Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach
Hamza Mediouni,
Amal Ezzouhri,
Zakaria Charouh,
Khadija El Harouri,
Soumia El Hani,
Mounir Ghogho
Affiliations
Hamza Mediouni
Energy Optimization, Diagnosis and Control Team, Center for Research in Engineering and Health Sciences and Techniques, ENSAM, Mohammed V University, Rabat 10100, Morocco
Amal Ezzouhri
TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11100, Morocco
Zakaria Charouh
TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11100, Morocco
Khadija El Harouri
Energy Optimization, Diagnosis and Control Team, Center for Research in Engineering and Health Sciences and Techniques, ENSAM, Mohammed V University, Rabat 10100, Morocco
Soumia El Hani
Energy Optimization, Diagnosis and Control Team, Center for Research in Engineering and Health Sciences and Techniques, ENSAM, Mohammed V University, Rabat 10100, Morocco
Mounir Ghogho
TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11100, Morocco
Range anxiety remains one of the main hurdles to the widespread adoption of electric vehicles (EVs). To mitigate this issue, accurate energy consumption prediction is required. In this study, a hybrid approach is proposed toward this objective by taking into account driving behavior, road conditions, natural environment, and additional weight. The main components of the EV were simulated using physical and equation-based models. A rich synthetic dataset illustrating different driving scenarios was then constructed. Real-world data were also collected using a city car. A machine learning model was built to relate the mechanical power to the electric power. The proposed predictive method achieved an R2 of 0.99 on test synthetic data and an R2 of 0.98 on real-world data. Furthermore, the instantaneous regenerative braking power efficiency as a function of the deceleration level was also investigated in this study.
