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Sustainable Energy Management in Electric Vehicles Through a Fuzzy Logic-Based Strategy

Author

Listed:
  • Efe Savran

    (Mechanical Engineering Department, Bursa Uludag University, Bursa 16059, Turkey)

  • Esin Karpat

    (Electrical-Electronics Engineering Department, Bursa Uludag University, Bursa 16059, Turkey)

  • Fatih Karpat

    (Mechanical Engineering Department, Bursa Uludag University, Bursa 16059, Turkey)

Abstract

The purpose of this study was to develop a fuzzy logic controller (FLC)-based energy management strategy for battery electric vehicles that enables them to reduce their energy consumption and carbon emission levels without sacrificing their performance. An electric vehicle model was developed in MATLAB/Simulink using a virtual battery and validated with real-world driving tests to save time and money. An in-depth investigation is conducted on both virtual and real vehicles to confirm the effectiveness of the proposed energy management strategy. This study shows that by using FLC-based energy management, an energy consumption advantage of 9.16% can be achieved while maintaining acceptable performance levels in real-world driving conditions. This advantage results in significant reductions annually: 1044.09 tons of CO 2 emissions, USD 164,770.65 in savings for electric bus lines, and 5079 battery cycles. For European passenger electric vehicles, this corresponds to 405,657.6 tons of CO 2 emissions reduced, USD 64,017,840 saved, and 5.071 battery cycles per vehicle. This strategy not only enhances energy efficiency but also contributes to long-term sustainability in public transportation systems, particularly for electric bus fleets, which play a critical role in urban mobility.

Suggested Citation

  • Efe Savran & Esin Karpat & Fatih Karpat, 2024. "Sustainable Energy Management in Electric Vehicles Through a Fuzzy Logic-Based Strategy," Sustainability, MDPI, vol. 17(1), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:17:y:2024:i:1:p:89-:d:1553938
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    References listed on IDEAS

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    1. Bao, Xinyuan & Chen, Liping & Lopes, António M. & Li, Xin & Xie, Siqiang & Li, Penghua & Chen, YangQuan, 2023. "Hybrid deep neural network with dimension attention for state-of-health estimation of Lithium-ion Batteries," Energy, Elsevier, vol. 278(C).
    2. Basma, Hussein & Mansour, Charbel & Haddad, Marc & Nemer, Maroun & Stabat, Pascal, 2022. "Energy consumption and battery sizing for different types of electric bus service," Energy, Elsevier, vol. 239(PE).
    3. Naef, Alain, 2024. "The impossible love of fossil fuel companies for carbon taxes," Ecological Economics, Elsevier, vol. 217(C).
    4. Chen, Liping & Xie, Siqiang & Lopes, António M. & Li, Huafeng & Bao, Xinyuan & Zhang, Chaolong & Li, Penghua, 2024. "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion," Energy, Elsevier, vol. 286(C).
    5. Vasyl Mateichyk & Nataliia Kostian & Miroslaw Smieszek & Jakub Mosciszewski & Liudmyla Tarandushka, 2023. "Evaluating Vehicle Energy Efficiency in Urban Transport Systems Based on Fuzzy Logic Models," Energies, MDPI, vol. 16(2), pages 1-22, January.
    6. Martin Weiss & Trey Winbush & Alexandra Newman & Eckard Helmers, 2024. "Energy Consumption of Electric Vehicles in Europe," Sustainability, MDPI, vol. 16(17), pages 1-26, August.
    7. Yan Xing & Quanbo Fu & Yachao Li & Hanshuo Chu & Enyi Niu, 2023. "Optimal Model of Electric Bus Scheduling Based on Energy Consumption and Battery Loss," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    Full references (including those not matched with items on IDEAS)

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