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Optimizing EV Powertrain Performance and Sustainability through Constraint Prioritization in Nonlinear Model Predictive Control of Semi-Active Bidirectional DC-DC Converter with HESS

Author

Listed:
  • P. S. Praveena Krishna

    (Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India)

  • Jayalakshmi N. Sabhahit

    (Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India)

  • Vidya S. Rao

    (Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India)

  • Amit Saraswat

    (Department of Electrical Engineering, Manipal University Jaipur, Jaipur 303007, India)

  • Hannah Chaplin Laugaland

    (Department of Engineering Cybernetics, Norwegian University of Science and Technology, Postboks 8900, NO-7491 Trondheim, Norway)

  • Pramod Bhat Nempu

    (Department of Electrical and Electronics Engineering, Nitte Mahalinga Adyantaya Memorial Institute of Technology, NITTE (deemed to be university), Nitte 574110, India)

Abstract

The global transportation sector is rapidly shifting towards electrification, aiming to create more sustainable environments. As a result, there is a significant focus on optimizing performance and increasing the lifespan of batteries in electric vehicles (EVs). To achieve this, the battery pack must operate with constant current charging and discharging modes of operation. Further, in an EV powertrain, maintaining a constant DC link voltage at the input stage of the inverter is crucial for driving the motor load. To satisfy these two conditions simultaneously during the energy transfer, a hybrid energy storage system (HESS) consisting of a lithium–ion battery and a supercapacitor (SC) connected to the semi-active topology of the bidirectional DC–DC converter (SAT-BDC) in this research work. However, generating the duty cycle for the switches to regulate the operation of SAT-BDC is complex due to the simultaneous interaction of the two mentioned constraints: regulating the DC link voltage by tracking the reference and maintaining the battery current at a constant value. Therefore, this research aims to efficiently resolve the issue by incorporating a highly flexible nonlinear model predictive control (NMPC) to control the switches of SAT-BDC. Furthermore, the converter system design is tested for operational performance using MATLAB 2022B with the battery current and the DC link voltage with different priorities. In the NMPC approach, these constraints are carefully evaluated with varying prioritizations, representing a crucial trade-off in optimizing EV powertrain operation. The results demonstrate that battery current prioritization yields better performance than DC link voltage prioritization, extending the lifespan and efficiency of batteries. Thus, this research work further aligns with the conceptual realization of the sustainability goals by minimizing the environmental impact associated with battery production and disposal.

Suggested Citation

  • P. S. Praveena Krishna & Jayalakshmi N. Sabhahit & Vidya S. Rao & Amit Saraswat & Hannah Chaplin Laugaland & Pramod Bhat Nempu, 2024. "Optimizing EV Powertrain Performance and Sustainability through Constraint Prioritization in Nonlinear Model Predictive Control of Semi-Active Bidirectional DC-DC Converter with HESS," Sustainability, MDPI, vol. 16(18), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:8123-:d:1479951
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    References listed on IDEAS

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    1. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    2. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    3. Takele Ferede Agajie & Ahmed Ali & Armand Fopah-Lele & Isaac Amoussou & Baseem Khan & Carmen Lilí Rodríguez Velasco & Emmanuel Tanyi, 2023. "A Comprehensive Review on Techno-Economic Analysis and Optimal Sizing of Hybrid Renewable Energy Sources with Energy Storage Systems," Energies, MDPI, vol. 16(2), pages 1-26, January.
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