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

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.