Reweighted Error Minimization Algorithm to Enhance Current Following Adaptability in a Multiobjective EV Charging Architecture

This paper presents a multi-objective EV charging dynamics with a hybrid configuration of AC and DC charging compatibility. A single-stage photovoltaic (PV) array with battery energy storage (BES) integration is also provided to enhance the charging reliability in presence of grid vulnerability. The hybrid charging architecture provides a seamless charging environment during both grid-connected and islanded operation. A PV-maximum power point tracking algorithm is employed that simultaneous utilizes the BES and PV -array energy sources to minimize the grid dependency during peak hour charging implementation. Unity power factor operation is guaranteed through the grid-connected charging operation to ensure an improved power quality at the grid and EV charging interface. To enable DC fast charging operation a 3-phase isolated dual active bridge (DAB) converter is implemented that operates in phase-shifting modulation to carry out power flow in either of the direction. Similar operation is also depicted with single phase DAB for slow charging operation. A normalized least mean square algorithm is implemented to fasten and stabilize the current tracking operation during multi-mode charging implementation. The charging algorithm is implemented in MATLAB platform with the hybrid EV charging architecture to verify the EV dynamics through multi-objective grid power compensation and enhanced power quality operation.