Work

Data-Driven Prediction of Anode Relative Humidity and Voltage in an Open-Cathode PEM Fuel Cell Stack Using the Koopman Operator

Year: 2025

Type: article

Abstract: Control of anode relative humidity is crucial for optimal hydration and performance in proton exchange membrane (PEM) fuel cells. Accurate predictive models can be essential to enabling real-time control of humidity. This study applies the Koopman operator with radial basis function (RBF) and time-delay embeddings as observables in an extended dynamic mode decomposition (EDMD) framework, to predict anode relative humidity and stack voltage in a 5kW open-cathode PEM fuel cell stack. The performance of the Koopman approaches is compared to a NARX neural network. The Koopman model with time delay embeddings as the basis function consistently outperformed that with RBF across all prediction horizons investigated. Particularly at a 5-step prediction horizon, the RBF EDMD recorded RMSE of 0.61% and 1.17V for humidity and voltage respectively while the time-delay EDMD demonstrated exceptional performance, achieving RMSE on the order of 10-13 for both outputs. The NARX model with random data division showed competitive results (RMSE of 0.77% and 1.05V) but deteriorated in performance (RMSE of 2.97% and 2.01V) when tested on unseen data. These findings highlight the effectiveness of the Koopman operator, especially with time-delay embeddings as coordinate basis. Additionally, the linearity of the Koopman model enables easy integration with established linear control strategies.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.07.086

Authors: Adwoa S. Adunyah, Carrie M. Hall

Institution: Illinois Institute of Technology

Language: English

Topic: Fuel Cells and Related Materials

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: diamond

Funder: National Science Foundation