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

Version 1.0

Piasek-Skupna, Joanna; Skupny Michał, 2026, "Differential Flatness of Grid-Connected EV Charging Stations with LCL Filters: Realizability Constraints and Trajectory Planning – code and simulation data", https://doi.org/10.18150/3E2C9T, RepOD, V1

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Description

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 coordinates

The 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 model

The 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 parametrization

The 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.

Subject
Engineering
Keyword

differential flatness, EVCS, trajectory optimization

Related Publication

Differential Flatness of Grid-Connected EV Charging Stations with LCL Filters: Realizability Constraints and Trajectory Planning (Joanna Piasek-Skupna, Michał Skupny) :

CC0 Creative Commons Zero 1.0 CC0 Creative Commons Zero 1.0

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num_Flatness.py
Unknown - 17.7 KB - Apr 7, 2026 - 2 Downloads
MD5: 5eeff57f492ae0f3db7af8b4ea2cb96a
License: CC0 Creative Commons Zero 1.0
Numerical verification of the flatness parametrization
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closed_loop_tracking.py
Unknown - 24.6 KB - Apr 7, 2026 - 2 Downloads
MD5: bc27ae7940eae66a256723fc00d522a2
License: CC0 Creative Commons Zero 1.0
Closed-loop simulation of the full EVCS model
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evcs_optimizer.py
Unknown - 33.8 KB - Apr 7, 2026 - 2 Downloads
MD5: 5855fbd5c6785b2b3605d6373b6e2e90
License: CC0 Creative Commons Zero 1.0
Trajectory optimization in flat coordinates
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