79 research outputs found

    Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

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    The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

    Data Science in Healthcare

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    Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

    Power Electronics and Energy Management for Battery Storage Systems

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    The deployment of distributed renewable generation and e-mobility systems is creating a demand for improved dynamic performance, flexibility, and resilience in electrical grids. Various energy storages, such as stationary and electric vehicle batteries, together with power electronic interfaces, will play a key role in addressing these requests thanks to their enhanced functionality, fast response times, and configuration flexibility. For the large-scale implementation of this technology, the associated enabling developments are becoming of paramount importance. These include energy management algorithms; optimal sizing and coordinated control strategies of different storage technologies, including e-mobility storage; power electronic converters for interfacing renewables and battery systems, which allow for advanced interactions with the grid; and increase in round-trip efficiencies by means of advanced materials, components, and algorithms. This Special Issue contains the developments that have been published b researchers in the areas of power electronics, energy management and battery storage. A range of potential solutions to the existing barriers is presented, aiming to make the most out of these emerging technologies

    Computational Optimizations for Machine Learning

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    The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

    Development of a Multi-Hour Ahead Wind Power Forecasting System

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    Wind energy, as a renewable and green energy source with substantial value that is vital for sustainable human development, is gaining more and more attention around the world. The variability of wind implies that wind power is random, intermittent, and volatile. In order to overcome the unfavourable factors brought by wind power and enhance the reliable, stable, and secure operation of electrical grids that incorporate wind power systems, a multi-hour ahead wind power forecasting system consisting of an optimal combination of statistical, physical, and artificial intelligence (AI) models for real wind farm applications was proposed in this research. Except for a direct persistence model that was able to produce wind power forecasts directly, an indirect persistence, an autoregressive integrated moving average (ARIMA), and a Weather Research and Forecasting (WRF) model were used to provide wind speed forecasts which, in turn, could be converted to wind power forecasts by using a power curve model. A technique for order of preference by similarity to ideal solution (TOPSIS) scheme was applied to construct a novel 5-in-1 (ensemble) WRF model for wind speed and wind power forecasting. An adaptive neuro-fuzzy inference system (ANFIS) model was employed to determine the power curve model, and another ANFIS model was utilised to build a wind speed correction model exclusively for correcting the wind speed forecasts provided by the 5-in-1 (ensemble) WRF model. By using a set of 24-day historical wind speed and wind power measurements acquired from an operational wind turbine in a real wind farm located in North China, the multi-hour ahead wind power forecasting system was proposed comprising the following components over various forecast time horizons: the direct and indirect persistence models for 30-minute ahead forecasting, the ARIMA model for 1-hour ahead forecasting, and the WRF-TOPSIS model (with corrections obtained from the ANFIS-based wind speed correction model) for 1.5-hour to 24-hour (with a 30-minute temporal resolution) ahead forecasting. The primary contribution of this research is the novel WRF-TOPSIS model strategy used to select and combine the best-performing WRF models from a vast ensemble of possible models. The results demonstrated that the proposed multi-hour ahead wind power forecasting system has excellent predictive performance and is of practical relevance

    Mobile Diagnosis 2.0

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    Mobile sensing and diagnostic capabilities are becoming extremely important for a wide range of emerging applications and fields spanning mobile health, telemedicine, point-of-care diagnostics, global health, field medicine, democratization of sensing and diagnostic tools, environmental monitoring, and citizen science, among many others. The importance of low-cost mobile technologies has been underlined during this current COVID-19 pandemic, particularly for applications such as the detection of pathogens, including bacteria and viruses, as well as for prediction and management of different diseases and disorders. This book focuses on some of these application areas and provides a timely summary of cutting-edge results and emerging technologies in these interdisciplinary fields

    AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

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    In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings

    Artificial Neural Networks in Agriculture

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    Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible
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