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

该奖项由2021年美国救援计划法案提供全部或部分资金（公法117-2）。这个促进工程变革和公平进步的研究思路（BRITE）枢轴奖将资助研究，使燃料电池作为车辆电源的使用成为减少交通部门碳排放的可行战略，从而促进科学的进步，促进国家繁荣和福利，增加对安全和环境友好型能源的依赖。燃料电池提供了一种非常适合于中型和重型车辆的燃料化车辆的方法，因为它们可以被加燃料并提供长的行驶里程，但与基于石油的发动机相比，它们可以以高得多的效率和减少或可忽略的排放来操作。汽车应用中典型的高度可变的功率需求使得燃料电池堆的动态特性的适当控制具有挑战性。需要新的工具来确保燃料或空气流量、温度或湿度的内部变化可以被理解、建模和调节，以确保持续的性能和优化的寿命。该项目将通过将燃料电池动力学研究与数据驱动的建模技术相结合来解决这一需求，该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传统发动机建模和控制方面的研究经验，实现具有重大社会技术重要性的复杂技术的关键飞跃。关于氢能经济的新在线课程将有助于扩大目前代表性不足的群体和为他们服务的机构的学生对STEM的参与。试点辅导计划将学生与汽车和运输系统的学科专家联系起来。本研究旨在为燃料电池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、基于物理的降阶模型来实现这一目标，这些模型将弥合校准密集型集总参数模型和计算要求苛刻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。该项目将把联合收割机基础实验与多维模型与人工神经网络相结合的理论进展相结合，从而显著降低计算成本，同时捕获重要的非线性、反应速率和老化效应。利用这种模型结构的非线性模型预测控制技术将被探索，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瞬态条件下进行实验验证，这些条件说明了汽车驾驶循环中遇到的情况。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.