Online Parameter Estimation and Current Optimization for Trielectrode Zinc–Air Cell
Résumé
The prediction mechanism for energy storage systems—such as state of charge, capacity, or end‐of‐discharge points—is improved by online adapting model parameters using available measurements, thereby enhancing overall accuracy. However, this online adaptation is subject to uncertainties and disturbances and involves filtering and estimation techniques that come with a nonnegligible computational load and have to be fine‐tuned in order to meet the real‐time constraints. On top of these challenges, the ultimate objective of the model adaptation is twofold: first, the monitoring and diagnosis for a safe use and, second, an operational adjustment of the discharge profile to meet both performance optimization and constraints satisfaction. While the diagnosis relies on open‐loop supervision techniques based on adequate residual generation, the operational optimization can be efficiently achieved by means of a receding horizon control strategy. The present paper reviews all these principles and their practical implementation for a trielectrode zinc–air cell constructed in a laboratory‐based infrastructure.
