Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach

Hamza Mediouni (), Amal Ezzouhri, Zakaria Charouh, Khadija El Harouri, Soumia El Hani and Mounir Ghogho
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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

Energies, 2022, vol. 15, issue 17, 1-17

Abstract: 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 R 2 of 0.99 on test synthetic data and an R 2 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.

Keywords: range anxiety; hybrid approach; synthetic dataset; real-world data; instantaneous regenerative braking power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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