armiro / energy-consumption-in-ev

Driving Range Prediction and Energy Consumption Rate Deviation Classification using ML Models based on Real Electric Vehicle Data

Driving Range Estimation and Energy Consumption Rate Deviation Classification in Electric Vehicles using Machine Learning Methods

Description

Using a dataset collected from https://spritmonitor.de/ driving range for electric vehicles is predicted via input features, such as driving_style, avg_speed and route_type.

• Regressors:

1. Linear Regression
2. Multilayer Perceptron (MLP)
3. Random Forest
4. AdaBoost
5. Deep Multilayer Perceptron (Deep MLP)

• Classifiers:

1. Support Vector Machines (SVM)
2. Multilayer Perceptron (MLP)
3. Random Forest
4. Deep Multilayer Perceptron (Deep MLP)

Citation

Find the related published conference paper here.

@inproceedings{amirkhani2019electric,
  title={Electric Vehicles Driving Range and Energy Consumption Investigation: A Comparative 
  Study of Machine Learning Techniques},
  author={Amirkhani, Abdollah and Haghanifar, Arman and Mosavi, Mohammad R},
  booktitle={2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)},
  pages={1--6},
  year={2019},
  organization={IEEE}
}

Input data

Dataset crawler (vehicle_crawler.py) and an example result (volkswagen_e_golf.csv) in csv file can be found here: https://github.com/armiro/crawlers/tree/master/SpritMonitor-Crawler

Run the code

First, change the dataset path in both files. Then,

• run the driving_range_prediction.py file to predict the trip distance of the electric vehicle; how long this vehicle can go in the next trip.
• run the ECR_deviation_classification.py file to classify the ECR deviation from the manufacturer; whether in this trip ECR is higher (more consumption) or lower (less consumption) than the factory-defined ECR.