Lateral misalignment along the driving direction may achieve half the length of a transmission coil in a dynamic wireless charging system for electric vehicles, which is called limit misalignment. This limit misalignment may cause the dynamic wireless charging system to malfunction because of the large fluctuation in mutual inductance. In this paper, an asymmetric and reverse series coil (ARSC) structure is proposed. In the ARSC structure, the transmission coil is smaller than the receiving coil in size to smoothen the fluctuation in mutual inductance. The receiving coil consists of a large coil ( $\boldsymbol{R}_{\mathrm {x1}}$ ) and a small coil ( $\boldsymbol{R}_{\mathrm {x2}}$ ) and $\boldsymbol{R}_{\mathrm {x1}}$ is reversely connected to $\boldsymbol{R}_{\mathrm {x2}}$ in series. The purpose of this design is to further reduce the fluctuation in mutual inductance. A calculation method of mutual inductance is then proposed based on the vector potential to calculate the mutual inductance of the ARSC structure. The law of mutual inductance is analyzed by the proposed calculation method for the ARSC structure. Moreover, an optimization method of coil parameters is proposed to obtain quasi-constant mutual inductance. The ARSC structure and optimization method are not only simple but can also make the mutual inductance nearly constant. Calculation, simulation, and experimental results validating the proposed method and structure are shown.

Mutual inductance can be increased, and magnetic leakage can be reduced by adding finite magnetic core materials to the coil structure. However, the problem of the calculation of the mutual inductance between two arbitrarily positioned circular coils with a finite magnetic core has not been resolved. In this article, an analytical formula for calculating the mutual inductance between two circular coaxial coils on a finite magnetic core is obtained based on regional hierarchical expansion analysis (RHEA), the Maxwell equation, the Bessel function, and the boundary conditions for electromagnetic fields. The mutual inductance between two arbitrarily positioned coils is obtained by using the analytical geometry method. A series of experiments are carried out to verify the analytic calculations. The experimental results for each misalignment case are compared with the calculated results and simulated results. The theoretical results and simulated results are in good agreement with the experimental data, which verifies the validity of the proposed method.

Modern power systems produce nonstationary harmonics which bring harm to the electric equipment. Real-time and accurate parameter estimation is the key step of the harmonic elimination. This paper proposes a novel technique of the parameter estimation for fundamental and harmonic using nonergodic S-transform (NEST), which can effectively eliminates the estimated parameter error caused by nonsynchronous sampling. First, the signal is decomposed into the fundamental and harmonics NEST vectors (NESTVs) by NEST, and the amplitudes are detected accurately by extracting the magnitude of NESTVs, which are optimized by the Gaussian window width coefficient of the NEST kernel function. Second, the overlapping and abandoning method is used to remove the end effect of NESTVs so as to obtain the effective and complete data for the parameter estimation. Then, the estimated instantaneous frequency and phase are obtained using the linear phase characteristic of NESTVs. Finally, simulations and experiments prove that the proposed method can not only estimate the stationary signal parameters accurately but also trace the nonstationary signal parameters in the real time under the spectrum leakage condition.

Magnetic resonance wireless power transfer (WPT) is an emerging technology that may create new applications for wireless power charging. However, the mutual inductance fluctuations resulting from lateral misalignments are the major factors hindering the promotion of this technology. In this paper, a structure of a transmission coil and four cascaded receiving coils for wireless charging systems is proposed to suppress fluctuations in mutual inductance with lateral misalignments. The mathematical model of the proposed structure with lateral misalignments is built based on the equivalent circuit method. The expressions of the output voltage and the efficiency are then derived by solving the system equivalent equations. In addition, a method of compensation capacitance is proposed to suppress the effect of the cross-coupling mutual inductance on the voltage and efficiency. The primary advantage of the proposed structure is that the sum of the mutual inductances between the receiving coils and the transmission coil is nearly constant with lateral misalignments. Therefore, the output voltage and efficiency can also be kept nearly constant by using the proposed structure with lateral misalignments. Simulation and experimental results validating the proposed method are shown.

An adaptive fault diagnosis method for rotating machinery based on maximum kurtosis incomplete S-transform is proposed in this paper. Firstly, the incomplete S-transform is performed on the fault frequency band of the vibration signal, and the module vector group is obtained through module calculation. Subsequently, the kurtosis of all the modulus vectors are calculated and the vector corresponding to the maximum kurtosis is located to adaptively determine the envelope of the fault frequency component in the vibration signal. Then, fast Fourier transform is performed on the envelope to obtain its main frequency, which is matched with the fault mode frequency to achieve fault diagnosis of rotating machinery. Finally, the mean peak ratio (MPR) was used to evaluate the performance of different methods under various operating conditions. The results show that the maximum MPR is obtained by the proposed method, demonstrating its stronger noise resistance and demodulation ability.

The accuracy of battery state of charge (SOC) is crucial for solving the problems such as overcharge, overdischarge, and mileage anxiety of electric vehicle power battery. In this study, an SOC estimation method using a hybrid method (HM) based on threshold switching is proposed, which combines the advantages of the extended Kalman filter (EKF) and the ampere hour integration (AHI) to improve the estimation accuracy and convergence speed. First, the parameters of the second-order RC equivalent model are identified using the least square. Then, the equation of EKF for updating the state variable is reconstructed by using the identified parameters to solve the problem of multiple iterations caused by the uncertainty of the initial value. Finally, the difference between the estimated voltage and the sampling voltage is used as the threshold value for switching between the AHI and the EKF to estimate the SOC of the battery. Simulation results show that the estimated SOC error of the proposed algorithm is less than 1.6% and the convergence time is within 70 s. Experiments under different SOC initial values are carried out to prove the advantages of the proposed method.