This repository focuses on the research of speed optimization of eco-driving for connected electric vehicles based on reinforcement learning.
The project aims to develop an eco-driving system for electric vehicles that leverages the benefits of connectivity and reinforcement learning techniques for optimizing energy usage and reducing carbon emissions.
Integration of RL-based eco-driving algorithms for connected electric vehicles Training the eco-driving model based on historical driving data Optimization of vehicle speed to minimize energy consumption and carbon emissions Implementation of connected vehicle technology to gather and transmit environmental and traffic data Real-time feedback and recommendations to drivers for energy-efficient and eco-friendly driving behavior Evaluation of the eco-driving system's performance based on energy consumption, travel time, and carbon footprint The scalability of the system to consider multiple vehicles and complex traffic scenarios.
Python Reinforcement learning libraries (e.g., TensorFlow, Keras, PyTorch) Simulation tools (e.g., SUMO, OMNET++) Connected vehicle technology and protocols (e.g., DSRC, V2X) Data analysis and visualization tools (e.g., pandas, Matplotlib)
Develop a sustainable and energy-efficient eco-driving system for electric vehicles Reduce carbon emissions and improve air quality by optimizing driving behavior Encourage and incentivize eco-friendly driving behavior among vehicle users Provide real-time feedback and recommendations to drivers for better energy efficiency, safety, and comfort. Enhance the scalability and applicability of eco-driving systems to multiple vehicles and cities.
Integration of additional environmental and traffic data sources (e.g., weather, road conditions, traffic signals) Enhancement of the system's decision-making capabilities using advanced reinforcement learning algorithms Collaboration with city planners and transportation authorities to incorporate larger traffic management strategies into the eco-driving system Integration of the eco-driving system with smart charging stations and renewable energy sources.
Contributors are welcome to suggest and work on additional features or improvements. All contributions, including bug reports, tests, documentation, and code, should be submitted via pull requests. Please see the CONTRIBUTING.md document for more details.
This project is licensed under the MIT License. See the LICENSE file for details.
For questions, feedback, or suggestions, please contact the project maintainer at maintainer@gmail.com.