CHAMPPy - Mobility and Charging Profiles in Python
Project description
CHAMPPy
CHAMPPy (Charging and Mobility Profiles in Python) is a Python library to generate synthetic mobility and charging profiles for different types of electric vehicles (EVs) including vans ๐, trucks ๐, busses ๐ and passanger cars ๐.
Road transport decarbonization requires realistic charging demand models across all vehicle classes. However, most existing studies and publicly available tools focus on private passenger cars. Commercial electric vehicles such as vans and trucks are often underrepresented despite their major relevance for emissions and grid impacts. CHAMPPy is an open Python package that addresses this gap by generating synthetic driving and charging profiles for different EV types, including commercial fleets. The model combines a Markov chain to represent vehicle locations over time with beta-distributed journey speeds, from which trip distances are derived, and uses dedicated algorithms to generate mobility and charging profiles. An optional clustering approach increases profile heterogeneity and is particularly useful when analyzing individual profiles.
๐ ๏ธ CHAMPPy supports two workflows:
- Light: Use existing parameters to quickly generate drving and charging profiles with user-defined settings (e.g., simulation period, number of vehicles, charging power, battery capacity).
- Full: Re-parameterize the model with custom reference data (e.g., other countries, fleet compositions, or vehicle classes). Generate drving and charging profiles from your model parameters.
Links
- Documentation: https://champpy.readthedocs.io
- Source code: https://github.com/ffe-munich/CHAMPPy
- PyPI releases: https://pypi.org/project/champpy/
- License: http://opensource.org/licenses/MIT
Authors
CHAMPPy has been developed by Florian Biedenbach (lead), Valentin Preis und Daniel Godin.
Repo structure
CHAMPPy/
โโโ src/champpy/ # Main package source code
โ โโโ __init__.py
โ โโโ core/ # Core functionality
โ โ โโโ __init__.py
โ โ โโโ charging/ # Charging profile module
โ โ โ โโโ __init__.py
โ โ โ โโโ charging_model.py # Model to generate charging profiles
โ โ โ โโโ charging_validation.py # Charging validation & plotting
โ โ โโโ mobility/ # Mobility profile module
โ โ โโโ __init__.py
โ โ โโโ mobility_cleaning.py # Data cleaning
โ โ โโโ mobility_components.py # Data components
โ โ โโโ mobility_data.py # Data structures
โ โ โโโ mobility_model.py # Model to generate profiles
โ โ โโโ mobility_validation.py # Validation & plotting
โ โ โโโ parameterization.py # Parameter extraction
โ โโโ utils/ # Utilities
โ โ โโโ __init__.py
โ โ โโโ data_utils.py # Ddata helpers
โ โ โโโ logging.py # Logging configuration
โ โ โโโ time_utils.py # Time utilities
| โโโ data/
โ โโโ params_info.parquet # Info about existing model parameters
โ โโโ params.parquet # Existing model parameters
โโโ notebooks/ # Jupyter notebooks
โ โโโ 01_demo_without_parameterization.ipynb # Demo notebook 1
โ โโโ 02_demo_including_parameterization.ipynb # Demo notebook 2
โโโ scripts/ # Python scripts
โโโ tests/ # Test suite
โโโ data/ # Data directory
โโโ plots/ # Generated plots (HTML files)
โโโ pyproject.toml # Project configuration
โโโ LICENSE # License file
โโโ README.md # This file
Installation
Prerequisites
- Python 3.11 or higher
- pip
Install from source
# Clone the repository
git clone https://github.com/ffe-munich/CHAMPPy.git
cd CHAMPPy
# Create a virtual environment
python -m venv .venv
# Activate virtual environment
# On Windows:
.\.venv\Scripts\activate
# On Linux/Mac:
source .venv/bin/activate
# Install the package
pip install .
Install from PyPI
# Create a virtual environment
python -m venv .venv
# Activate virtual environment
# On Windows:
.\.venv\Scripts\activate
# On Linux/Mac:
source .venv/bin/activate
pip install champpy
Examples
To get started, check out the interactive Jupyter notebooks in the notebooks/ directory:
-
01_demo_without_parameterization.ipynb
Simple demo showing how to generate mobility and charging profiles using existing model parameters. -
02_demo_including_parameterization.ipynb
Complete workflow including parameterization from reference data, model generation, and validation.
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