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

P. S. Praveena Krishna, Jayalakshmi N. Sabhahit (), Vidya S. Rao, Amit Saraswat, Hannah Chaplin Laugaland and Pramod Bhat Nempu
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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

Sustainability, 2024, vol. 16, issue 18, 1-21

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.

Keywords: hybrid energy storage system; nonlinear model predictive control; life cycle of lithium–ion battery; prioritization of constraint variables; semi-active topology of bidirectional DC–DC converter (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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