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Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction

Author

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  • Loïc Joud

    (DRIVE EA1859, Université Bourgogne Franche-Comté, 58027 Nevers, France
    DANIELSON ENGINEERING, Technopôle du Circuit, 58470 Magny-Cours, France)

  • Rui Da Silva

    (DANIELSON ENGINEERING, Technopôle du Circuit, 58470 Magny-Cours, France)

  • Daniela Chrenko

    (Femto-ST, CNRS, Université Bourgogne Franche-Comté, 90010 Belfort, France)

  • Alan Kéromnès

    (DRIVE EA1859, Université Bourgogne Franche-Comté, 58027 Nevers, France)

  • Luis Le Moyne

    (DRIVE EA1859, Université Bourgogne Franche-Comté, 58027 Nevers, France)

Abstract

The objective of this work is to develop an optimal management strategy to improve the energetic efficiency of a hybrid electric vehicle. The strategy is built based on an extensive experimental study of mobility in order to allow trips recognition and prediction. For this experimental study, a dedicated autonomous acquisition system was developed. On working days, most trips are constrained and can be predicted with a high level of confidence. The database was built to assess the energy and power needed based on a static model for three types of cars. It was found that most trips could be covered by a 10 kWh battery. Regarding the optimization strategy, a novel real time capable energy management approach based on dynamic vehicle model was created using Energetic Macroscopic Representation. This real time capable energy management strategy is done by a combination of cycle prediction based on results obtained during the experimental study. The optimal control strategy for common cycles based on dynamic programming is available in the database. When a common cycle is detected, the pre-determined optimum strategy is applied to the similar upcoming cycle. If the real cycle differs from the reference cycle, the control strategy is adapted using quadratic programming. To assess the performance of the strategy, its resulting fuel consumption is compared to the global optimum calculated using dynamic programming and used as a reference; its optimality factor is above 98%.

Suggested Citation

  • Loïc Joud & Rui Da Silva & Daniela Chrenko & Alan Kéromnès & Luis Le Moyne, 2020. "Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction," Energies, MDPI, vol. 13(11), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2954-:d:369011
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    References listed on IDEAS

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    Cited by:

    1. Tuyen Nguyen & Yannick Rauch & Reiner Kriesten & Daniela Chrenko, 2023. "Approach for a Global Route-Based Energy Management System for Electric Vehicles with a Hybrid Energy Storage System," Energies, MDPI, vol. 16(2), pages 1-20, January.
    2. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    3. Kapetanović, Marko & Núñez, Alfredo & van Oort, Niels & Goverde, Rob M.P., 2021. "Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trains," Applied Energy, Elsevier, vol. 294(C).
    4. Anatole Desreveaux & Alain Bouscayrol & Elodie Castex & Rochdi Trigui & Eric Hittinger & Gabriel-Mihai Sirbu, 2020. "Annual Variation in Energy Consumption of an Electric Vehicle Used for Commuting," Energies, MDPI, vol. 13(18), pages 1-15, September.
    5. Zhang, Zhen & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Yang, Jian & Jia, Qingxiao, 2023. "Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle," Energy, Elsevier, vol. 269(C).

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