The ACN Portal is a suite of research tools developed at Caltech to accelerate the pace of large-scale EV charging research. Checkout the documentation at https://acnportal.readthedocs.io/en/latest/.
For more information about the ACN Portal and EV reasearch at Caltech check out https://ev.caltech.edu.
The ACN-Data Dataset is a collection of EV charging sessions collected at Caltech and NASA's Jet Propulsion Laboratory (JPL). This basic Python client simplifies the process of pulling data from his dataset via its public API.
ACN-Sim is a simulation environment for large-scale EV charging algorithms. It interfaces with ACN-Data to provide access to realistic test cases based on actual user behavior.
algorithms is a package of common EV charging algorithms which can be used for comparison when evaluating new algorithms.
This package is intended to be populated by the community. If you have a promising EV charging algorithm, please implement it as a subclass of BasicAlgorithm and send a pull request.
Download or clone this repository. Navigate to its root directory. Install using pip.
pip install .See the tutorials directory for jupyter notebooks that you can
run to learn some of the functionality of acnportal. These
tutorials are also included on the readthedocs page. Additional
demos and case studies can be found at
https://github.com/caltech-netlab/acnportal-experiments
We also have a video series of acnportal demos, which can
be found at TODO.
Tests may be run after installation by executing
python -m unittest discover -vRemove -v after discover to suppress verbose output.
If you're submitting a bug report, feature request, question, or documentation suggestion, please submit the issue through Github and follow the templates outlined there.
If you are contributing code to the project, please view the contributing guidelines here.
Contact the ACN Research Portal team at mailto:ev-help@caltech.edu with any questions, or submit a question through Github issues.