Electric Vehicle Range Prediction Project

Table of Contents

• EVPRE Configuration and Usage
• Dependencies Summary

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Software Description

The Electric Vehicle Range Prediction Project is a piece of software that uses various services and data to make route predictions based on energy consumption.

Running the Software

Now that all dependencies have been downloaded, simply navigate to where the code is saved, use your favorite shell and run 'jupyter notebook'. This will open up a web browser with all the code to run.

EVPRE Configuration and Usage

Config.py and .env

The Config.py requires the package python-dotenv. In the root level of the application a .env file will need to be created like this:

GOOGLE_MAPS_KEY=GOOGLE MAPS ELEVATION API KEY
WEATHER_KEY=OPEN WEATHER API KEY
TRAFFIC_KEY=TOMTOM API KEY

• GOOGLE MAPS ELEVATION API KEY
• OPEN WEATHER API KEY
• TOMTOM API KEY

See the documentation on python-dotenv for more details.

Jupyter Notebook

A sample Jupyter notebook (main.ipynb) is included to show example usage of the EVPRE software.

EVPRE Configuration

To configure the EVPRE software, modify config.py with the relevant vehicle and environment data.

See the models README.md for more information on each model and its parameters.

EVPRE Usage

For example, to utilize the simple energy model,

route_estimator_simple_model = RouteEstimator(Config())
route_estimator_simple_model.activate_energy_model()
route_map_simple_e = route_estimator_simple_model.create_map()
route_map_simple_e

Creates a map where a user can find the most efficient route between two markers.

To generate an isochrone with the given parameters,

range_estimator_simple_model = RangeEstimator(Config(), route_estimator_simple_model.get_graph())
range_estimator_simple_model.generate_isochrone()

Energy and Vehicle Model Information

See the models README.md

Installation

1. To install all of the required packages: pip install -r requirements.txt

2. To install fastsim: Navigate to the directory (example: fastsim-2021a) that contains the fastsim source code. Run pip install . to install it to your pip.

3. Make sure you have installed python-dotenv using pip install python-dotenv.

4. Make sure networkx and osmnx are installed to your pip.

5. Make a .env file in the root containing the keys like above.

6. After everything is installed, run jupyter-lab in the root directory of the application. This should open a jupyter notebook. The main examples are in main.ipynb.