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Accurate and Efficient Energy Management System of Fuel Cell/Battery/Supercapacitor/AC and DC Generators Hybrid Electric Vehicles

Author

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  • Aissa Benhammou

    (Laboratory of Instrumentation and Advanced Materials (LIMA), Nour Bachir University Center, El-Bayadh 32000, Algeria
    SGRE Laboratory, Tahri Mohamed University, Bechar 08000, Algeria)

  • Hamza Tedjini

    (SGRE Laboratory, Tahri Mohamed University, Bechar 08000, Algeria)

  • Mohammed Amine Hartani

    (Sustainable Development and Computer Science Laboratory SDCS-L, Ahmed Draia University, Adrar 01000, Algeria)

  • Rania M. Ghoniem

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Ali Alahmer

    (Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA
    Department of Mechanical Engineering, Faculty of Engineering, Tafila Technical University, Tafila 66110, Jordan)

Abstract

The development of hybrid electric vehicles (HEVs) is rapidly gaining traction as a viable solution for reducing carbon emissions and improving fuel efficiency. One type of HEV that is gaining significant interest is the fuel cell/battery/supercapacitor HEV (FC/Bat/SC HEV), which combines fuel cell, battery, supercapacitor, AC, and DC generators. These FC/B/SC HEVs are particularly appealing because they excel at efficiently managing energy and cater to a wide range of driving requirements. This study presents a novel approach for exploiting the kinetic energy of a sensorless HEV. The vehicle has a primary fuel cell resource, a supercapacitor, and lithium-ion battery energy storage banks, where each source is connected to a special converter. The obtained hybrid system allows the vehicle to enhance autonomy, support the fuel cell during low production moments, and improve transient and steady-state load requirements. The exploitation of kinetic energy is performed by the DC and AC generators that are linked to the electric vehicle front wheels to transfer the HEV’s wheel rotation into power, contributing to the overall power balance of the vehicle. The energy management system for electric vehicles determines the FC setpoint power through the classical state machine method. At the same time, a robust speed controller-based artificial intelligence algorithm reduces power losses and enhances the supply efficiency for the vehicle. Furthermore, we evaluate the performance of a robust controller with a speed estimator, specifically using the adaptive neuro-fuzzy inference system (ANFIS) and the model reference adaptive system (MRAS) estimator in conjunction with the direct torque control-support vector machine (DTC-SVM), to enhance the torque and speed performance of HEVs. The results demonstrate the feasibility and reliability of the vehicle while utilizing the additional DC and AC generators to extract free kinetic energy, both of which contributed to 28% and 24% of the total power for the vehicle, respectively. This approach leads to a vehicle supply efficiency exceeding 96%, reducing the burden on fuel cells and batteries and resulting in a significant reduction in fuel consumption, which is estimated to range from 25% to 35%.

Suggested Citation

  • Aissa Benhammou & Hamza Tedjini & Mohammed Amine Hartani & Rania M. Ghoniem & Ali Alahmer, 2023. "Accurate and Efficient Energy Management System of Fuel Cell/Battery/Supercapacitor/AC and DC Generators Hybrid Electric Vehicles," Sustainability, MDPI, vol. 15(13), pages 1-27, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10102-:d:1179488
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    References listed on IDEAS

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    Cited by:

    1. Yong Liu & Jimin Ni & Rong Huang & Xiuyong Shi & Zheng Xu & Yanjun Wang & Yuan Lu, 2024. "Optimization of Energy Management Strategy of a PHEV Based on Improved PSO Algorithm and Energy Flow Analysis," Sustainability, MDPI, vol. 16(20), pages 1-27, October.
    2. Mohammad Aldossary & Hatem A. Alharbi & Nasir Ayub, 2024. "Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability," Mathematics, MDPI, vol. 12(17), pages 1-29, August.
    3. Rania M. Ghoniem & Ali Alahmer & Hegazy Rezk & Samer As’ad, 2023. "Optimal Design and Sizing of Hybrid Photovoltaic/Fuel Cell Electrical Power System," Sustainability, MDPI, vol. 15(15), pages 1-19, August.
    4. Yu Nie & Xiaotian Zhang & Yihua Hu & Mohammad Nasr Esfahani, 2024. "Automatic Power Direction Control of Dual Active Bridge/Triple Active Bridge Converter in Emergency Energy Supply for Sustainability," Sustainability, MDPI, vol. 16(18), pages 1-18, September.

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