Praveenkumar Thangavelu

Lithium‐NMC batteries in electric vehicles exhibit complex degradation mechanisms, where capacity fade, internal resistance growth, and discharge behavior evolve nonlinearly under varying operating conditions. Accurate remaining useful life prediction necessitates capturing these intricate interdependencies, which traditional models fail to generalize effectively. This study develops a robust machine‐learning framework leveraging experimental cycling data under nominal and over‐discharge conditions. Key parameters like voltage, discharge time, internal resistance, and state of health were chosen due to their direct correlation with electrochemical aging, resistive losses, and failure progression, ensuring high sensitivity to degradation dynamics. Support Vector Regression and Bayesian‐optimized Lasso Regression were employed to model these dependencies, providing precise predictions of key battery health indicators. A hybrid framework integrating these models for remaining useful life estimation achieved , MAE, RMSE of 0.9998, 0.093 and 0.138 respectively, significantly outperforming conventional approaches. Rigorous evaluation through K‐fold cross‐validation and subset stability analysis ensured generalizability across diverse operating conditions. Benchmark comparisons with state‐of‐the‐art methods demonstrated superior predictive accuracy. By addressing critical limitations in traditional degradation modeling, this work provides a scalable, data‐driven solution for real‐time battery health management, enhancing the reliability and sustainability of electric vehicle applications.

This study investigates the performance and emission characteristics of a diesel engine fueled with Azolla microphylla biodiesel blends and the effects of aluminium oxide (Al₂O₃) and copper oxide (CuO) nanoparticles as fuel additives. Azolla microphylla, an aquatic fern, was used to produce biodiesel through a transesterification process. Diesel fuel was blended with Azolla biodiesel in ratios of 10:90 (B10), 20:80 (B20), and 30:70 (B30). Engine performance parameters, including brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and smoke, were evaluated. The results showed that the B30 blend exhibited the highest BTE under maximum load conditions. To address the high viscosity and poor combustion performance of higher biodiesel blends, Al₂O₃ and CuO nanoparticles were added to the B30 blend at concentrations of 50 ppm and 100 ppm. The B30 blend with 100 ppm Al₂O₃ nanoparticles demonstrated the highest BTE and the lowest emissions of HC, CO, CO₂, and smoke compared to the other test blends. The improved combustion and reduced emissions were attributed to the enhanced heat transfer and catalytic properties of the Al₂O₃ nanoparticles. These findings suggest that Azolla biodiesel, especially when combined with Al₂O₃ nanoparticles, can be a viable and sustainable alternative to conventional diesel fuel.

This review offers a comprehensive overview of synthetic fuels as promising alternatives to conventional fossil fuels. The carbon-neutral potential of synthetic fuels, when produced using renewable energy and captured CO₂, offering significant opportunities to mitigate CO₂ emissions is discussed. Moreover, the efficiency of synthetic fuels is presented as they do not require dedicated agricultural land or substantial water resources, addressing concerns related to land use change and water scarcity associated with traditional biofuels. The economic viability of synthetic fuels is explored highlighting the advancements in technology and decreased renewable energy costs, coupled with their independence from food crops, mitigating concerns about potential impacts on food prices. Major investments by industry leaders like Porsche, HIF Global, and ExxonMobil, totalling $1 B, aimed at achieving an annual production of 550 M liters by 2026 are detailly covered. This study further extends emphasizing the scalability of synthetic fuel production through modular processes, enabling tailored facilities to meet regional demands and contribute to a decentralized and resilient energy infrastructure. Additionally, the "drop-in" nature of synthetic fuels, seamlessly compatible with existing fuel storage, pipelines, and pumps, is highlighted, facilitating a smooth transition without requiring extensive infrastructure changes. Challenges such as the current high cost of synthetic fuel production are acknowledged, necessitating supportive government policies and incentives for widespread adoption. Overall, synthetic fuels emerge as promising contenders in the pursuit of sustainable and adaptable energy solutions, with tangible benefits for the environment, economy, and existing energy infrastructure.