Reduced Representations of Vector-Valued Coupling Variables in Decomposition-Based Design Optimization

1. Abstract Decomposition-based methods for system design optimization introduce consistency constraints, which contain coupling variables communicated between adjacent subproblems and link them together. When these variables are vector-valued (e.g., dynamic responses), the problem size can increase dramatically and make such methods impractical. Therefore, it is necessary to represent these vector-valued coupling variables with a reduced representation that will enable efficient optimization while maintaining an acceptable level of accuracy with respect to the original representation. This study investigates two representation techniques, radial-basis function artificial neural networks and proper orthogonal decomposition, and implements each in an analytical target cascading problem formulation for electric vehicle powertrain system optimization. Implementation of each representation technique is demonstrated and the techniques are assessed in terms of efficiency (decision vector dimensionality) and accuracy.