The current research is concerned with the production of an LM25‐FE3O4—Gr metal matrix hybrid composites (MMHCs) and the analysis of its dry sliding wear conditions. The hybrid composites were made out of 3 wt% Fe3O4 and 4 wt% Gr particles with a mesh size of 200 meshes and were made using the stir casting method. Wear test on Taguchi’s L9 orthogonal arrays employs three process parameters: load, sliding velocity, and distance, each changed for three levels on a pin‐on‐disc tester position. The wear behavior of hybrid composite was investigated using loads of 20 N, 40 N, and 60 N; velocities of 2 m/s, 4 m/s, and 6 m/s; and distances of 1,000 m, 2,000 m, and 3,000 m. The major parameters were developed utilizing the signal‐to‐noise ratio by selecting “smaller‐is‐better” wear rates and COF features. FESEM was used to look at the worn surfaces of the composite specimen in order to determine the wear mechanism. Wear properties are enhanced in materials having aluminium hybrid metal matrix composites. According to the ANOVA table, the load parameter has the greatest impact on wear resistance and coefficient of friction, with maximum load values of 35.64 N and 5.782 N, respectively.

In the quest for sustainable energy sources, wind generators (WGs) have emerged as a promising solution to provide power for remote and grid-connected applications within IoT-enabled smart power systems. This research underscores the significance of wind energy as a valuable and sustainable energy source in the context of IoT-enabled smart power systems and delves into optimizing its utilization. Specifically, The authors introduce a novel approach to address the limitations of conventional hill climbing search (HCS) methods by incorporating a modified fuzzy logic-based HCS algorithm. The results unequivocally demonstrate that this proposed algorithm significantly enhances efficiency, consistently achieving an impressive efficiency rating of approximately 95% across diverse wind conditions. These findings mark a significant stride towards realizing the full potential of wind energy as a reliable and sustainable energy source within the context of IoT-connected smart power systems, paving the way for a greener and smarter energy future.

Cloud computing can be considered as a key tool in the energy management process that reflects effective monitoring, controlling as well as conserving energy. Energy management is a key concern in pepper implementation of smart grid technology. Due to the absence of cloud computing, energy cannot be supplied to a broad range of users as limited storage and memory are present in traditional technology. In the traditional energy management process, various kinds of stability issues can be seen in the absence of any cloud. Concerning all those reasons cloud computing is used in smart grid technology for providing a broad range of energy without interrupting the power supply. On the other hand, smart grid technology reflects a unique network that generates a two-way electricity flow by using various kinds of digital tools and techniques. This innovative technology can easily detect and at the same time react against any kind of issue generated in this technology due to the presence of cloud computing techniques. Compared to traditional technology, a huge level of renewable energy resources are used in smart grid technologies. A less amount of carbon emission reflects the generation of a better future environment. A survey analysis has occurred by taking 51 random people and three questions are asked related to effectiveness of cloud computing on smart grid technology and impact of this technology on the environment. A detailed discussion has also given after analysing survey.

This work presents a dual-output Boost–SEPIC converter developed for compact and efficient auxiliary power management in electric vehicles. The converter independently regulates two output voltages that may be higher, lower, or equal to the input, while its hybrid topology reduces component count and enhances power density. The study includes steady-state analysis, small-signal modelling, and derivation of key transfer functions to support controller design. A digital current-programmed predictive control strategy is implemented to achieve accurate current tracking, low ripple, and reliable response under dynamic load conditions. Simulation and experimental results validate the robustness of the proposed system, demonstrating stable dual-output regulation, improved efficiency, and reduced stress on the energy storage unit. These contributions collectively highlight the converter’s novelty as a compact and intelligent power solution for next-generation electric mobility platforms.

Power electronic conversion units function as essential components, which enable photovoltaic (PV) energy systems to transform solar module output into electricity needed by their connected loads. DC–DC converters serve as essential interfaces between solar PV sources and connected loads, where accurate control is necessary to achieve efficient and reliable power transfer. The systems require continuous input power and converter operation and fault prediction monitoring because their performance depends on those three components. The PV-supplied electric vehicle system requires immediate fault detection and mitigation for DC–DC converters because these functions determine system reliability. The system efficiency will experience high operational risk because a control failure will create critical system malfunctions. The researchers present a control system design that combines two existing control systems into one operational framework and developed a new control system that uses one shared state variable to perform two functions. The new approach requires low processing power to operate existing control systems, which allow users to execute two tasks while detecting system faults that occur after switching operational modes. The research study demonstrates the proposed framework effectiveness through simulation tests and hardware testing of a boost converter, which operates at 20 kHz using a 14 V input source. The converter produces a steady output voltage of about 28 V while the inductor current operates at 3.1 A and creates a current ripple of almost 1.8 A. The fault detection scheme based on slope identification identifies open circuit and short circuit faults (OCFs and SCFs) in one switching cycle (approximately 50 µ s). The converter maintains the output voltage variation with load variations between ±5%.