NSGA-II-Based Codesign Optimization for Power Conversion and Controller Stages of Interleaved Boost Converters in Electric Vehicle Drivetrains

Dai-Duong Tran, Sajib Chakraborty, Yuanfeng Lan, Mohamed El Baghdadi and Omar Hegazy
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Dai-Duong Tran: Department of Electrical Machines and Energy Technology (ETEC) & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Sajib Chakraborty: Department of Electrical Machines and Energy Technology (ETEC) & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Yuanfeng Lan: Department of Electrical Machines and Energy Technology (ETEC) & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Mohamed El Baghdadi: Department of Electrical Machines and Energy Technology (ETEC) & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Omar Hegazy: Department of Electrical Machines and Energy Technology (ETEC) & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium

Energies, 2020, vol. 13, issue 19, 1-31

Abstract: This article proposes a holistic codesign optimization framework (COF) to simultaneously optimize a power conversion stage and a controller stage using a dual-loop control scheme for multiphase SiC-based DC/DC converters. In this study, the power conversion stage adopts a non-isolated interleaved boost converter (IBC). Besides, the dual-loop control scheme uses type-III controllers for both inner- and outer- loops to regulate the output voltage of the IBC and tackle its non-minimum phase issue. Based on the converter architecture, a multi-objective optimization (MOO) problem including four objective functions (OFs) is properly formulated for the COF. To this end, total input current ripple, total weight of inductors and total power losses are selected as three OFs for the power conversion stage whilst one OF called integral of time-weighted absolute error is considered for the controller stage. The OFs are expressed in analytical forms. To solve the MOO problem, the COF utilizes a non-dominated sorted genetic algorithm (NSGA-II) in combination with an automatic decision-making algorithm to obtain the optimal design solution including the number of phases, switching frequency, inductor size, and the control parameters of type-III controllers. Furthermore, compared to the conventional ‘k-factor’ based controller, the optimal controller exhibits better dynamic responses in terms of undershoot/overshoot and settling time for the output voltage under load disturbances. Moreover, a liquid-cooled SiC-based converter is prototyped and its optimal controller is implemented digitally in dSPACE MicroLabBox. Finally, the experimental results with static and dynamic tests are presented to validate the outcomes of the proposed COF.

Keywords: simultaneous codesign optimization; non-dominated sorted genetic algorithm; multiport converter; interleaved boost converter; optimal type-III controller; SiC MOSFET modules; finite element analysis; electric vehicle drivetrains (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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