Differential Flatness of Grid-Connected EV Charging Stations with LCL Filters: Realizability Constraints and Trajectory Planning – code and simulation data

This repository contains Python code used to reproduce the simulations presented in the paper “Differential Flatness of Grid-Connected EV Charging Stations with LCL Filters: Realizability Constraints and Trajectory Planning.”The repository contains three main components:1. Trajectory optimization in flat coordinatesThe file evcs_optimizer.py implements multi-objective trajectory optimization in the flat-output space. The optimizer parametrizes the flat outputs and computes system trajectories subject to realizability constraints, actuator limits, and multi-objective performance criteria, including power tracking and DC-link voltage smoothness.2. Closed-loop simulation of the full EVCS modelThe file closed_loop_tracking.py implements a full-order averaged dq-frame model of the EVCS with an LCL filter and evaluates tracking performance of the planned trajectories using a cascaded PI control structure with active damping and actuator saturation.3. Numerical verification of the flatness parametrizationThe file num_Flatness.py provides numerical verification of the flatness reconstruction formulas and demonstrates the recovery of system states and inputs from the flat outputs and their derivatives.