Mono and bi-Level optimization architectures for powertrain design

Abstract

Purpose The purpose of this paper is to compare two design optimization architectures for the optimal design of a complex device that integrates simultaneously the sizing of system components and the control strategy for increasing the energetic performances. The considered benchmark is a battery electric passenger car. Design/methodology/approach The optimal design of an electric vehicle powertrain is addressed within this paper, with regards to performances and range. The objectives and constraints require simulating several vehicle operating points, each of them has one degree of freedom for the electric machine control. This control is usually determined separately for each point with a sampling or an optimization loop resulting in an architecture called bi-level. In some conditions, the control variables can be transferred to the design optimization loop by suppressing the inner loop to get a mono-level formulation. The paper describes in which conditions this transformation can be done and compares the results for both architectures. Findings Results show a calculation time divided by more than 30 for the mono-level architecture compared to the natural bi-level on the study case. Even with the same models and optimization algorithms, the structure of the problem should be studied to improve the results, especially if computational cost is high. Originality/value The compared architectures bring new guidelines in the field optimal design for electric powertrains. The way to formulate a design optimization with some inner degrees of freedom can have a significant impact on computing time and on the problem understanding