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The main hindrances to the large-scale development of renewable-energy projects are the lack of bankability and the inability to align investments and investors with suitable financial instruments or robust policy measures. To illustrate a bankable project, this paper presents a research-based case study on the installation of solar photovoltaic panels on the rooftops of 195 trains of the Indian Railways. Detailed information on the annual running hours, exposure to sunlight, efficiency of solar photovoltaic generation and electrical power demands of each rail coach is considered to conduct a quantitative measure of the tentative amount of fossil fuel savings. The purpose is to provide insight into the types of renewable-energy projects that can be highly attractive to financial institutions and promoters due to their lucrative internal return on investment. As seen in this case study, there are annual savings in diesel of 12 323 088 litres and a CO2 reduction of 32 755 tonnes, with return on investment of 1.3 years. Furthermore, this study conducts a comprehensive analysis of the limitations of existing renewable-energy project financing mechanisms in India. Subsequently, three policy measures are recommended to develop a robust financial mechanism that can effectively meet the needs of investors and investors. These measures include increasing equity injection through a buy-and-hold strategy, providing direct tax benefits to promoters and financing through real-estate investment trusts. The findings are highly relevant to address the challenges associated with bridging the financial gap between access to finance and capital investment in the renewable-energy sector, especially for Asian countries. [ABSTRACT FROM AUTHOR]

The aim of this paper is to demonstrate the role of shading devices in the improvement of energy efficiency of buildings in hot dusty and dry tropical regions. The effect of shading in reducing the energy consumption of buildings is investigated by considering a case study of a guest house chosen because of its logical design approach to reduce thermal loads. The building plan, measurements, and details on schedules of building usage activities have been used as input data to a simulation program of the building. Based on the inputs, a thermal building model is developed in TRNSYS 17 simulation program and the effect of external shading on the building has been explored. It is seen that building design and orientation determine the effectiveness of shading. Movable shading over windows has a significant impact reducing temperatures by about 1.5°C in each thermal zone. The difference in thermal energy loads of the building calculated from modeling simulations of the base case and the control case utilizing movable shading devices is approximately 8%. A programmable logic controllers (PLC)-based movable shading device has been designed to facilitate optimal shading control. The results enable us to draw inferences regarding the additional contribution of the shading factor in energy saving techniques for buildings. [ABSTRACT FROM AUTHOR]

Energy has been universally recognized as one of the most important inputs for economic growth and human development but due to increasing pressure of population and increasing use of energy in different sectors of the economy is an area of concern for India. According to 2011 energy survey, there is a huge gap between energy demand and energy production in India. Since the industrial revolution, fossil fuel has become the primary source of energy to produce electricity but the use of fossil fuel is not eco friendly as it produces greenhouse effect. Besides the environmental effects, all the reserves of primary sources of energy are on their way to getting used up. Among the total electricity produced per year, 35 to 40 percent of primary energy is consumed by the household sectors. So the reduction of energy consumption in buildings is of high socio-economic relevance. Besides the reduction of energy consumption we have solar energy which is far better than fossil fuel and it is the most abundant energy available in the planet. Due to enormous advantages of solar energy, an innovative design of sustainable building is presented in this paper where solar energy is used to make it a zero energy building. Green building also indirectly helps to reduce green house gas emission and pollution produced by the combustion of fossil fuels. This paper also deals with the electricity supply control, lighting control and solar tracking control are designed using Programmable Logic Controller (PLC) which helps to increase the power supply security and the overall efficiency of the building. The automation system has been designed and tested using GE, FANUC PLC. [ABSTRACT FROM AUTHOR]

Estimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while at the same time implementing a complex core temperature estimation strategy in onboard low-cost BMS is also challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. Broadly, there are first-order and second-order heat resistor–capacitor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study. The aim was to determine whether it is worth investing towards developing a second-order thermal model instead of a first-order model with respect to prediction accuracy considering the modeling complexity and experiments required. Both the thermal models along with the parameter estimation scheme were modeled and simulated in a MATLAB/Simulink environment. Models were validated using laboratory test data of a cylindrical 18,650 LIB cell. Further, a Kalman filter with appropriate process and measurement noise levels was used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models were analyzed for comparison purposes. [ABSTRACT FROM AUTHOR]

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, a cell temperature beyond the manufacturer's specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB, such as state of charge, state of health and so on. High-capacity LIB packs, used in electric vehicles and grid-tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to researchers and industries towards developing a highly accurate, intelligent, adaptive, easy-to-implement and computationally efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS. [ABSTRACT FROM AUTHOR]

An effective battery management system (BMS) is indispensable for any lithium-ion battery (LIB) powered systems such as electric vehicles (EVs) and stationary grid-tied energy storage systems. Massive wire harness, scalability issue, physical failure of wiring, and high implementation cost and weight are some of the major issues in conventional wired-BMS. One of the promising solutions researchers have come up with is the wireless BMS (WBMS) architecture. Despite research and development on WBMS getting momentum more than a decade ago, it is still in a preliminary stage. Significant further upgradation is required towards developing an industry-ready WBMS, especially for high-power LIB packs. Therefore, an in-depth survey exclusively on WBMS architectures is presented in this article. The aim is to provide a summary of the existing developments as well as to present an informative guide to the research community for future developments by highlighting the issues, emerging trends, and challenges. In-depth analysis of the existing WBMS topologies will not only help the researchers to understand the existing challenges and future research scopes clearly but at the same time enthuse them to focus their research inclination in the domain of WBMS. [ABSTRACT FROM AUTHOR]

Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance. [ABSTRACT FROM AUTHOR]

Cancer nanoparticles are swiftly progressing and are being applied to solve several limitation of conventional drug delivery system such as non-specific biodistribution and targeting, lack of water solubility and poor oral bioavailability. Advances in protein engineering and materials science have contributed to novel nanoscale targeting approaches that may bring new hope to cancer patients. Several therapeutic nanocarries have been approved for clinical use. Nanoparticles have been designed for optimal size and surface characteristic to improve their biodistribution and to increase their circulation time in the bloodstream. By selectively using the unique pathophysiology of tumours, such as their enhanced permeability and retention effect nanotherapeutics arew able to carry loaded active drug to cancer cells. In addition to this passive targeting mechanism, active targeting stratigies using ligards or antibodies directed against selected tumour targets magnify the specific of these therapeutic nanoparticles. Multi functional nanoparticles are now being actively investigated and are on the horizon as the nexty generation of nanoparticles and tailored cancer treatment. [ABSTRACT FROM AUTHOR]