Machine Learning in Fuel Cell Stacks, Systems and Modeling
Authors: Swider-Lyons Karen , Du Liang , Litster Shawn , Mebane David , Skidmore Dustan 
Published: December 2024

Machine learning (ML) has become a prominent tool for science and engineering as computing speed has increased over the last 5 to10 years. This paper describes our recent progress in applying ML to polymer electrolyte fuel cells (PEFC) systems prognostics and their research and development.
A key requirement for ML is the availability of high quality, labeled data to train models. Plug Power has 60,000 fuel cells in the field for materials handling and small stationary power, a sufficient number of systems from which to collect data. However, cost effective, practical systems have limited on board computing power, and often can be difficult to retrofit with processors that are compatible with ML. Real-time high-speed data collection for 60,000 systems for offboard computing can also become prohibitively expensive. Despite the data challenges, we have been able to use onboard system data to inexpensively predict the failure of valves and coolant pumps from sensors already instrumented on the systems. The valves are solenoids, which fail catastrophically within microseconds. We have found that by training a long-short-term-memory (LSTM) model with 3 to 4 variables, we observe changes in the fuel cell balance of plant that predict an unhealthy valve ahead of valve failure. A similar approach is used for the coolant loop, however although this subsystem has different behavior than the valves, and oscillate between healthy and unhealthy, before gradually becoming too unhealthy to use. For both cases, we had to understand the system operation in combination with training on the system sensors to establish which variables were most relevant to the state of health of the system and component.
ML is also becoming invaluable to modeling, particularly for expediting the cost and accuracy of the complex models that correlate the nano- and micro-structures of a fuel cell to its electrochemical performance. Computational simulation of fuel cells at the cell scale or stack level has traditionally required the use of computational fluid dynamics (CFD) models with reduced-order, analytical solutions for the catalyst layer phenomena. Examples include the agglomerate model that is used to model the coupled oxygen transport and oxygen reduction reaction in the catalyst agglomerates. However, as our fundamental understanding of the complex phenomena at the catalyst scale increases, including the impacts of local ionomer resistance, anion adsorption, and capillary condensation in the carbon-supported catalyst, there is an increasing amount of significant phenomena that should be incorporated into the models. The combined phenomena are beyond the capabilities of analytical solutions, and requires computational simulation to evaluate the local reaction rates. A valuable, emerging approach to addressing the significant length scale disparity is to train an ML model on a catalyst-scale simulation and embed the output of that model into a cell-scale model. We will highlight the use of this approach in evaluating the efficacy of high oxygen permeability ionomer (HOPI) with high surface area carbon supports versus low surface area supports.
Emerging research is showing how to create the next generation of data-informed electrochemical models. Instead of separating data-driven models from physical models, a new paradigm for embedding data-driven elements within physical models promises increased accuracy and speed relative to standalone physical models, pure “black-box” ML or hybrid approaches that still enforce a strict separation between data-driven and physical elements. Embedded ML has already shown considerable promise in dynamic systems, outperforming state-of-the-art recurrent neural networks such as LSTM and gated recurring units (GRUs) on benchmark problems. Embedded methods require computationally compact data-driven functions such as Karhunen-Loève decomposed Gaussian processes and gradient boosted trees, which represent physically well-defined functions such as rate constants, equilibrium constants and transport coefficients in the context of chemical and electrochemical device-scale models. The connection to physical quantities creates opportunities for independent measurements via targeted experimentation. The combination of speed and accuracy offered by embedded ML is ideal for product and process design as well as control applications.