Nowadays, smart grid online monitoring systems are widely used in high voltage (HV) power transmission networks, leading to the requirement of reliable power supply units for the monitoring equipment. In this paper, we present a novel solution to harvest energy from the magnetic field around a 110 kV transmission line and transmit the energy to the online monitoring equipment on the transmission tower over 1.1 meter insulation distance. Printed circuit board (PCB) resonators are covered by the composite insulation material forming the first HV insulator with wireless power transfer (WPT) capability. The prototype of the HV insulator has passed the rigorous HV tests at 550 kV. The WPT characteristic of the HV insulator including the effects of corona rings is analyzed and verified by practical measurement.

In this paper, a Wireless Power Transfer (WPT) system parameter identification method that combines an artificial neural network and system modeling is presented. During wireless charging, there are two critical parameters; specifically, mutual inductance and load resistance, which change due to the movement of the transmitter/receiver and battery conditions. The identification of these two uncertain parameters is an essential prerequisite for the implementation of feedback control. The proposed method utilizes an Artificial Neural Network (ANN) to acquire a mutual inductance value. A succinct system model is formulated to calculate the load resistance of the remote receiver. The maximum error of the mutual inductance estimation is 2.93%, and the maximum error of the load resistance estimation is 7.4%. Compared to traditional methods, the proposed method provides an alternative way to obtain mutual inductance and load resistance using only primary-side information. Experimental results were provided to validate the effectiveness of the proposed method.

Wireless power transfer (WPT) has become a crucial feature in numerous electronic devices, electric appliances, and electric vehicles. However, traditional design methods for WPT suffer from numerous drawbacks, such as time-consuming computations and high error counts due to inaccurate model parameters. As artificial intelligence (AI) continues to gain traction across industries, its ability to provide quick decisions and solutions makes it highly attractive for system optimizations. In this paper, a method for optimizing WPT parameters based on machine learning is proposed. The convolutional neural network is adapted for training and predicting the performance of a pair of coupled coils under a set of input parameters. The performance parameters include the spatial magnetic field distribution map, quality factor, inductance value, and mutual inductance value, which are critical in determining the efficiency and selecting optimal coil parameters such as the number of turns and wire diameter. Moreover, the spatial magnetic field distribution map is also helpful for identifying design compliance with the electromagnetic field safety standards. The training results reveal that the proposed method takes an average of 3.2 ms with a normalized image prediction error of 0.0034 to calculate the results to calculate one set of parameters, compared to an average of 23.74 s via COMSOL. This represents significant computational time savings while still maintaining acceptable computational accuracy.

In common industrial practices, the Japan Electronics and Information Technology Industries Association (JEITA) guideline is widely adopted for battery temperature regulations. However, the conventional JEITA-compliant temperature-regulated current control (TRCC) is generally designed without considering the charging speed. To address this issue, a multi-hysteresis temperature-regulated current control (MTRCC) is proposed in this paper. The proposed MTRCC is designed based on the conventional JEITA-compliant TRCC by dividing the hysteresis band for each temperature reference from one to multiple. As a result, the number of charging current levels is increased from three to multiple, such that the charging speed can be improved according to the analysis. The proposed control inherits the simple and effective temperature regulations of the conventional control, while enhancing the charging speed without additional hardware costs. This approach has potential to be an alternative of the state-of-the-art TRCC in JEITA guidelines for single-cell charging applications. Empirical results validate that the proposed MTRCC (with four hysteresis bands) can achieve faster charging than the conventional control at four different ambient temperatures and the charging speed improvement is more significant when the ambient temperature is higher.

Inductive power transfer (IPT) technology becomes highly attractive option for wireless battery charging applications. To fulfill the charge profile of battery packs, wide output voltage regulation is always necessary. In this paper, an improved LCC-S compensated IPT system utilizing variable frequency switched capacitor control is put forward. Compared with the traditional LCC-S compensation topology, an extra capacitor is connected in series with the compensation inductor to form a symmetrical T-network. Switched capacitor is utilized to regulate the equivalent capacitance of the middle branch of T-network, which has the advantages of low voltage stress. In the proposed control method, the variation range of switching frequency is in compliance with SAE J2954 Standard and frequency bifurcation can be avoided. The detuning issue at receiver side caused by switching frequency variation is solved by using the variable inductor. The proposed IPT system is able to realize nearly unity power factor with high efficiency in the whole voltage range. Finally, a 592 W prototype with 112-168V output voltage is built to verify the proposed IPT system. Experimental results show that the peak efficiency of the improved LCC-S compensated IPT system reaches 94.6% at full load when the coupling coefficient k =0.2.

In this article, a power-split-based wireless charging system (WCS) with communication-free coordination control is proposed to combat wide load and coupling variations. By using a magnetic integrated inductor, two power delivery channels are created from a single receiver coil. Large portion of received energy is directly delivered to the load. Small amount of energy is converted by using a low-voltage low-power dc–dc converter to regulate the output voltage or current achieving constant voltage (CV) or constant current (CC) charging operation. This proposal achieves higher power conversion efficiency than the traditional two-stage cascaded converter WCS. Since the dc–dc converter only processes partial power of the WCS system, this proposal reduces the voltage stress of the devices and design difficulty. A communication-free coordination control is applied, which is established by sensing the input current drop caused by the burst operation of the partial-power dc–dc converter. Thus, the CC-CV charging is realized by PWM control at the receiver (Rx) side, and the inverter is regulated with subharmonics control scheme to maintain nearly constant Rx coil current. Simulation results for both high-voltage high-power scenarios and low-voltage low-power (LVLP) scenarios are provided to investigate the operation of the proposed WCS. Finally, experimental results are captured from a 250 W/60–84 V LVLP prototype to validate the feasibility and practicability of proposed concept.