Driving conditions and styles significantly affect the energy economy and battery lifespan of hybrid energy storage system (HESS) for electric vehicles (EVs). An adaptive wavelet transform (WT)-based energy management strategy (EMS) that considers driving style is proposed to extend battery lifespan and reduce energy loss. First, the dynamic models of the battery, ultracapacitor, and DC/DC converter are established in detail. Second, driving style recognition (DSR) is implemented by analyzing the jerk based on driving cycle recognition (DCR), which is divided into conservative, normal and aggressive. Next, a WT decomposition level adaptive method is designed to separate the transient power online, which can improve the adaptability of the EMS to driving conditions and driving styles. Finally, fuzzy logic control (FLC) is employed to achieve optimal power allocation. Simulation results indicate that compared with the traditional WT-based EMS, the adaptive-level WT-FLC-based EMS has significant advantages in extending the battery lifespan and saving energy. It can effectively suppress the maximum battery current by 58.09%, extend the driving range by 1.49 km at a constant speed of 40 km/h and reduce power fluctuations by 38.04%.
Wireless power transmission technology can be used to solve the complicated wired charging and fixed station charging problems of unmanned aerial vehicles (UAVs). In this paper, a combined 5D transmitting coil structure consisting of four-cell D-shaped coils and one compensatory D-shaped coil is proposed. By combining different D coils according to the switching strategy, the rated power transmission can be achieved in the entire transmitting region; thus, the number and position of loads become flexible, which increases the redundancy of the system. Owing to the multiple loads with multiple-frequency characteristics, cross-coupling should be considered when accessing a system. Owing to the structure of the loose coupler, the power fluctuation between multiple loads is low in the charging region, which means that cross-coupling is negligible. An experimental prototype is constructed to verify its structural characteristics. The experimental results showed that the combined 5D transmitting coil structure can supply energy to a single load in the entire transmitting region by combining different coils with high efficiency or simultaneously supplying energy to multiple independent loads with different frequencies at different positions. This verifies the anti-offset ability and redundancy characteristics of the system.
Large language models (LLMs) are promising tools for knowledge representation, reasoning, and decision-making in industrial sectors. The employment of LLM in power systems is receiving increasing attention, but is still limited by the scarcity of domain-specific data, the absence of unified benchmarks, the complexity of integrating heterogeneous data, and strict physical time-varying constraints. This paper presents a focused survey on the applications and fine-tuning techniques of LLMs within power systems. The taxonomy of LLMs is first introduced, covering general foundation LLMs, industry foundation LLMs, domain-specific LLMs, and vertical scenario LLMs. The discussion then addresses the principles, categories of fine-tuning methods, evaluation metrics, and benchmark datasets that support model adaptation. Finally, the current progress, potential applications, and challenges of specialized LLMs in the power sector are examined, with emphasis on hybrid fine-tuning strategies, multimodal data integration, and deployment frameworks designed to ensure secure and reliable power system operation.
Eddy-current sensor-free control (ESFC) of a bearingless motor system can reduce the volume, cost, weight, and impact of shear on transmitted liquids. Moreover, implementing an ESFC algorithm in practice is challenging due to its small displacement value and strict signal-to-noise ratio requirements. An extensive overview of ESFC for bearingless machines is provided. Initially, ESFC methods are divided into three categories, the parameter estimation method (ESFC I), observer method (ESFC II), and machine learning and intelligent algorithm (ESFC III). Different ESFC algorithms are evaluated and summarized in terms of cost, complexity, scope of application, and feasibility. This study focuses on a comparative analysis of the signal-to-noise ratio (SNR) in ESFC and speed/position sensorless control. Furthermore, an ESFC method with a high signal-to-noise ratio based on the intrinsic Hall effect is presented. Finally, the theoretical considerations and control algorithms for some existing ESFC solutions are experimentally verified, and the experimental performance of the discussed topologies is compared.
Weak-grid operation strengthens the interaction between the active-power/frequency and reactive-power/voltage channels of virtual synchronous generators (VSGs), leading to coupled $P/Q$ oscillation, operating-point deviation, and reduced stability margins. A port-Hamiltonian-system (PHS) energy model is developed for the dominant VSG states and defines an energy coupling coefficient (ECC) to quantify the proportion of cross-state coupling energy. The ECC is then used as the only online coupling indicator for an ECC-guided $R_{\mathrm{v}}-L_{\mathrm{v}}$ coordinated adaptive virtual-impedance control strategy. The virtual inertia $J$ and damping coefficient $D$ are kept at their nominal values, while the virtual resistance $R_{\mathrm{v}}$ and virtual inductance $L_{\mathrm{Y}}$ are adjusted under magnitude, rate, and smoothing constraints. A dissipativity-oriented stability analysis is further provided, showing that the reduced-order closed-loop system remains locally dissipative and practically bounded under bounded disturbances and admissible virtual-impedance ranges. Simulation and HIL results under source-load disturbance, grid-frequency/voltage disturbance, SCR/X/R scans, and real-time implementation conditions verify that the proposed method reduces power coupling and ECC without introducing time-varying inertia or damping into the VSG frequency loop.
