As electric vehicles and renewable energy systems become more widespread, there is an increasing need for DC–DC converters that can provide higher voltage gain, better efficiency, and improved reliability. This paper presents a comprehensive review of the latest advancements in enhanced gain DC–DC converter technologies. It covers a range of topologies including interleaved, coupled-inductor, switched-capacitor, multiport, and resonant converters, each suited for different application requirements. The review also explores the role of emerging semiconductor materials such as silicon carbide (SiC) and gallium nitride (GaN), as well as the integration of modern control strategies like model predictive control (MPC), fuzzy logic, and sliding mode control. Artificial intelligence (AI) and digital twin technologies are also discussed as tools for improving real-time performance and predictive maintenance. Through comparative studies and application-specific recommendations, this paper identifies key research gaps and future directions that could enhance scalability, cost-effectiveness, and thermal performance in power conversion systems for EVs and renewable energy.

Combined heat and power (CHP) systems are widely used in industries for their high energy efficiency and reduced carbon emissions. The optimal dispatch of CHP systems involves scheduling the operation of various equipment to minimize the total operational cost while meeting the heat and power demand of the facility. In this research work, a novel quantum‐inspired optimization algorithm is proposed for the first time to solve the optimal dispatch problem of CHP systems. The proposed algorithm combines the principles of quantum mechanics with classical optimization algorithms to achieve a better solution. The algorithm uses quantum gates to perform quantum operations on the optimization variables, which allows for the exploration of a larger search space and potentially better solutions than classical algorithms. The proposed algorithm also incorporates a classical optimizer to refine the numerical evaluations acquired from the quantum operations. The performance of the adopted optimization technique was demonstrated by associating it with various other optimization techniques based on factors such as the speed of convergence, computational time, and the quality of the solution. The comparison is made on two standard CHP systems subjected to various quality and inequality constraints. The simulation results indicate that the quantum‐inspired optimization technique surpassed the other algorithms in both solution quality and computational efficiency. The implemented algorithm provides a promising solution to the optimal dispatch problem of CHP systems. Future research can further explore the application of quantum‐inspired optimization algorithms in other energy systems and optimize the algorithm’s parameters to improve its performance.

This work proposes a module unit (MU) based single-source thirteen-level inverter (SSTLI) for solar PV applications. The proposed topology is 13-level design with single MU and 1.5 voltage gain. This work describes its operational modes at various output levels. Due to their symmetric functioning within a cycle, two capacitors were series-connected. with MU may achieve self-voltage balancing, which reduces complexity of control compared to typical Multilevel inverters (MLIs). The single-source generalized structure with additional MUs can be utilized in order to boost voltage levels for the output. With more MU, voltage gain increases, in addition the output levels are greatly enhanced. The merits of the proposed SSTLI are fewer components, self-balance, voltage stress. A comparison between proposed topology and other MLIs is shown in this work. To determine viability and dynamic performance of proposed SSTLI, MATLAB simulation results are presented for dynamic values of Modulation Index (M) for different loads along with the harmonic analysis. Experimental results also have been presented for the practicality of SSTLI topology.

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client–server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video.