As demand continues to surge, the necessity to enhance energy efficiency and extend battery life grows for both manufacturers and users. Traditional methods to achieve better energy efficiency are not very effective due to the high complexity of factors influencing EV performance. In this project, an analysis of driving patterns, charging behavior, and local environment conditions will be performed using data analysis and machine learning. These predictive models will therefore improve the performance of the batteries and improve the accuracy of the range predictions to make electric vehicles more efficient and sustainable.
Week 1-2: Initial Planning and Requirement Analysis
Define Project objectives, scope, and high-level requirements
Gather data and other resources necessary.
Week 3-4: Data Collection and Preprocessing
Collection of data regarding driving patterns, charging behavior, and environmental conditions. Extract the features and clean the data.
Train machine learning models to predict and optimize energy efficiency. The model's accuracy and performance should be validated with test data.
System design for energy efficiency optimization
Integrate the model with the user interface
Testing and refinement by system performance metrics and feedback from users
Week 9-10: System Integration and Testing Phase
Thorough Berry testing for accurate energy efficiency optimizations. System performance monitoring and user interaction.
Implement continuous improvement based on analytics and user feedback.
Week 11-12: Final Evaluation and Reporting Phase
Conduct final evaluations and validation of the energy efficiency optimization system.
Compile the final project report and documentation.
Present individual report presentations by students.
