Fuel Cell
Fuel cells, devices converting chemical energy directly into electricity, are a focus of intense research driven by the need for clean energy solutions. Current research emphasizes improving fuel cell efficiency and durability through advancements in materials science (e.g., metal-organic frameworks as electrolytes), refined control strategies (e.g., model predictive control incorporating machine learning for precise temperature regulation and energy management), and optimized production processes (e.g., decentralized scheduling for modular electrolysis plants). These efforts aim to reduce production costs, enhance reliability, and expand the applications of fuel cells in diverse sectors, including transportation and stationary power generation.
Papers
Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning
Qi Zhang, Lei Xie, Weihua Xu, Hongye Su
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
Qi Zhang, Lei Wang, Weihua Xu, Hongye Su, Lei Xie