、温度或湿度的内部变化，并对其进行建模和调节，以确保持续的性能和最佳的寿命。该项目将通过将燃料电池动力学研究与数据驱动建模技术相结合来满足这一需求，这些技术能够实时控制燃料电池堆的内部状态。首席研究员将利用过去在常规发动机建模和控制方面的研究经验，实现一项具有重大社会技术重要性的复杂技术的关键飞跃。关于氢能经济的新的在线课程将有助于扩大目前代表人数不足的群体和为他们服务的机构的学生对STEM的参与。一项试点辅导计划将把学生与汽车和交通系统方面的学科专家联系起来。这项研究旨在为燃料电池电堆内部动力学的建模和控制做出基础性贡献，重点是燃料杂质的影响、检测和缓解。它将通过开发数据驱动的、基于物理的降阶模型来实现这一结果，这些模型弥合了校准密集型集中参数模型和计算要求高的气体动力学模型之间的差距，这两种模型都不允许实时控制燃料电池状态。该项目将在多维模型与人工神经网络的集成方面将基础实验与理论进步相结合，在显著降低计算成本的同时捕捉重要的非线性、反应速度和老化效应。将探索利用这种模型结构的非线性模型预测控制技术，并与基于模型的燃料杂质检测方法配对。最终控制方法的能力将在稳态和暂态条件下进行实验演示，以说明汽车驾驶循环中遇到的情况。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot award will fund research that makes the use of fuel cells as vehicle power sources a viable strategy for reducing carbon emissions from the transportation sector, thereby promoting the progress of science, advancing the national prosperity and welfare, and increasing the reliance on secure and environmentally friendly sources of energy. Fuel cells provide a method of electrifying vehicles that is well suited to medium and heavy-duty vehicles, since they can be refueled and provide a long driving range but can operate with a much higher efficiency and reduced or negligible emissions compared to petroleum-based engines. The highly variable power demands typical of automotive applications make proper control of the dynamics of a fuel cell stack challenging. New tools are needed to ensure that internal variations in fuel or air flow, temperature, or humidity can be understood, modeled, and regulated to ensure sustained performance and optimized longevity. This project will address this need by infusing the study of fuel cell dynamics with data-driven modeling techniques that enable real-time control of the internal state of a fuel cell stack. The principal investigator will leverage past research experience in conventional engine modeling and control to achieve a critical leap forward of a complex technology of significant sociotechnical importance. New online courses on the hydrogen energy economy will help broaden participation in STEM of students from currently underrepresented groups and the institutions that serve them. A pilot mentoring program will connect students with disciplinary experts in automotive and transportation systems. This research aims to make fundamental contributions to the modeling and control of internal fuel cell stack dynamics with emphasis on the impact, detection, and mitigation of fuel impurities. It will achieve this outcome by developing data-driven, physics-based, reduced-order models that bridge the gap between calibration-intensive, lumped-parameter models and computationally demanding gas dynamics models, neither of which allow for real-time control of the fuel cell state. The project will combine foundational experiments with theoretical advances in the integration of multi-dimensional models with artificial neural networks that significantly lower the computational cost while capturing important nonlinearities, reaction rates, and aging effects. Nonlinear model-predictive control techniques that leverage this model structure will be explored and paired with a model-based fuel impurity detection methodology. The capabilities of the final control approach will be demonstrated experimentally at steady-state and transient conditions illustrative of those encountered in automotive drive cycles.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.