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Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range

Author

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  • Jakov Topić

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10000 Zagreb, Croatia)

  • Branimir Škugor

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10000 Zagreb, Croatia)

  • Joško Deur

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10000 Zagreb, Croatia)

Abstract

A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed and transformed into 1D or 2D static maps to serve as a static input to the neural network. Several deep feedforward neural network architectures are considered for this application along with different model input formats. Two energy demand models are derived, where the first one predicts the battery state-of-charge and fuel consumption at destination for an extended range electric vehicle, and the second one predicts the vehicle all-electric range. The models are validated based on a separate test dataset when compared to the one used in neural network training, and they are compared with the traditional response surface approach to illustrate effectiveness of the method proposed.

Suggested Citation

  • Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1396-:d:221850
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Valery Vodovozov & Andrei Aksjonov & Eduard Petlenkov & Zoja Raud, 2021. "Neural Network-Based Model Reference Control of Braking Electric Vehicles," Energies, MDPI, vol. 14(9), pages 1-22, April.
    2. Shimi Sudha Letha & Math H. J. Bollen & Tatiano Busatto & Angela Espin Delgado & Enock Mulenga & Hamed Bakhtiari & Jil Sutaria & Kazi Main Uddin Ahmed & Naser Nakhodchi & Selçuk Sakar & Vineetha Ravin, 2023. "Power Quality Issues of Electro-Mobility on Distribution Network—An Overview," Energies, MDPI, vol. 16(13), pages 1-21, June.
    3. Zeng, Tao & Zhang, Caizhi & Zhou, Anjian & Wu, Qi & Deng, Chenghao & Chan, Siew Hwa & Chen, Jinrui & Foley, Aoife M., 2021. "Enhancing reactant mass transfer inside fuel cells to improve dynamic performance via intelligent hydrogen pressure control," Energy, Elsevier, vol. 230(C).
    4. Jakov Topić & Branimir Škugor & Joško Deur, 2021. "Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    5. Valery Vodovozov & Zoja Raud & Eduard Petlenkov, 2021. "Review on Braking Energy Management in Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-26, July.
    6. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    7. Liang Zhang & Shunli Wang & Daniel-Ioan Stroe & Chuanyun Zou & Carlos Fernandez & Chunmei Yu, 2020. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries," Energies, MDPI, vol. 13(8), pages 1-12, April.
    8. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
    9. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    10. Raymond Kene & Thomas Olwal & Barend J. van Wyk, 2021. "Sustainable Electric Vehicle Transportation," Sustainability, MDPI, vol. 13(22), pages 1-16, November.

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