Nested models and optimization for the optimal design of complex multiphysics systems under optimal operations

摘要

The repaid advances of numerical modeling, global optimization and computation techniques opened new opportunities for the design optimization of complex multiphysics systems for which dedicated computer models are needed to accurately predict their performance through computation intensive numerical simulations. In contrast to traditional engineering design problems in which pure mechanical or electrical systems are designed, the multiphysics systems often consists of sub-systems of different types, such as mechanical systems, electrical systems, electro-chemical energy conversion processes, and system controls. These systems present unlimited states of operation, and rely on optimal control to achieve best overall system performance. The addition and reliance on embedded control strategies and algorithms constantly alter the states and behaviors of the multiphysics system, making the design optimization of the system much more challenging than identifying pure mechanical or electrical design parameters. A new and generic approach for addressing this issue using nested system performance model, and nested optimizations has been presented in this paper. In system modeling, the top-level system design model associates key system design parameters with given design performance measures, while the bottom-level system control model associate system operation variables with both optimal operation and design performance measures of the system. The bottom-level model is used to measure system performance from operation perspective for a given system design with given design parameters, while the top-level model is used to measure system performance from design perspective for different designs with various combinations of design parameters. In addition, different system architectures can captured by changing the top-level system design model. In system design optimization, the top-level system design optimization seeks the optimal design on key system design parameters, while the bottom-level system control optimization produced the optimal performance measure of the system used in the top-level optimization for a given set of design parameters to support the top-level system design optimization. The approach is illustrated using two optimal design examples, one on the hybrid energy storage system (ESS) for electrified vehicles (EV) and the other on active distribution network (ADN) of smart power grid with renewable energy sources.