Md. Muzakkir Hussain

Public Opinion on Viability of x EVs in India

This work demonstrates a smart charging system of electric vehicle using information technology and cloud computing. xEVs (electric plugin hybrid, battery electric vehicles) charging management system will be very helpful for the varying charging infrastructure demands, namely perspectives from automakers, electricity providers, vehicle owners, and charging service providers. Through dedicated interface, the developed system will provide real-time information to xEV users regarding nearest charging station with minimum queuing delay and with minimum charging cost through a secured online accessing mechanism for accessing state of charge (SOC) of the xEV’s battery being charged. The system not only provide an execution framework for the xEV users but also provide an optimal energy trading solution to all entities involved in a smart charging infrastructure such as charging station, aggregators, smart grid. The work also explains the cloud-enabled bidding strategies that look for day-ahead and term-ahead markets. The aggregators will use the smart decisions undertaken by cloud analytics to execute their bidding strategies in way to maximize the profit. Further, the work also assesses the possible cybersecurity aspects of such architectures along with providing possible solutions.

A Risk Averse Business Model for Smart Charging of Electric Vehicles

Smart collaborations among the smart grid, electric vehicles, and aggregators will provide range of benefits to stakeholders involved in an intelligent transportation system (ITS). The EVs, nowadays, are becoming the epicenter of smart power system research towards the electrification of transport. However, massive penetration of EVs will pose management threats to the supporting smart grid in the foreseeable future. This work proposes a risk averse optimization framework for smart charging management of electric vehicles. Adopting conditional value at risk (CVaR) for estimating the risks, the work attempts to propose an optimized bidding strategy for the smart charging stations (SCS) that act on behalf of aggregators for managing the financial risk caused by the uncertainties. Finally, a fuzzified translation model is discussed along with notable methodologies as a solution strategy to the risk averse cost optimization problem.

A FOG Computing Based Battery Swapping Model for Next Generation Transport

It has been a consensus persuasion from automotive industries, policymakers, R&Ds and vehicle vendors that electric vehicle is the powertrain archetype for future transport. The current Electric, plug in electric and plug in hybrid electric vehicles (xEVs) no longer remain only a means of commute, but can act as prime actors to have active business participation with various markets in the power system such as V2G, demand side management (DSM) etc. The modern development in the information and communication technology (ICT) evolves such vehicle into intelligent vehicle (IV) and augments their utility to provide diverse services for Intelligent Transportation (ITS) infrastructure. However, due to lack of viable charging infrastructures the contemporary power system fails to accommodate the incoming xEV flux. The inability is manifested in the form poor quality of service, which causes customer dissatisfaction and ultimately lower adoption of xEVs. This work proposes an energy efficient battery swapping topology (BSS) adopting the notion of Internet of Things (IoT). The work introduced the innovative notion of integrating internet of things (IoT) into smart charging infrastructures and proposed a data driven IoT-BSS model whose operation is regulated through Fog computing and Big Data analytics. Further, a four layer fog computing execution stack is developed to set up the service oriented architecture (SOA) for an efficient and real-time decision making framework for next generation intelligent transportation. The work also highlights the data science prospects and challenges that can elucidate in course of realization the proposed infrastructure.

Feasibility of Fog Computing in Smart Grid Architectures

Contemporary Smart Grid (SG) systems are enticed by smart devices and entities due to unfolded developments in both the IT sectors viz. Intelligent Transportation and Information Technology. The intelligent transportation infrastructure elements when bestowed with Internet of Things (IoT) and sensor network of latter IT (Information Technology), makes every object active and brings them online. In such scenario, the traditional cloud deployment perishes to meet the analytics and computational exigencies for such dynamic cum resource-time critical subsystems. Starting with highlighting the mission-critical requirements of an idealized SG infrastructure, this work proposes an edge-centered FOG (From cOre to edGe) computing model primarily focused to realize the processing and computational objectives of SG. The objective of this work is to comprehend the applicability of FOG computing algorithms to interplay with the core-centered cloud computing support, thus enabling to come up with a new breed of real-time and latency free utilities. Further, for demonstrating the feasibility of the proposed framework, the SG use case is considered and an exemplary FOG Service-Oriented Architecture (SOA) is depicted. Finally, the potential adoption challenges elucidated in the realization of the proposed framework are highlighted along with nascent research domains that call for efforts and investments in successfully guiding the FOG approaches into a pinnacle.