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Geetha Ramasamy

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Review Open access 2020

Next-Generation Battery Technologies for Electric Vehicles

The worldwide move towards sustainable transport has seen electric vehicles (EVs) become the keystone to the mobility systems in the future. Battery technology is central in the performance, safety, affordability, and adoption of EVs. The domineering conventional lithium-ion batteries (LIBs), though, are becoming weaker due to their constraints around energy density, charging rates, thermal safety, lifecycle degradation, availability of raw materials, and environmental effects. Such issues have only served to drive more research into research on next-generation battery technologies that could support the power needs of the applications of EVs in the modern world. This paper comprehensively and systematically reviews the next-generation battery technologies in electric vehicles with solid-state batteries, lithium-sulfur battery, lithium-air battery, sodium-ion battery, and emerging multivalent and hybrid energy storage systems. The article starts with the description of the changes in EV battery performance as concept of energy density and power density, safety, cost, sustainability, and recyclability. A critical review of literature looks into the current developments, experimental innovations, and industrial developments on several battery chemistries. The suggested section of the methodology proposes a comparative techno-economic/performance analysis framework, which incorporates the electrochemical modelling, lifecycle analysis and systems-level optimization. The feasibility of every battery technology in the deployment of EVs is measured by using quantitative performance metrics, degradation models, and efficiency equations. Discussion and results point to the existing trade-offs between competing technologies with a specific focus on the balance between gravimetric energy density, volumetric efficiency, thermal stability, and supply chain resilience. The paper then summarises by identifying areas of research such as knowledge gaps, challenges to commercialisation and ongoing research such as artificial intelligence-assisted materials discovery, scalable manufacturing and standardisation driven by policies. The purpose of this work is to provide a full source of information to the researchers, automotive engineers, policy makers, and those stakeholders in the industry interested in the development of next generation EV battery systems.

Hari A. Patel, Geetha Ramasamy · 0 citations
Open access 2024

AI-Driven Digital Twin Framework for Smart Industrial Process Optimization

Industry 4.0 has accelerated intelligent manufacturing through AI, IIoT, cloud-edge computing, and Digital Twin technologies. This study proposes an AI-driven Digital Twin framework that integrates real-time sensing, machine learning, deep learning, and predictive analytics for intelligent process monitoring, predictive maintenance, anomaly detection, energy optimization, and autonomous decision-making. The framework enables continuous synchronization between physical assets and their digital counterparts, supporting closed-loop optimization with low-latency edge computing and cloud-based analytics. Reinforcement learning further improves production efficiency, equipment reliability, and resource utilization while reducing downtime and operational costs. Applicable across multiple industrial sectors, the framework also addresses interoperability, cybersecurity, and data governance challenges, providing a scalable foundation for sustainable and human-centric Industry 5.0 manufacturing systems.

Geetha Ramasamy, Hari A. Patel · 0 citations
Open access 2018

Multi-Path Data Transmission for Enhancing Reliability in Wireless Sensor Networks

It is demonstrated that multi-path data transmission significantly enhances network reliability while ensuring optimal resource utilization, thereby contributing to advancements in the field of wireless communication.

Hari A. Patel, Geetha Ramasamy · 0 citations

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