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Hybrid Power Management Strategy with Fuel Cell, Battery, and Supercapacitor for Fuel Economy in Hybrid Electric Vehicle Application

Author

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  • V. Mounica

    (School of Electrical Engineering (SELECT), Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India)

  • Y. P. Obulesu

    (School of Electrical Engineering (SELECT), Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India)

Abstract

The power management strategy (PMS) is intimately linked to the fuel economy in the hybrid electric vehicle (HEV). In this paper, a hybrid power management scheme is proposed; it consists of an adaptive neuro-fuzzy inference method (ANFIS) and the equivalent consumption minimization technique (ECMS). Artificial intelligence (AI) is a key development for managing power among various energy sources. The hybrid power supply is an eco-acceptable system that includes a proton exchange membrane fuel cell (PEMFC) as a primary source and a battery bank and ultracapacitor as electric storage systems. The Haar wavelet transform method is used to calculate the stress σ on each energy source. The proposed model is developed in MATLAB/Simulink software. The simulation results show that the proposed scheme meets the power demand of a typical driving cycle, i.e., Highway Fuel Economy Test Cycle (HWFET) and Worldwide Harmonized Light Vehicles Test Procedures (WLTP—Class 3), for testing the vehicle performance, and assessment has been carried out for various PMS based on the consumption of hydrogen, overall efficiency, state of charge of ultracapacitors and batteries, stress on hybrid sources and stability of the DC bus. By combining ANFIS and ECMS, the consumption of hydrogen is minimized by 8.7% compared to the proportional integral (PI), state machine control (SMC), frequency decoupling fuzzy logic control (FDFLC), equivalent consumption minimization strategy (ECMS) and external energy minimization strategy (EEMS).

Suggested Citation

  • V. Mounica & Y. P. Obulesu, 2022. "Hybrid Power Management Strategy with Fuel Cell, Battery, and Supercapacitor for Fuel Economy in Hybrid Electric Vehicle Application," Energies, MDPI, vol. 15(12), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4185-:d:833337
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    References listed on IDEAS

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    Cited by:

    1. Mehroze Iqbal & Amel Benmouna & Frederic Claude & Mohamed Becherif, 2023. "Efficient and Reliable Power-Conditioning Stage for Fuel Cell-Based High-Power Applications," Energies, MDPI, vol. 16(13), pages 1-15, June.
    2. Laura Zecchi & Giulia Sandrini & Marco Gadola & Daniel Chindamo, 2022. "Modeling of a Hybrid Fuel Cell Powertrain with Power Split Logic for Onboard Energy Management Using a Longitudinal Dynamics Simulation Tool," Energies, MDPI, vol. 15(17), pages 1-18, August.
    3. Aissa Benhammou & Mohammed Amine Hartani & Hamza Tedjini & Hegazy Rezk & Mujahed Al-Dhaifallah, 2023. "Improvement of Autonomy, Efficiency, and Stress of Fuel Cell Hybrid Electric Vehicle System Using Robust Controller," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    4. Mohammad Kamrul Hasan & AKM Ahasan Habib & Shayla Islam & Mohammed Balfaqih & Khaled M. Alfawaz & Dalbir Singh, 2023. "Smart Grid Communication Networks for Electric Vehicles Empowering Distributed Energy Generation: Constraints, Challenges, and Recommendations," Energies, MDPI, vol. 16(3), pages 1-20, January.
    5. Xueliang Li & Xinyu Kang & Xin Ba & Zengxiong Peng & Shujun Yang & Zhifu Zhao, 2022. "A Design Methodology for Dual-Mode Electro-Mechanical Transmission Scheme Based on Jointing Characteristics," Energies, MDPI, vol. 15(15), pages 1-15, July.
    6. Ahmed Abdelhak Smadi & Farid Khoucha & Yassine Amirat & Abdeldjabar Benrabah & Mohamed Benbouzid, 2023. "Active Disturbance Rejection Control of an Interleaved High Gain DC-DC Boost Converter for Fuel Cell Applications," Energies, MDPI, vol. 16(3), pages 1-17, January.
    7. Hu, Jianjun & Wang, Yangguang & Zou, Lingbo & Wang, Zhouxin, 2023. "Adaptive rule control strategy for composite energy storage fuel cell vehicle based on vehicle operating state recognition," Renewable Energy, Elsevier, vol. 204(C), pages 166-175.

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