# Multi-Objective Optimization of LCC-S-Compensated IPT System for Improving Misalignment Tolerance
## 1 Introduction
In order to extend position tolerance, an LCC-S compensation topology with stable output current was numerically solved in [22]. The series compensation in the secondary side was chosen to fully compensate for the inductance of the secondary coil, and the primary LC compensation parameters were the focus of parameter optimization. In these investigations, parameter selection and optimization were generally obtained through complex derivative calculations, and load variation was usually not considered simultaneously. Nowadays, artificial intelligence (AI) is rapidly expanding and has been applied to power electronic systems during the past several decades [23]. For practical IPT systems' feasibility, the misalignment tolerance, along with ideal stable output and low losses, were considered as objective functions in the multi-objective optimization problem. Once the optimization task for a particular application is determined, the optimal solution can be obtained by deterministic programming methods or metaheuristic methods [24]. Deterministic programming methods, which require calculation of the gradient and the Hessian matrix, are challenging for most optimization tasks in the field of power electronics due to their complexity. As a generic end-to-end tool, the metaheuristic method requires less expert experience and is efficient and scalable for a variety of optimization tasks. The metaheuristic methods include trajectory-based methods and population-based methods. Compared to the trajectory-based methods, the population-based methods (genetic algorithm (GA) [25; 26], particle swarm optimization (PSO) [27; 28], differential evolution [29], ant colony optimization (ACO) algorithm [30], immune algorithm (IA) [31], etc.) are superior in their convergence speed and global searching capability, and they are especially suitable for large-scale optimization issues. For IPT systems' anti-misalignment optimization, the application of population-based methods to the different types of magnetic couplers are the first to be widely studied by researchers. A nondominated genetic algorithm (NSGA-II) is employed as the optimization solver to find the optimal geometriesof bipolar power pads (BPP) coupler in [32]. In [27], a PSO-based optimization design of a W-Type contactless coupler is proposed to improve power transfer stability in the dynamic wireless power transmission system. Compared with the complicated trial-and-error method, the quantity of samples was reduced. As an evolutionary algorithm, PSO algorithm makes the particles in the population continuously move to the optimal position through position updates and avoids complex genetic manipulations. In [33], a holistic comparison of four coupler concepts is discussed using multi-objective Pareto analysis. For compensation parameter optimization, a synchronous optimization of compensation network based on non-dominated sorting genetic algorithm III (NSGA-III) is proposed in [34], which optimizes the demand of constant output voltage but lacks the description of the compromise solution for output fluctuation and system efficiency. A PSO-based parameter design method for a constant-voltage-output-type S-CLC-compensated IPT systems featuring high misalignment tolerance is presented in [35]. However, the primary side adopts series compensation, which will be affected by unbounded current when there is almost no coupling between the primary and secondary sides. Due to the high information processing and evolution capabilities, the IA algorithm is widely applied in pattern recognition and classification problems [31]. However, the robustness of the algorithm is heavily affected by the antibody levels. As for the ACO algorithm, the convergence speed and the quality of the solutions are very sensitive to the initial parameters [30]. Thanks to the advantages of easy implementation, high accuracy, and fast convergence speed, the PSO algorithm is widely applied and is selected to be executed to extract the optimal parameters in this paper.