Investigation On Robust Multi-objective Evolutionary Optimization And Optimal Resonant Topology Search Of ICPT System

Inductive Coupled Power Transfer(ICPT) technology is one kind of wireless power transfer technologies based on electromagnetic induction. Traditional ICPT system parameter optimization design has the drawbacks of lacking overall consideration and poor robustness, which limit its validity and practicality. Robust ICPT systems satisfying multiple objectives are expected. The performance of a sensitive ICPT system may be degraded by these uncertainties, even to unacceptable degree. Thus it is a vital problem to improve the robustness of ICPT design. On the other hand, the resonant topology decides the upper limit of an ICPT system. It is very helpful to improve system performance if designers know how to search for an optimal resonant topology from various structures.In order to efficiently optimize ICPT systems, this paper studied the resonant parameters design method for four basic structrues. Moreover, this paper proposes a robust multi-objective evolutionary optimization method as a design approach for ICPT systems for given resonant topology with the following two parts. First one is multi-objective optimization model with uncertainty. Second one is the multi-objective evolutionary optimization algorithm to solve the proposed model. As a comparison, nominal multi-objective optimization has also been considered.On the other hand, for given design specfications, based on the non-dominated optimal designs for every individual topology in a topology library(a set of various resonant topology), this paper proposes a hierarchical synthesis ICPT(robust) design approach in order to search for the optimal resonant topology and corresponding non-dominated optimal parameter designs.Based on the design approach, two ICPT systems have been designed for given specifications. Robustness analysis and numerical simulation have been carried out in MATLAB. The experimental prototypes have also been implemented. All the results of robustness analysis, numerical simulation, and experiments have verified the proposed method ?better robustness and validity than traditional nominal optimization method.