The paper deals with an evolutionary method for solving many-objective optimization problems exhibiting a high-dimensionality objective space, which is a challenging problem. An application in the optimal synthesis of Compensation Networks (CNs) of wireless power transfer systems for charging the batteries of electric vehicles is developed. This design problem is characterized by a set of multiple objectives in mutual conflict, which should be simultaneously considered. The optimization aims to the maximization of both the efficiency and the transferred power; a further criterion selects the networks with a suitable profile of impedance vs. frequency. Moreover, the minimization of current and voltage values relevant to inductors and capacitors in the networks, respectively, is pursued. These five design criteria are optimized exploiting the concept of the degree of conflict, which is the core of the proposed method, named 'EStra-many'. The method is applied by considering two approaches: the single-objective one, based on the degree of conflict function only, and the bi-objective approach in which the tradeoff between the degree of conflict function itself and another objective function (in turn, the efficiency, the transferred power, the distance of the resonance frequency from the supply frequency, the maximum value of the inductance current, the maximum value of the capacitor voltage, the distance from the Utopia point, and the number of inductors in the CN), is searched for. This way, all in one, seven different optimization problems are solved. The main element of novelty of the paper is a method to solve an optimization problem characterized by a high number of objective functions. In view of this, instead of considering a weighted sum of the objectives, a preference function inspired by the concept of least-conflict solution is formulated accordingly, the preference function is minimized by a cost-effective evolutionary algorithm of lowest order.
Synthesis of WPTS compensation networks considering multiple criteria
Bertoluzzo M.Membro del Collaboration Group
;Di Barba P.Membro del Collaboration Group
;Forzan M.Membro del Collaboration Group
;Sieni E.Membro del Collaboration Group
2022
Abstract
The paper deals with an evolutionary method for solving many-objective optimization problems exhibiting a high-dimensionality objective space, which is a challenging problem. An application in the optimal synthesis of Compensation Networks (CNs) of wireless power transfer systems for charging the batteries of electric vehicles is developed. This design problem is characterized by a set of multiple objectives in mutual conflict, which should be simultaneously considered. The optimization aims to the maximization of both the efficiency and the transferred power; a further criterion selects the networks with a suitable profile of impedance vs. frequency. Moreover, the minimization of current and voltage values relevant to inductors and capacitors in the networks, respectively, is pursued. These five design criteria are optimized exploiting the concept of the degree of conflict, which is the core of the proposed method, named 'EStra-many'. The method is applied by considering two approaches: the single-objective one, based on the degree of conflict function only, and the bi-objective approach in which the tradeoff between the degree of conflict function itself and another objective function (in turn, the efficiency, the transferred power, the distance of the resonance frequency from the supply frequency, the maximum value of the inductance current, the maximum value of the capacitor voltage, the distance from the Utopia point, and the number of inductors in the CN), is searched for. This way, all in one, seven different optimization problems are solved. The main element of novelty of the paper is a method to solve an optimization problem characterized by a high number of objective functions. In view of this, instead of considering a weighted sum of the objectives, a preference function inspired by the concept of least-conflict solution is formulated accordingly, the preference function is minimized by a cost-effective evolutionary algorithm of lowest order.| File | Dimensione | Formato | |
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