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Energy-Saving of Battery Electric Vehicle Powertrain and Efficiency Improvement during Different Standard Driving Cycles

Author

Listed:
  • Khairy Sayed

    (Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag 82524, Egypt)

  • Ahmed Kassem

    (Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag 82524, Egypt)

  • Hedra Saleeb

    (Electrical Department, Faculty of Technology and Education, Sohag University, Sohag 82524, Egypt)

  • Ali S. Alghamdi

    (Electrical Engineering Department, College of Engineering, Majmaah University, Almajmaah 15341, Saudi Arabia)

  • Ahmed G. Abo-Khalil

    (Electrical Engineering Department, College of Engineering, Majmaah University, Almajmaah 15341, Saudi Arabia
    Electrical Engineering Department, College of Engineering, Assiut University, Assiut 71515, Egypt)

Abstract

This article focuses on the energy-saving of each driving distance for battery electric vehicle (BEV) applications, by developing a more effective energy management strategy (EMS), under different driving cycles. Fuzzy logic control (FLC) is suggested to control the power management unit (PMU) for the battery management system (BMS) for BEV applications. The adaptive neural fuzzy inference system (ANFIS) is a modeling technique that is mainly based on data. Membership functions and FLC rules can be improved by simply training the ANFIS with real driving cycle data gathered from the MATLAB/SIMULINK program. Then, FLC console blocks are rewritten by enhanced membership functions by ANFIS traineeship. Two different driving cycles are chosen to check the improvement in the efficiency of this proposed system. The suggested control system is validated by simulation and comparison with the traditional proportional-integral (PI) control. The optimized FLC shows better energy-saving.

Suggested Citation

  • Khairy Sayed & Ahmed Kassem & Hedra Saleeb & Ali S. Alghamdi & Ahmed G. Abo-Khalil, 2020. "Energy-Saving of Battery Electric Vehicle Powertrain and Efficiency Improvement during Different Standard Driving Cycles," Sustainability, MDPI, vol. 12(24), pages 1-26, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10466-:d:462108
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    References listed on IDEAS

    as
    1. Wang, Hewu & Zhang, Xiaobin & Ouyang, Minggao, 2015. "Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing," Applied Energy, Elsevier, vol. 157(C), pages 710-719.
    2. Polterovich, Victor & Popov, Vladimir, 2006. "Эволюционная Теория Экономической Политики: Часть I: Опыт Быстрого Развития [An Evolutionary Theory of Economic Policy: Part I: The Experience of Fast Development]," MPRA Paper 22168, University Library of Munich, Germany.
    3. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    4. Khairy Sayed & Ziad M. Ali & Mujahed Aldhaifallah, 2020. "Phase-Shift PWM-Controlled DC–DC Converter with Secondary-Side Current Doubler Rectifier for On-Board Charger Application," Energies, MDPI, vol. 13(9), pages 1-18, May.
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    Cited by:

    1. Liu, Huanlong & Chen, Guanpeng & Li, Dafa & Wang, Jiawei & Zhou, Jianyi, 2021. "Energy active adjustment and bidirectional transfer management strategy of the electro-hydrostatic hydraulic hybrid powertrain for battery bus," Energy, Elsevier, vol. 230(C).
    2. Mauro Zucca & Vincenzo Cirimele & Jorge Bruna & Davide Signorino & Erika Laporta & Jacopo Colussi & Miguel Angel Alonso Tejedor & Federico Fissore & Umberto Pogliano, 2021. "Assessment of the Overall Efficiency in WPT Stations for Electric Vehicles," Sustainability, MDPI, vol. 13(5), pages 1-19, February.
    3. Liao, Peng & Tang, Tie-Qiao & Liu, Ronghui & Huang, Hai-Jun, 2021. "An eco-driving strategy for electric vehicle based on the powertrain," Applied Energy, Elsevier, vol. 302(C).
    4. Stefan Tabacu & Dragos Popa, 2023. "Backward-Facing Analysis for the Preliminary Estimation of the Vehicle Fuel Consumption," Sustainability, MDPI, vol. 15(6), pages 1-19, March.

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