This article proposes a single-transmitter-controlled multiple-channel constant current outputs scheme, which can energize multiple hovering drones over the air simultaneously. As an unmanned and cost-effective solution to extend the cruising range of drones, the multiple-drone in-flight wireless charging has to deal with two key technical challenges, namely the continuous disturbance of multiple mutual inductances ( M ) caused by the hovering attitude of drones, as well as the limit of drone-side payload. Accordingly, this article adopts the multifrequency resonating compensation to establish multiple transmission channels and achieves the single-transmitter-controlled multiple-channel constant current outputs based on a communication-free multiple- M estimation method. The simulated and experimental results are both given in this article, which shows that the accuracy of the multiple- M estimation is up to 97% and the steady-state accuracy of the current output control is up to 95%. What is more, the current output control can effectively restrain the output current fluctuation of the battery load under continuous multiple- M disturbance, indicating that the proposed scheme is feasible to improve the stability and reliability of the multiple-drone in-flight wireless charging system.

Multiple-drone in-flight wireless charging offers a cost-effective solution for energy replenishment in long-range drone operations. Since multiple transmission channels impact each other, the conventional mitigation of single-channel mutual inductance disturbance cannot be directly utilized for in-flight wireless charging of drones, which has been nearly unexplored in previous studies. To address this challenge, this article presents a multiple-coupling-observed multi-CC output control scheme. The proposed scheme leverages a multiresonant LCC/P topology to extract phase information from parallel branch currents on the transmitter side, enabling real-time monitoring of multiple mutual inductances without requiring load information. Using the monitored mutual inductance, the scheme regulates constant current (CC) outputs for multiple receivers from a single transmitter, eliminating the need for control, detection, or communication on the receiver side, which contributes to drone lightweighting. Experimental results demonstrate that the proposed method achieves mutual inductance monitoring errors below 2.5%, maintains steady-state current variations under 5.5% after mutual inductance changes, and effectively suppresses output current fluctuations during continuous mutual inductance disturbances. These findings confirm that the proposed scheme effectively ensures stable CC charging in multidrone in-flight wireless power transfer (WPT) systems.

Model-free predictive current control (MFPCC) has the advantage of strong motor parameter robustness because of the unused motor parameter. However, the torque ripple of this method is extremely high since only seven basic voltage vectors are utilized. To bypass this shortcoming, an MFPCC with an extended control set for permanent magnet synchronous motors (PMSMs) is proposed in this article. First, an extended control set is constructed by the discrete space vector modulation (DSVM) technique. Then, a novel current gradient establishment approach is developed to void the current gradient update stagnation problem along with a high computational burden. After that, a vector preselection strategy is carried out to reduce the candidate voltage vector number, thus dropping computation cost. Finally, compared experiments of the conventional MFPCC and the proposed method are implemented in a 2 kW PMSM drive experimental platform to validate the effectiveness and feasibility of the proposed method.

In this paper, a novel and simple statistical prediction model for sea surface multivariate is developed based on extended empirical orthogonal functions (referred to as the MEEOF model). This simple model embeds the temporal evolution information into the empirical orthogonal function spatial patterns, which effectively captures the spatial distribution of the sea surface variables and their evolution process over time, and can be used to improve the accuracy of intra-seasonal ocean forecasts. At the same time, it considers both the correlation between different spatial and temporal points and the dynamic balance between different sea surface variables. The performance of the MEEOF prediction model has been examined in the South China Sea (SCS) based on remote sensing satellite datasets. Experimental results demonstrate that this model has significant forecasting capability within the forecast window of 15–90 days, compared with the traditional persistence forecasts and optimal climatic normal (OCN) results. Furthermore, this model exhibits good forecast performance throughout the entire forecast window; the final prediction model (referred to as the fusion model) is established by obtaining the weighted average for the MEEOF forecasts and persistence forecast results. Numerical experimental results show that this fusion prediction model consistently outperforms the persistence model, the OCN model, and the linear regression model over the entire forecast window. A case study shows that the propagation of ocean waves and the coordination between different sea surface variables can be well predicted by this simple model, indicating that this fusion model has a potential advantage in intra-seasonal predictions of the ocean.

In this paper, a computation-efficient current harmonic mitigation scheme is proposed and implemented for dual three-phase permanent magnet synchronous motors (DTP-PMSMs), namely deadbeat-initialized (DB-initialized) continuous control set model predictive control (CCS-MPC), with the objective of reducing current harmonics and alleviating computational burden. Specifically, this paper proposed a direct CCS-MPC scheme with implicit modulator, which combines control and modulation in one computation stage, thereby avoiding additional modulation delays subsequently reducing the current harmonic. Furthermore, the model predictive control is formulated as the numerical solution of two convex quadratic programming (QP) problems. More importantly, a DB-initialized method is employed, utilizing the deadbeat computation results as the initial values for CCS-MPC, aiming to alleviate the computational burden. Lastly, the effectiveness of the proposed DB-initialized CCS-MPC scheme is verified experimentally in this paper.

Multifrequency one-to-many wireless power transfer (O2M-WPT) is advantageous for charging various medium- and small-power loads, offering cost-effectiveness, simplicity, and minimal cross-coupling effects. However, inevitable hard switching caused by the multifrequency modulation method may lead to significant switching losses. To reduce the switching losses, a novel selected-interharmonic-injected pulse density modulation (SI ² PDM) method for simultaneous O2M-WPT systems is proposed in this article. SI ² PDM actively injects the selected interharmonics into the primary coil for multifrequency modulation. Besides, SI ² PDM artfully regulates the interharmonic by choosing the optimal sequence from the sequences with the same pulse density to achieve independent power allocation. Most importantly, the switching frequency is reduced below the maximum operating frequency, which is beneficial for reducing switching losses and improving efficiency. Comparative experimental results between SI ² PDM and an existing method reveal a remarkable 18.58% to 82.83% reduction in inverter power losses and a substantial 1.23% to 20.27% improvement in dc-to-load efficiency. These results affirm the feasibility and effectiveness of SI ² PDM in achieving independent power allocation and enhancing efficiency in simultaneous O2M-WPT systems.