Fast Design Optimization Method Utilizing a Combination of Artificial Neural Networks and Genetic Algorithms for Dynamic Inductive Power Transfer Systems

Abstract

Multiple parameters with large nonlinear characteristics must be considered simultaneously to design the coil dimensions of static inductive power transfer (SIPT) systems. The design of dynamic inductive power transfer (DIPT) systems is more challenging due to the large number of parameters needed to be considered. In the conventional artificial neural network (ANN)-based design approach, optimal coil dimensions are found using ANN that has learned the nonlinear characteristics between coil dimensions and magnetic characteristics using the finite element method (FEM). However, this approach requires a large amount of training data, and it is difficult to reach an optimum design if there are many design criteria. In order to overcome these challenges, this paper proposes a design optimization method using two approaches: improving the time efficiency of ANN training data collection by superposing the magnetic fields from the coils and improving the input value of ANN using a genetic algorithm. Design results predicted by the ANN are compared with FEM simulation, circuit simulations, and experimental results to verify the validity of the proposed algorithm. The FEM and circuit simulation results and the ANN prediction results match with errors of 10.2% or less for all design requirements. Experimental results are provided for a 3 kW DIPT system with four transmitter coils and an automated test rail. Comparison results between ANN predicted values and experimental values match with an error of less than 12.7%.

Mutual Inductance Estimation Using an ANN for Inductive Power Transfer in EV Charging Applications

“…In [13], the authors focused their attention on building a deep neural network (DNN) to design a wireless power transfer (WPT) system for an EV to posteriorly compare its performance with the calculation results of a classic Monte Carlo algorithm. Using a DNN divided into three parts that uses spatial variables as features, as well as the mutual inductance measured between the transmitter and receiver sides, the equivalent resistance of the primary and secondary loops and the load resistance, it was possible to reduce the estimation time from 1369 s, in the case of the Monte Carlo algorithm, to only 152 s. Alternatively, in [20], with the information from the structural parameters of an IPT system, an ANN was employed with the goal of not just reducing the training time and training data but also comparing the estimation performance with that of the finite element method (FEM) for different variables, like power transfer, self-inductance and mutual inductance, among others. Furthermore, in [24], an ANN was designed to characterize the quality factor of spiral coils used in IPT systems.…”

50 kW Reflexive Tuning Networks With Low Uncoupled Transmitter Currents for Dynamic Inductive Power Transfer Systems

“…For the conventional reflexive tuning circuit, design parameters c 1 and c 3 are assumed to be 1, as it lacks C 1,add and C r,add . The double-sided LCC circuit was designed based on the optimization algorithm presented in [38].…”