Power Factor Corrector Design applied to an 85-kHz Wireless Charger

Wireless charging technology extends the battery autonomy by allowing more flexible and practical ways of recharging it even when the electric vehicle is on move. The frequency conversion, which is required to generate a kHz-ranged magnetic field, also leads to considerable harmonics. As a result, the power factor and the corresponding efficiency decrement. This paper proposes a Power Factor Corrector which overcomes this drawback. The most relevant feature of the designed Power Factor Corrector is that it does not need any electrical signal from the secondary side to adjust its operation properly. The simulation results show the ability of the proposed scheme to increment the system efficiency for different State-Of-Charge in the Battery.

Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning

Evolutionary algorithms alone cannot solve optimization problems very efficiently since there are many random (not very rational) decisions in these algorithms. Combination of evolutionary algorithms and other techniques have been proven to be an efficient optimization methodology. In this talk, I will explain the basic ideas of our three algorithms along this line (1): Orthogonal genetic algorithm which treats crossover/mutation as an experimental design problem, (2) Multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses decomposition techniques from traditional mathematical programming in multiobjective optimization evolutionary algorithm, and (3) Regular model based multiobjective estimation of distribution algorithms (RM-MEDA) which uses the regular property and machine learning methods for improving multiobjective evolutionary algorithms.