A major challenge in ensuring the reliability of battery systems is the uncertainty surrounding their service life. An accurate prediction of the remaining useful life (RUL) is essential for effective maintenance and operation. Traditional extended Kalman filter (EKF) algorithms, which rely heavily on historical data, often have limited long-term prediction accuracy. To overcome this problem, this study proposes a multi-observation fusion approach to enhance the performance of the EKF for battery life prediction. To improve the practical applicability, we replaced the conventional aging capacity test values with operational capacity data obtained under real-world conditions. This modification refined the trajectory of the battery aging state space, thereby reducing the dependency on the quantity and quality of historical data while simultaneously boosting the long-term prediction accuracy and stability. Furthermore, a semi-empirical aging model was introduced to extract prior knowledge from offline data. This provided valuable insights into the life degradation trends and guided the filtering process. The resulting framework forms the basis for a novel RUL prediction method that utilizes a multi-observation fusion EKF. Validation experiments showed that the proposed method enhanced the prediction accuracy by over 60% compared with traditional EKF and particle filter algorithms throughout the lifecycle of lithium-ion batteries. Additionally, the technique exhibited robust stability (with no divergence observed over the full battery life) and demonstrated notable improvements in early stage RUL prediction.

Although numerous research advances have been made on asynchronous motors, particularly regarding internal parameters and complex control methods, the causes of the complex dynamic characteristics observed during normal motor operation have received less attention. To avoid abnormal vibration damage to the motor under normal operating conditions, this study investigated the fluctuating nonlinear dynamic characteristics of the motor by considering variable voltages, frequencies, and changing workloads from both analytical and numerical perspectives. Motor stability was evaluated using the Lienard-Chipart stability criterion and the Runge-Kutta method. The results indicate that speed fluctuations occur within a specific range of input voltage and angular frequency, and the amplitude fluctuation region expands when load variation is considered. Furthermore, the Poincare map and Lyapunov exponent were adopted to determine the oscillation distribution and dynamic characteristics in a typical region. Specifically, the chaos phenomenon mostly occurred at the edge of the unstable region with an increase in the load. In contrast, periodic conciliation was mainly concentrated within the unstable region.

In wireless charging devices, using single inverter to output multiple frequencies can effectively reduce device size and address the frequency incompatibility problem caused by different wireless charging standards. In addition, in the field of consumer electronics, the demand for simultaneous wireless power supply to multiple receivers is becoming increasingly urgent. Receivers that follow different standards require different transmission frequencies, and the generation of multi-frequency signals from single inverter is becoming an indispensable technology. Two methods for generating multi-frequency signals using single inverter, including multi-frequency programmed harmonic modulation (MF-PHM) and multi-frequency multi-amplitude (MFMA) superposition modulation, are investigated. The two methods are compared from the perspectives of system DC voltage utilization rate and inverter output harmonic components. The advantages and disadvantages of each method, together with the underlying mechanisms, are analyzed. Taking the simultaneous generation of 129.5 kHz, 203.5 kHz, and 351.5 kHz by single inverter as an example, a simulation and experimental platform is established to verify the analysis and conclusions. References and guidance for selecting multi-frequency-generation methods using single inverter are provided.

This paper aims to investigate the effects of circulating currents in AC copper loss and propose a suppression method. Firstly, the circuit model of the winding is established, and the magnetic leakage field in the stator slot is analyzed. Then, the equivalent model is built according to the magnetic leakage field in the slot, which is used to analyze the magnetic linkage of the strand as the position change of the conductor in the slot. Moreover, the circulating currents between the two different winding arrangements are compared. Furthermore, a winding arrangement is newly proposed to suppress the circulating current loss according to the investigation. Finally, experiments are performed to validate the effectiveness of the proposed new design.

New energy-vehicle charging and discharging units in vehicle to grid (V2G) application scenarios experience long and large transient fluctuations in power and frequency at their power injection points. Furthermore, the power matching degree is low, which has a serious impact on the V2G support performance. This paper proposes a two-step model prediction-virtual synchronous machine (MPC-VSG) control strategy that considers the SOC of the charging and discharging unit. First, the VSG equivalent model of the V2G grid-connected inverter is established, and the power weight coefficients of the charging and discharging unit SOC participating in frequency regulation are obtained by analyzing its characteristics. Second, the power change prediction value is obtained from the VSG model, and the two-step prediction of the reference frequency is performed to reduce the error of the reference value remaining unchanged at the instant of sudden power change. Minimizing the sum of squares of real-time output power deviations and frequency fluctuations as a cost function to compensate for the optimal active losses and suppress the frequency transient fluctuations. Finally, simulation analysis is carried out under the working conditions of island mode operation, V2G grid-connected operation, and wind power multi-energy complementary operation, and an experimental verification of the inverter part is carried out to verify the effectiveness and superiority of the proposed control strategy.

Accurately predicting photovoltaic (PV) power generation is essential for effective power system scheduling. However, increased incidences of sandy weather in recent years pose significant challenges for PV power plants in desert regions. This study addresses the inaccuracies in PV power forecasting under windy-sandy conditions by conducting wind tunnel experiments to evaluate the impact of varying wind speeds and PV panel tilt angles on power generation. Polynomial equations are developed through polynomial surface fitting to establish a relationship between the power loss rate, sand-blowing wind speed, and panel tilt angle. Latin hypercube sampling is used to extend the fitted surface scenarios, simulating real-world variations in PV power generation to enhance the accuracy and applicability of the model. In addition, the model incorporates three uncertainties related to wind direction, module aging, and airflow effects on PV arrays. A case study utilizing data from a PV power plant employs the Pseudo-similar day selection method, enabling a comparative analysis of the predicted outcomes with traditional methodologies. This comparison demonstrates the accuracy and effectiveness of the proposed method for improving PV power prediction under challenging conditions.

Sensitivity analysis quantifies the extent to which design parameters affect output parameters using computational models. It assists designers in systematically comprehending the extent of the influence of design parameters on the performance of permanent magnet motors, and assumes an increasingly significant position in the optimized design of electric machines. The computational model of the motor is first introduced, followed by the elucidation of the sampling techniques employed for sensitivity analysis. Subsequently, the applicability of various sensitivity analysis approaches in motor design is thoroughly examined. The benefits of various sensitivity analysis techniques in motor optimization design are examined. Finally, the issues and challenges encountered by these methods in motor design are addressed while anticipating future trends in sensitivity analysis within the motor sector.

Additive manufacturing (AM) has emerged as a transformative technology in electric machine (EM) design and production, addressing critical challenges in power density, thermal management, and material utilization. This review systematically examines recent advancements in 3D-printed conductive and magnetic materials, process innovations, and topology optimization strategies. Case studies in aerospace, electric vehicles, and medical robotics have demonstrated AM's ability to reduce EM weight, improve torque density, and enable complex geometries, such as conformal cooling channels and lattice-structured rotors. Challenges, including residual-stress control, material standardization, and scalability, are critically analyzed. A roadmap for AM adoption in next-generation EMs is concluded, hybrid manufacturing and sustainable material development are emphasized.

Home energy systems (HESs) are pivotal to Europe's distributed energy transition and to achieving the EU carbon neutrality goals. This study systematically reviews high-impact literature from the past five years, analyzing interdisciplinary progress and key challenges in European HES research. The findings reveal significant regional disparities: photovoltaic systems dominate in Southern Europe, thermal energy storage optimization is prioritized in the North, while Eastern Europe retains its reliance on traditional energy. Policy regulations, economic incentives, and sociocultural factors drive the deployment of HESs beyond the pilot stage. System optimization is increasingly utilizing hybrid energy storage and artificial intelligence-driven controls to enhance economic viability and self-sufficiency. Although HESs contribute to emission reductions and improved air quality, persistent energy equity gaps require targeted policy interventions. Current research limitations include scarce long-term empirical data, limited cross-scale modeling, and inadequate policy synergy assessments. Future studies should prioritize multienergy system integration, digital twin applications, differentiated incentive frameworks, and multidimensional fairness assessments. By synthesizing insights from energy engineering, economics, and environmental sciences, a theoretical framework for the synergistic evolution of the HES technology, policy, and society is proposed. This framework offers a knowledge map for scholars and evidence-based guidance for policymakers and supports the HES transition toward integrated sociotechnical energy systems, providing insights relevant to global energy democratization.