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Inductive power transfer is a method that can be used to transfer power to electric vehicles. The output power and the transmission efficiency drop significantly when there is a misalignment between receiver on the vehicle and transmitter on the parking spot. This can be resolved by designing the transmitter coil that produces uniform magnetic fields for wireless charging. Here, a planar rectangular coil with unevenly spaced turns is proposed for improving the uniform magnetic field distribution over the charging zone, together with the genetic algorithm for determining the geometric parameters of the coil structure. An analysis that is divided into four design variables is presented. The magnetic characteristic of the optimal unevenly spaced transmitter coil using genetic algorithm and optimal evenly spaced transmitter coil using conventional method are compared and analysed. The uniform magnetic field distribution of the designed prototype is measured by an electric and magnetic field probe and by a receiver coil. The measured results agree well with the theoretical values and validate the improved uniform magnetic field distribution.

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In this study, a new method to estimate the load and mutual inductance for a wireless charging system (WCS) is presented. Instead of simultaneously using the root mean square and phase angles of related voltages and currents, accurate estimations can be obtained through the phase differences in the primary side. Firstly, the WCS model with dual-side inductorcapacitor-capacitor compensation networks is established, and the reflected impedance is analysed based on the phase differences in the primary side. Then, the estimation calculation of load and mutual inductance is derived according to the reflected impedance. Meanwhile, the output voltage estimation in the primary side is realised based on the estimated values of the load and mutual inductance. Finally, experiments are conducted to verify the effectiveness of the proposed method in the 3.3 kW electric vehicle wireless charging experiment platform. Experimental results show that the estimation errors of the load and mutual inductance are <5 and 3.5%, respectively, and the system output voltage can be effectively estimated based on their estimated results.

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