Hybrid fuel cell-supercapacitor system: modeling and energy management using Proteus

The increasing adoption of electric vehicles (EVs) presents a promising solution for achieving sustainable transportation and reducing carbon emissions. To keep pace with technological advancements in the vehicular industry, this paper proposes the development of a hybrid energy storage system (HESS) and an energy management strategy (EMS) for EVs, implemented using Proteus Spice Ver 8. The HESS consists of a proton exchange membrane fuel cell (PEMFC) as the primary source and a supercapacitor (SC) as the secondary source. The EMS, integrated into an electronic board based on the STM32, utilizes a low-pass filter algorithm to distribute energy between the sources. The accuracy of the proposed PEMFC and SC models is validated by comparing Proteus simulation results with experimental tests conducted on the Bahia didactic bench and Maxwell SC bench, respectively. To optimize energy efficiency, simulations of the HESS system involve adjusting the hybridization rate through changes in the cutoff frequency. The analysis compares the state-of-charge (SOC) of the SC and the voltage efficiency of the fuel cell (FC), across different frequencies to optimize overall system performance. The results highlight that the chosen strategy satisfies the energy demand while preserving the FC’s dynamic performance and optimizing its utilization to the maximum

FINCH: A Blueprint for Accessible and Scientifically Valuable Remote Sensing Satellite Missions

Satellite remote sensing missions have grown in popularity over the past fifteen years due to their ability to cover large swaths of land at regular time intervals, making them suitable for monitoring environmental trends such as greenhouse gas emissions and agricultural practices. As environmental monitoring becomes central in global efforts to combat climate change, accessible platforms for contributing to this research are critical. Many remote sensing missions demand high performance of payloads, restricting research and development to organizations with sufficient resources to address these challenges. Atmospheric remote sensing missions, for example, require extremely high spatial and spectral resolutions to generate scientifically useful results. As an undergraduate-led design team, the University of Toronto Aerospace Team’s Space Systems Division has performed an extensive mission selection process to find a feasible and impactful mission focusing on crop residue mapping. This mission profile provides the data needed to improve crop residue retention practices and reduce greenhouse gas emissions from soil, while relaxing performance requirements relative to many active atmospheric sensing missions. This is accompanied by the design of FINCH, a 3U CubeSat with a hyperspectral camera composed of custom and commercial off-the-shelf components. The team’s custom composite payload, the FINCH Eye, strives to advance performance achieved at this form factor by leveraging novel technologies while keeping design feasibility for a student team a priority. Optical and mechanical design decisions and performance are detailed, as well as assembly, integration, and testing considerations. Beyond its design, the FINCH Eye is examined from operational, timeline, and financial perspectives, and a discussion of the supporting firmware, data processing, and attitude control systems is included. Insight is provided into open-source tools that the team has developed to aid in the design process, including a linear error analysis tool for assessing scientific performance, an optical system tradeoff analysis tool, and data processing algorithms. Ultimately, the team presents a comprehensive case study of an accessible and impactful satellite optical payload design process, in hopes of serving as a blueprint for future design teams seeking to contribute to remote sensing research

A non-linear autoregressive model for indoor air-temperature predictions in smart buildings

In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40% of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short-and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models

Design and experiment of Panax notoginseng root orientation transplanting device based on YOLOv5s

Consistent root orientation is one of the important requirements of Panax notoginseng transplanting agronomy. In this paper, a Panax notoginseng orientation transplanting method based on machine vision technology and negative pressure adsorption principle was proposed. With the cut-main root of Panax notoginseng roots as the detection object, the YOLOv5s was used to establish a root feature detection model. A Panax notoginseng root orientation transplanting device was designed. The orientation control system identifies the root posture according to the detection results and controls the orientation actuator to adjust the root posture. The detection results show that the precision rate of the model was 94.2%, the recall rate was 92.0%, and the average detection precision was 94.9%. The Box-Behnken experiments were performed to investigate the effects of suction plate rotation speed, servo rotation speed and the angle between the camera and the orientation actuator(ACOA) on the orientation qualification rate and root drop rate. Response surface method and objective optimisation algorithm were used to analyse the experimental results. The optimal working parameters were suction plate rotation speed of 5.73 r/min, servo rotation speed of 0.86 r/s and ACOA of 35°. Under this condition, the orientation qualification rate and root drop rate of the actual experiment were 89.87% and 6.57%, respectively, which met the requirements of orientation transplanting for Panax notoginseng roots. The research method of this paper is helpful to solve the problem of orientation transplanting of other root crops

A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings

In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40% of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short- and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models

A novel resource-constrained insect monitoring system based on machine vision with edge AI

Effective insect pest monitoring is a vital component of Integrated Pest Management (IPM) strategies. It helps to support crop productivity while minimising the need for plant protection products. In recent years, many researchers have considered the integration of intelligence into such systems in the context of the Smart Agriculture research agenda. This paper describes the development of a smart pest monitoring system, developed in accordance with specific requirements associated with the agricultural sector. The proposed system is a low-cost smart insect trap, for use in orchards, that detects specific insect species that are detrimental to fruit quality. The system helps to identify the invasive insect, Brown Marmorated Stink Bug (BMSB) or Halyomorpha halys (HH) using a Microcontroller Unit-based edge device comprising of an Internet of Things enabled, resource-constrained image acquisition and processing system. It is used to execute our proposed lightweight image analysis algorithm and Convolutional Neural Network (CNN) model for insect detection and classification, respectively. The prototype device is currently deployed in an orchard in Italy. The preliminary experimental results show over 70 percent of accuracy in BMSB classification on our custom-built dataset, demonstrating the proposed system feasibility and effectiveness in monitoring this invasive insect species

A low-cost plant monitoring system and smart irrigation

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Electrical and Electronic Engineering, May 2022During the dry season, there is little or no rainfall, and plant life suffers as a result. Water is available during the rainy season, thus plants will thrive but may perish if swamped. There must be a system in place that can harvest the limited water available then use it as needed. Ghanaian farmers in rural areas want a system that delivers real-time data on their crops. Safer, faster, and more effective means of such operations have emerged. Most traditional activities have evolved as a result of the internet's penetration. An Internet of Things (IoT) system could help enhance the effectiveness of a standard surface watering system. The system is equipped with sensors that can detect changes in temperature, pH, soil moisture, and humidity, among other things, and can send this information to a smart device via the Internet. Ghana needs to adopt new technologies in order to compete with the rest of the world. Plant monitoring is an important aspect of agriculture and horticulture. The majority of people are fixated on seeing the farm physically and performing the majority of farm tasks manually, which affects efficiency and productivity. In this project, the development of a plant monitoring system and smart irrigation operated using sensors and a web-based platform is explored for the monitoring of water consumption, with the help of Internet of Things (IoT).Ashesi Universit