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Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions

Author

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  • Seyed Mahdi Miraftabzadeh

    (Department of Energy, Politecnico di Milano, Via La Masa, 34, 20156 Milan, Italy)

  • Michela Longo

    (Department of Energy, Politecnico di Milano, Via La Masa, 34, 20156 Milan, Italy)

  • Federica Foiadelli

    (Department of Energy, Politecnico di Milano, Via La Masa, 34, 20156 Milan, Italy)

Abstract

The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO 2 reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel estimation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow. Moreover, the pattern identifies Thursdays and Fridays as the days of peak energy usage, while weekend days and holidays present the lowest range.

Suggested Citation

  • Seyed Mahdi Miraftabzadeh & Michela Longo & Federica Foiadelli, 2021. "Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions," Energies, MDPI, vol. 14(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:854-:d:494747
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    Cited by:

    1. Xiaoyu Li & Tengyuan Wang & Jiaxu Li & Yong Tian & Jindong Tian, 2022. "Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model," Energies, MDPI, vol. 15(11), pages 1-17, June.
    2. Gianfranco Di Lorenzo & Erika Stracqualursi & Rodolfo Araneo, 2022. "The Journey Towards the Energy Transition: Perspectives from the International Conference on Environment and Electrical Engineering (EEEIC)," Energies, MDPI, vol. 15(18), pages 1-5, September.
    3. Manfred Dollinger & Gerhard Fischerauer, 2021. "Model-Based Range Prediction for Electric Cars and Trucks under Real-World Conditions," Energies, MDPI, vol. 14(18), pages 1-27, September.
    4. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    5. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.
    6. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.
    7. Quynh T. Tran & Leon Roose & Chayaphol Vichitpunt & Kumpanat Thongmai & Krittanat Noisopa, 2022. "A Comprehensive Model to Estimate Electric Vehicle Battery’s State of Charge for a Pre-Scheduled Trip Based on Energy Consumption Estimation," Clean Technol., MDPI, vol. 5(1), pages 1-13, December.

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