Implementation of a Robust PMAE Algorithm to Limit Large Gain Variation in a Fuel-Cell Integrated EV Charging Architecture

This paper proposes an adaptive error convergence technique to improve the charging dynamics of an electric vehicle (EV) charging system in a grid-integrated proton-exchange membrane fuel cell (PEMFC) architecture. The fuel cell is most often characterized by its slow dynamics during multiple load transients and preferably operated as an auxiliary energy source in EV charging architecture. The delayed response affects the overall charging dynamics with further controller phase lag and non-linear system parameters to induce a significant convergence error during reference command tracking. To effectively control multi-source integrated charging dynamics in an EV charging architecture, a projection-modified adaptive estimation (PMAE) algorithm is proposed in this paper. The control algorithm establishes a variable gain adaptation within a bounded error contour and updates the controller gains based on the tracking error dynamics gradient. The fuel cell, supporting other energy resources, provides a stronger charging resiliency with a duty-modified control technique at the EV charging station. To validate the effectiveness of the controller implementation, a grid-integrated multi-objective EV charging prototype is designed and developed for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) at variable operating conditions.