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Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles

Author

Listed:
  • Vishnu P. Sidharthan

    (Department of Electrical and Electronics Engineering, National Institute of Technology, Surathkal, Mangaluru 575025, Karnataka, India)

  • Yashwant Kashyap

    (Department of Electrical and Electronics Engineering, National Institute of Technology, Surathkal, Mangaluru 575025, Karnataka, India)

  • Panagiotis Kosmopoulos

    (Institute for Environmental Research and Sustainable Development, National Observatory of Athens (IERSD/NOA), 15236 Athens, Greece)

Abstract

The energy utilization of the transportation industry is increasing tremendously. The battery is one of the primary energy sources for a green and clean mode of transportation, but variations in driving profiles (NYCC, Artemis Urban, WLTP class-1) and higher C-rates affect the battery performance and lifespan of battery electric vehicles (BEVs). Hence, as a singular power source, batteries have difficulty in tackling these issues in BEVs, highlighting the significance of hybrid-source electric vehicles (HSEVs). The supercapacitor (SC) and photovoltaic panels (PVs) are the auxiliary power sources coupled with the battery in the proposed hybrid electric three-wheeler (3W). However, energy management strategies (EMS) are critical to ensure optimal and safe power allocation in HSEVs. A novel adaptive Intelligent Hybrid Source Energy Management Strategy (IHSEMS) is proposed to perform energy management in hybrid sources. The IHSEMS optimizes the power sources using an absolute energy-sharing algorithm to meet the required motor power demand using the fuzzy logic controller. Techno-economic assessment wass conducted to analyze the effectiveness of the IHSEMS. Based on the comprehensive discussion, the proposed strategy reduces peak battery power by 50.20% compared to BEVs. It also reduces the battery capacity loss by 48.1%, 44%, and 24%, and reduces total operation cost by 60%, 43.9%, and 23.68% compared with standard BEVs, state machine control (SMC), and frequency decoupling strategy (FDS), respectively.

Suggested Citation

  • Vishnu P. Sidharthan & Yashwant Kashyap & Panagiotis Kosmopoulos, 2023. "Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles," Energies, MDPI, vol. 16(3), pages 1-26, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1214-:d:1044164
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    References listed on IDEAS

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    1. Jamila Snoussi & Seifeddine Ben Elghali & Mohamed Benbouzid & Mohamed Faouzi Mimouni, 2018. "Auto-Adaptive Filtering-Based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-20, August.
    2. Song, Ziyou & Li, Jianqiu & Hou, Jun & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "The battery-supercapacitor hybrid energy storage system in electric vehicle applications: A case study," Energy, Elsevier, vol. 154(C), pages 433-441.
    3. Castaings, Ali & Lhomme, Walter & Trigui, Rochdi & Bouscayrol, Alain, 2016. "Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints," Applied Energy, Elsevier, vol. 163(C), pages 190-200.
    4. Wieczorek, Maciej & Lewandowski, Mirosław, 2017. "A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm," Applied Energy, Elsevier, vol. 192(C), pages 222-233.
    5. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    6. Niu, Junyan & Zhuang, Weichao & Ye, Jianwei & Song, Ziyou & Yin, Guodong & Zhang, Yuanjian, 2022. "Optimal sizing and learning-based energy management strategy of NCR/LTO hybrid battery system for electric taxis," Energy, Elsevier, vol. 257(C).
    7. Hoai-Linh T. Nguyen & Bảo-Huy Nguyễn & Thanh Vo-Duy & João Pedro F. Trovão, 2021. "A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles," Energies, MDPI, vol. 14(12), pages 1-23, June.
    8. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    9. Fang Zhou & Feng Xiao & Cheng Chang & Yulong Shao & Chuanxue Song, 2017. "Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-21, July.
    10. Sekhar Raghu Raman & Ka-Wai (Eric) Cheng & Xiang-Dang Xue & Yat-Chi Fong & Simon Cheung, 2021. "Hybrid Energy Storage System with Vehicle Body Integrated Super-Capacitor and Li-Ion Battery: Model, Design and Implementation, for Distributed Energy Storage," Energies, MDPI, vol. 14(20), pages 1-22, October.
    11. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Pavlos Papageorgiou & Konstantinos Oureilidis & Anna Tsakiri & Georgios Christoforidis, 2023. "A Modified Decentralized Droop Control Method to Eliminate Battery Short-Term Operation in a Hybrid Supercapacitor/Battery Energy Storage System," Energies, MDPI, vol. 16(6), pages 1-21, March.
    2. Nicola Campagna & Vincenzo Castiglia & Francesco Gennaro & Angelo Alberto Messina & Rosario Miceli, 2024. "Fuel Cell-Based Inductive Power Transfer System for Supercapacitor Constant Current Charging," Energies, MDPI, vol. 17(14), pages 1-22, July.

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