Sachin Kumar

Significant changes in society were emphasized as being required to achieve Sustainable Development Goals, a need which was further intensified with the emergence of the pandemic. The prospective society should be directed towards sustainable development, a process in which technology plays a crucial role. The proposed study discusses the technological potential for attaining the Sustainable Development Goals via disruptive technologies. This study further analyzes the outcome of disruptive technologies from the aspects of product development, health care transformation, a pandemic case study, nature-inclusive business models, smart cities and villages. These outcomes are mapped as a direct influence on Sustainable Development Goals 3, 8, 9 and 11. Various disruptive technologies and the ways in which the Sustainable Development Goals are influenced are elaborated. The investigation into the potential of disruptive technologies highlighted that Industry 5.0 and Society 5.0 are the most supportive development to underpin the efforts to achieve the Sustainable Development Goals. The study proposes the scenario where both Industry 5.0 and Society 5.0 are integrated to form smart cities and villages where the prospects of achieving Sustainable Development Goals are more favorable due to the integrated framework and Sustainable Development Goals’ interactions. Furthermore, the study proposes an integrated framework for including new age technologies to establish the concepts of Industry 5.0 and Society 5.0 integrated into smart cities and villages. The corresponding influence on the Sustainable Development Goals are also mapped. A SWOT analysis is performed to assess the proposed integrated approach to achieve Sustainable Development Goals. Ultimately, this study can assist the industrialist, policy makers and researchers in envisioning Sustainable Development Goals from technological perspectives.

Electric vehicles (EVs) have received widespread attention in the automotive industry as the most promising solution for lowering CO₂ emissions and mitigating worldwide environmental concerns. However, the effectiveness of EVs can be affected due to battery health degradation and performance deterioration with lifespan. Therefore, an advanced and smart battery management technology is essential for accurate state estimation, charge balancing, thermal management, and fault diagnosis in enhancing safety and reliability as well as optimizing an EV’s performance effectively. This paper presents an analytical and technical evaluation of the smart battery management system (BMS) in EVs. The analytical study is based on 110 highly influential articles using the Scopus database from the year 2010 to 2020. The analytical analysis evaluates vital indicators, including current research trends, keyword assessment, publishers, research categorization, country analysis, authorship, and collaboration. The technical assessment examines the key components and functions of BMS technology as well as state-of-the-art methods, algorithms, optimization, and control surgeries used in EVs. Furthermore, various key issues and challenges along with several essential guidelines and suggestions are delivered for future improvement. The analytical analysis can guide future researchers in enhancing the technologies of battery energy storage and management for EV applications toward achieving sustainable development goals.

One of the challenging issues in a hybrid power system (HPS) is to provide a stable power supply with minimum frequency deviation, which can be accomplished by ensuring a coordinated operation among intermittent type distributed generators (DGs). In this study, the coordinated operation of a tidal turbine generator (TTG) (an emerging and less explored DG), diesel engine generator (DEG) and plug-in hybrid electric vehicles (PHEVs) based an autonomous HPS (a less explored HPS of this kind) for load frequency control (LFC) is investigated. The coordinated operation of different control loops such as the blade pitch control loop of TTG, supplementary control loop of DEG, and power control loop of PHEVs are realized through the maiden application of the proposed quasi-oppositional harmony search algorithm (QOHSA) based model predictive control (MPC) strategy. To establish the superiority of the proposed QOHSA tuned MPC method in mitigating frequency deviation following disturbance, its performance is compared with that of the coordinated performance obtained using QOHSA tuned conventional controllers and other existing optimization algorithms. The results conclude that the suggested method can significantly reduce frequency fluctuation under different load disturbances. The proposed method can handle different variances of the disturbance signals or noise entering the system as well as the model mismatch.

The novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly as possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing preventive measures. In this study, three common machine learning (ML) approaches via linear regression (LR), sequential minimal optimization (SMO) regression, and M5P techniques have been discussed and implemented for forecasting novel coronavirus disease-2019 (COVID-19) pandemic scenarios. To demonstrate the forecast accuracy of the aforementioned ML approaches, a preliminary sample-study has been conducted on the first wave of the COVID-19 pandemic scenario for three different countries including the United States of America (USA), Italy, and Australia. Furthermore, the contributions of this study are extended by conducting an in-depth forecast study on COVID-19 pandemic scenarios for the first, second, and third waves in India. An accurate forecasting model has been proposed, which has been constructed on the basis of the results of the aforementioned forecasting models of COVID-19 pandemic scenarios. The findings of the research highlight that LR is a potential approach that outperforms all other forecasting models tested herein in the present COVID-19 pandemic scenario. Finally, the LR approach has been used to forecast the likely onset of the fourth wave of COVID-19 in India.

Enriching electric safety feature is the foremost concern for reducing fatality. Recent power system installations have been equipped with all safety features by employing modernized technological innovations. However, the age-old equipment installed long before lacks safety features. One of the critical safety feature issues that cause a threat to human life due to lack of safety features is the malfunction of the air break isolation switch which is caused by thermal effects. Similarly, fatality during the maintenance of distribution of the transformer is of significant concern. Globally, air break switches have been utilized to isolate power lines and transformers in the distribution premises. Facilitating the transformers with safety features is a highly complicated and expensive task. Consequently, severe fatalities have been recorded due to the lack of accident preventive techniques. In this perspective, the proposed work is to develop a Faulty Switching Detection and Alert System (FSDA) to alleviate the life-threatening issues. The FSDA mechanism is an integrated actuator setup configured beside the transformer switches and operating rods, and the malfunction of switches is detected and alerted. The Programmable Logic Controller (PLC) module is used to integrate the detection mechanism with alert and display system. The system generates an alarm to alert the workmen to avoid fatality.

This paper presents the design and development of an innovative device TRINETRA: A sensor based vision enhancement system for obstacle detection on railway tracks. The TRINETRA is termed as Technological Research and Innovation for National Empowerment of Trains to Reduce Accidents. The authors have developed a prototype of TRINETRA, which promises easy passage for trains in unknown environments such as zero visibility situations, fog, smog, and heavy rain. The prototype is based upon the integration of a camera, Radio Detection and Ranging (RADAR), and Infrared (IR) Light Amplification by Stimulated Emission of Radiation (LASER). The camera used in the prototype catches a long-range view of the track and presents it live on a mini screen fixed in the loco pilot cabin. A combined short, mid, and long-range radar sensor system is used to detect obstacles continuously in loco pilot blind spots on the track, particularly for collision avoidance assistance at high speed. The present work proposes a long-range laser IR illuminator with a wide range of color and mono cameras to aid clear and precise monitoring in zero visibility conditions, which is fixed on the front portion/engine of the locomotive/engine. The prototype experimental results for 2 m - 2 km distances have been performed on a live running train, which shows that the developed prototype tracks obstacles effectively during fog and smog conditions. The design concept, observation, prototype model, and other technical specifications have been presented, and satisfactory results were found.