Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

Raising awareness for water polution based on game activities using internet of things

Awareness among young people regarding the environment and its resources and comprehension of the various factors that interplay, is key to changing human behaviour towards achieving a sustainable planet. In this paper IoT equipment, utilizing sensors for measuring various parameters of water quality, is used in an educational context targeting at a deeper understanding of the use of natural resources towards the adoption of environmentally friendly behaviours. We here note that the use of water sensors in STEM gameful learning is an area which has not received a lot of attention in the previous years. The IoT water sensing and related scenaria and practices, addressing children via discovery, gamification, and educational activities, are discussed in detail

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

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

Analysis and optimisation through mathematical modelling: Muresk farm photovoltaic reverse osmosis water treatment plant

Photovoltaic reverse osmosis water treatment units can be deployed into remote regions to provide remote communities with a clean water source without the need for on site electricity supply to operate. Optimisation of these units has the potential to maximise the output of purified water and to improve the overall effectiveness of the PVRO unit once it has been deployed. The aim of this project is to develop a mathematical model for the optimisation of the Muresk PVRO unit. This is achieved using a local monitoring system that can log the operational data of the PVRO unit and utilising this data to validate and tune a Microsoft Excel based mathematical model of the Muresk PVRO unit. In this project an ESP32 microcontroller running an Arduino program was used to log the electrical and water flow data from the PVRO unit to a ThingSpeak IOT portal and a local SD card. A mathematical model of the Muresk PVRO system was developed, and two months of data were compared with the data from the monitoring unit to tune and validate the model. With the model tuned the mathematical model was used to investigate optimising the PVRO output by adjusting the tilt angle of the solar array. By increasing the array tilt from 30 degrees to 45-degrees the daily minimum output improved by 9% with a marginal loss of 1% to the annual water output. This increases the suitability of the unit to applications where a consistent output of clean water is more desired than just maximising the annual output

Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems

Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion

Reconfigurable Battery Techniques and Systems: A Survey

Battery packs with a large number of battery cells are becoming more and more widely adopted in electronic systems, such as robotics, renewable energy systems, energy storage in smart grids, and electronic vehicles. Therefore, a well-designed battery pack is essential for battery applications. In the literature, the majority of research in battery pack design focuses on battery management system, safety circuit, and cell-balancing strategies. Recently, the reconfigurable battery pack design has gained increasing attentions as a promising solution to solve the problems existing in the conventional battery packs and associated battery management systems, such as low energy efficiency, short pack lifespan, safety issues, and low reliability. One of the most prominent features of reconfigurable battery packs is that the battery cell topology can be dynamically reconfigured in the real-time fashion based on the current condition (in terms of the state of charge and the state of health) of battery cells. So far, there are several reconfigurable battery schemes having been proposed and validated in the literature, all sharing the advantage of cell topology reconfiguration that ensures balanced cell states during charging and discharging, meanwhile providing strong fault tolerance ability. This survey is undertaken with the intent of identifying the state-of-the-art technologies of reconfigurable battery as well as providing review on related technologies and insight on future research in this emerging area

MPPT battery charge controller with lorawan communication interface

Mestrado de dupla diplomação com Tunisia Private University (ULT Tunisie)Renewable sources, such as Photovoltaic Systems (PV), have been employed for decades to focus on cleaner types of electricity generation. Today, it is a subject of worry as to how to cut costs and enhance efficiency in order to harness and utilise these natural resources in the best way possible. As a result, the concept of Maximum Power Point Tracking Technique (MPPT) evolved, which is essentially a system used by charge controllers for wind turbines and Photovoltaic Systems to use and also give a maximised power output. This thesis is primarily focused with the application of such a system in order to achieve controlled photovoltaic power using the MPPT mechanism. The main goal is to add LoRaWAN capability such as to be able to transmit information regarding the solar charge battery controller internal state A micro-controller, which is part of a larger circuit, such as a solar charge controller, is required for MPPT hardware implementation. The heart of the hardware circuit is the solar charge controller. Furthermore, the system was integrated with a dashboard to provide easier access to data for analysis from anywhere, eliminating the physical work of data collecting.Fontes de energia renovável, como é o caso dos sistemas fotovoltaicos, têm sido vindo a ser cada vez mais utilizadas como alternativas menos impactantes do ponto de vista ambiental. Atualmente, é motivo de preocupação o recurso a métodos e tecnologias que permitam aproveitar os recursos naturais sustentáveis e, ao mesmo tempo, reduzir os custos e aumentar a sua eficiência. Neste contexto, é importante o recurso a técnicas de seguimento de ponto de potência máximo (MPPT), usado por controladores de carga para turbinas eólicas e sistemas fotovoltaicos, de modo a extrair a máxima potência do sistema elétrico de produção de energia. Esta tese assenta no desenvolvimento de um sistema fotovoltaico capaz de regular o processo de carga para baterias de gel, dotada de mecanismo MPPT integrado e com a capacidade de transmitir toda a telemetria associada à operação do regulador usando o protocolo LoRaWAN. Este sistema de controlo de carga é baseado num microcontrolador que implementa o algoritmo MQTT e os dados enviados, via LoRaWAN, são apresentados numa interface gráfica

IR Based Auto-Recharging System for Autonomous Mobile Robot

As autonomous mobile robots are progressively utilized for appropriated missions, a significant issue that should be tackled is the autonomous recharging problem. The robots can be recharged by planning and arranging effectively to maximize its working efficiency. This paper presents the implementation of automatic docking robot with docking strategy and recharging capabilities. The robot is programmed using an algorithm which will guide the robot to move around in a square path of 30 inch by 30 inch continuously. While the robot is performing its assigned task, the battery remaining voltage is monitored by voltage detection module. When the battery voltage reaches threshold value of less than 12V, the microcontroller commands the robot to go back to the docking station for recharging autonomously. This system uses IR receiver sensor in front of the robot and IR transmitter sensor near docking station. The active IR transmitter sensor which transmit infrared signal located near docking area serves as landmark in guiding robot towards docking area. The robot scans the transmitted IR signal from the sensor transmitter only when it needs to charge its battery, if detected it will take the path of charging station. Once the robot approaches the charging station with the required orientation, it connects to the supply terminals for charging. The data related to battery charging voltage is transmitted by microcontroller through Bluetooth HC-05 to PLX DAQ software tool in PC stores it in the Excel sheet as the data arrive. Once the battery is fully charged the robot moves back to continue its original task

Scenarios for Educational and Game Activities using Internet of Things Data

Raising awareness among young people and changing their behavior and habits concerning energy usage and the environment is key to achieving a sustainable planet. The goal to address the global climate problem requires informing the population on their roles in mitigation actions and adaptation of sustainable behaviors. Addressing climate change and achieve ambitious energy and climate targets requires a change in citizen behavior and consumption practices. IoT sensing and related scenario and practices, which address school children via discovery, gamification, and educational activities, are examined in this paper. Use of seawater sensors in STEM education, that has not previously been addressed, is included in these educational scenaria