Control of proton exchange membrane fuel cells using data driven state space models

Proton exchange membrane fuel cells (PEMFCs) are increasingly being researched upon due to their potential toward sustainable energy generation. Toward improved productivity of PEMFCs, it is important to develop systematic approaches for optimization and control of their operations. PEMFCs pose interesting challenges toward these tasks due to their complex behavior such as nonlinearity and spatial variations. While first principles model based approaches could be used, a more mathematically attractive and cost-effective alternative is to use empirical modeling approaches for representing the system dynamics toward optimization and control. In this paper, we propose to use a novel, innovation form of state space models that facilitate the development of advanced control algorithms such as linear quadratic Gaussian (LQG) and model predictive control (MPC), and provide improved disturbance rejection necessary for these applications. We demonstrate the applications of such model based algorithms via simulations involving a distributed along-the-channel model of the PEMFC, and also present experimental validation on a PEMFC setup.