DECharge

Introduction

Discomfort and Efficient-aware electric vehicle Charge coordination (DECharge) is a decentralized Electric Vehicle (EV) charging control framework based on collective learning of I-EPOS. It allows EVs to autonomously select charging stations for charge while minimizing travel and queuing time.

Setup

From builds upon Python 3.7 to 3.9

1. Clone this repo:

git clone git@github.com:TDI-Lab/DECharge.git

2. Modify parameters

• Modify the properties of algorithms in conf/epos.properties.

• Modify the parameters of scenarios in main.py.

3. Run the code:

python main.py

Code structure

├── LICENSE
├── README.md                       <- The top-level README for developers using this project.
├── IEPOS.jar                       <- The jar file to run I-EPOS in the DECharge framework
├── conf
│   ├── epos.properties             <- The parameters of the I-EPOS approach
│   ├── log4j.properties             
│   ├── measurement.conf             
│   ├── protopeer.conf            
├── datasets
│   ├── Stations                    <- The input historical dataset of charging stations
│   ├── ChargingDemands             <- The input generated dataset of EV charging requests 
│   ├── EVdemands                   <- The generated dataset as the input of I-EPOS
├── env
│   ├── RealWorld.py                <- Create the environent of the real scenarios
└────── main.py                     <- Case study and scenario settings

Documents

More details of I-EPOS can be found here.

The historical datasets of charging stations in Paris can be found here.

The historical datasets of EV charging requests in South Korea can be found here.

Citation

If you use DECharge in any of your work, please cite our paper: